# Identification of the Top TESS Objects of Interest for Atmospheric
Characterization of Transiting Exoplanets with JWST

Benjamin J. Hord Department of Astronomy, University of Maryland, College Park, MD 20742, USA  NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA  GSFC Sellers Exoplanet Environments Collaboration  Eliza M.-R. Kempton Department of Astronomy, University of Maryland, College Park, MD 20742, USA  Thomas Mikal-Evans Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg, Germany  David W. Latham Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  David R. Ciardi NASA Exoplanet Science Institute, IPAC, California Institute of Technology, Pasadena, CA 91125 USA  Diana Dragomir Department of Physics and Astronomy, University of New Mexico, 210 Yale Boulevard NE, Albuquerque, NM 87106, USA  Knicole D. Colón NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA  GSFC Sellers Exoplanet Environments Collaboration  Gabrielle Ross  Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Andrew Vanderburg Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Zoe L. de Beurs NSF Graduate Research Fellow and MIT Presidential Fellow  Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Karen A. Collins Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Cristilyn N. Watkins Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Jacob Bean Department of Astronomy $\&$ Astrophysics, University of Chicago, 5640 S Ellis Ave, Chicago, IL 60637, USA  Nicolas B. Cowan Department of Earth and Planetary Sciences and Department of Physics, McGill University, 3600 rue University, Montréal, QC, H3A 2T8, Canada  Tansu Daylan Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130, USA  Caroline V. Morley The University of Texas at Austin, Department of Astronomy, 2515 Speedway, Stop C1400, Austin, Texas 78712-1205  Jegug Ih Department of Astronomy, University of Maryland, College Park, MD 20742, USA  David Baker Physics Department, Austin College, Sherman, TX 75090, USA  Khalid Barkaoui Astrobiology Research Unit, Université de Liège, 19C Allée du 6 Août, 4000 Liège, Belgium  Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Instituto de Astrofísica de Canarias (IAC), Calle Vía Láctea s/n, 38200, La Laguna, Tenerife, Spain  Natalie M. Batalha Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95060, USA  Aida Behmard NSF Graduate Research Fellow  Division of Geological and Planetary Science, California Institute of Technology, Pasadena, CA 91125, USA  Alexander Belinski Sternberg Astronomical Institute, Lomonosov Moscow State University, Universitetskii prospekt, 13, Moscow 119992, Russia  Zouhair Benkhaldoun Oukaimeden Observatory, High Energy Physics and Astrophysics Laboratory, Faculty of sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco  Paul Benni Acton Sky Portal (private observatory), Acton, MA USA  Krzysztof Bernacki Silesian University of Technology, Department of Electronics, Electrical Engineering and Microelectronics, Akademicka 16, 44-100 Gliwice, Poland  Allyson Bieryla Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Avraham Binnenfeld Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv 6997801, Israel  Pau Bosch-Cabot  Observatori Astronòmic Albanyà, Camí de Bassegoda S/N, Albanyà 17733, Girona, Spain  François Bouchy Observatoire de Genève, Département d’Astronomie, Université de Genève, Chemin Pegasi 51b, 1290 Versoix, Switzerland  Valerio Bozza Dipartimento di Fisica “E.R. Caianiello”, Università di Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy  Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, Via Cintia, 80126 Napoli, Italy  Rafael Brahm Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal las Torres 2640, Peñalolén, Santiago, Chile  Millennium Institute for Astrophysics, Macul, Santiago, Chile  Lars A. Buchhave DTU Space, Technical University of Denmark, Elektrovej 328, DK-2800 Kgs. Lyngby, Denmark  Michael Calkins  Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Whipple Observatory  Ashley Chontos NSF Graduate Research Fellow  Institute for Astronomy, University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA  Catherine A. Clark Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA  NASA Exoplanet Science Institute, IPAC, California Institute of Technology, Pasadena, CA 91125 USA  Ryan Cloutier Department of Physics & Astronomy, McMaster University, 1280 Main St W, Hamilton, ON, L8S 4L8, Canada  Marion Cointepas  Univ. Grenoble Alpes, CNRS, IPAG, F-38000 Grenoble, France  Observatoire de Genève, Département d’Astronomie, Université de Genève, Chemin Pegasi 51b, 1290 Versoix, Switzerland  Kevin I. Collins George Mason University, 4400 University Drive, Fairfax, VA, 22030 USA  Dennis M. Conti American Association of Variable Star Observers, 185 Alewife Brook Parkway, Suite 410, Cambridge, MA 02138, USA  Ian J. M. Crossfield Kansas University Dept. of Physics and Astronomy 1082 Malott, 1251 Wescoe Hall Dr. Lawrence, KS 66045 USA  Fei Dai Division of Geological and Planetary Sciences 1200 E California Blvd, Pasadena, CA, 91125, USA  Jerome P. de Leon Department of Multi-Disciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan  Georgina Dransfield School of Physics & Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom  Courtney Dressing Department of Astronomy, University of California Berkeley, Berkeley, CA 94720, USA  Adam Dustor Silesian University of Technology, Department of Telecommunications and Teleinformatics, Akademicka 16, 44-100 Gliwice, Poland  Gilbert Esquerdo Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Phil Evans Phil Evans, El Sauce Observatory, Coquimbo Province, Chile  Sergio B. Fajardo-Acosta Caltech/IPAC, Mail Code 100-22, Pasadena, CA 91125, USA  Jerzy Fiołka Silesian University of Technology, Department of Electronics, Electrical Engineering and Microelectronics, Akademicka 16, 44-100 Gliwice, Poland  Raquel Forés-Toribio Departamento de Astronomía y Astrofísica, Universidad de Valencia, E-46100 Burjassot, Valencia, Spain  Observatorio Astronómico, Universidad de Valencia, E-46980 Paterna, Valencia, Spain  Antonio Frasca INAF – Osservatorio Astrofisico di Catania, Via S.Sofia 78, I-95123, Catania, Italy  Akihiko Fukui Komaba Institute for Science, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan  Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, Tenerife, Spain  Benjamin Fulton NASA Exoplanet Science Institute/Caltech-IPAC, MC 314-6, 1200 E California Blvd, Pasadena, CA 91125, USA  Elise Furlan NASA Exoplanet Science Institute, Caltech/IPAC, Pasadena, CA 91125, USA  Tianjun Gan Department of Astronomy, Tsinghua University, Beijing 100084, China  Davide Gandolfi Dipartimento di Fisica, Universita degli Studi di Torino, via Pietro Giuria 1, I-10125 Torino, Italy  Mourad Ghachoui  Astrobiology Research Unit, Université de Liège, Allée du 6 Août 19C, B-4000 Liège, Belgium  Oukaimeden Observatory, High Energy Physics and Astrophysics Laboratory, Cadi Ayyad University, Marrakech, Morocco  Steven Giacalone Department of Astronomy, University of California Berkeley, Berkeley, CA 94720, USA  Emily A. Gilbert Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA  Michaël Gillon Astrobiology Research Unit, Université de Liège, 19C Allée du 6 Août, 4000 Liège, Belgium  Eric Girardin Grand Pra Observatory, 1984 Les Haudères, Switzerland  Erica Gonzales Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064, USA  Ferran Grau Horta Observatori de Ca l’Ou, Carrer de dalt 18, Sant Martí Sesgueioles 08282, Barcelona, Spain  Joao Gregorio Crow Observatory, Portalegre, Portugal  Michael Greklek-McKeon Division of Geological and Planetary Sciences, California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, USA  Pere Guerra Observatori Astronòmic Albanyà, Camí de Bassegoda S/N, Albanyà 17733, Girona, Spain  J. D. Hartman Department of Astrophysical Sciences, Princeton University, NJ 08544, USA  Coel Hellier  Astrophysics Group, Keele University, Staffordshire, ST5 5BG, UK  Krzysztof G. Hełminiak Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, ul. Rabiańska 8, 87-100 Toruń, Poland  Thomas Henning Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg, Germany  Michelle L. Hill Department of Earth and Planetary Sciences, University of California, Riverside, CA 92521, USA  Keith Horne SUPA Physics and Astronomy, University of St. Andrews, Fife, KY16 9SS Scotland, UK  Andrew W. Howard Department of Astronomy, California Institute of Technology, Pasadena, CA 91125, USA  Steve B. Howell NASA Ames Research Center, Moffett Field, CA 94035, USA  Daniel Huber Institute for Astronomy, University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA  Howard Isaacson Department of Astronomy, University of California Berkeley, Berkeley CA 94720, USA  Centre for Astrophysics, University of Southern Queensland, Toowoomba, QLD, Australia  Giovanni Isopi  Campo Catino Astronomical Observatory, Regione Lazio, Guarcino (FR), 03010 Italy  Emmanuel Jehin  Space Sciences, Technologies and Astrophysics Research (STAR) Institute, Université de Liège, Allée du 6 Août 19C, B-4000 Liège, Belgium  Jon M. Jenkins NASA Ames Research Center, Moffett Field, CA 94035, USA  Eric L. N. Jensen Dept. of Physics & Astronomy, Swarthmore College, Swarthmore PA 19081, USA  Marshall C. Johnson Department of Astronomy, The Ohio State University, 4055 McPherson Laboratory, 140 West 18th Ave., Columbus, OH 43210 USA  Andrés Jordán Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal las Torres 2640, Peñalolén, Santiago, Chile  Millennium Institute for Astrophysics, Macul, Santiago, Chile  Data Observatory Foundation, Providencia, Santiago, Chile  Stephen R. Kane Department of Earth and Planetary Sciences, University of California, Riverside, CA 92521, USA  John F. Kielkopf Department of Physics and Astronomy, University of Louisville, Louisville, KY 40292, USA  Vadim Krushinsky Kourovka observatory, Ural Federal University, 19 Mira street, Yekaterinburg, Russia  Sławomir Lasota Silesian University of Technology, Department of Electronics, Electrical Engineering and Microelectronics, Akademicka 16, 44-100 Gliwice, Poland  Elena Lee Dept. of Physics & Astronomy, Swarthmore College, Swarthmore PA 19081, USA  Pablo Lewin The Maury Lewin Astronomical Observatory, Glendora,California.91741. USA  John H. Livingston Astrobiology Center, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Astronomical Science Program, Graduate University for Advanced Studies, SOKENDAI, 2-21-1, Osawa, Mitaka, Tokyo, 181-8588, Japan  Jack Lubin Department of Physics & Astronomy, University of California Irvine, Irvine, CA 92697, USA  Michael B. Lund NASA Exoplanet Science Institute – Caltech/IPAC, Pasadena, CA 91125 USA  Franco Mallia  Campo Catino Astronomical Observatory, Regione Lazio, Guarcino (FR), 03010 Italy  Christopher R. Mann Département de physique, Université de Montréal, 1375 Avenue Thérèse-Lavoie-Roux, Montréal, Québec, H3T 1J4, Canada  Trottier Institute for Research on Exoplanets (_iREx_)  Giuseppi Marino Wild Boar Remote Observatory, San Casciano in val di Pesa, Firenze, Italy  Gruppo Astrofili Catanesi, Catania, Italy  Nataliia Maslennikova Sternberg Astronomical Institute, Lomonosov Moscow State University, Universitetskii prospekt , 13, Moscow 119992, Russia  Faculty of Physics, Moscow State University, 1 bldg. 2, Leninskie Gory, Moscow 119991, Russia  Bob Massey Villa ’39 Observatory, Landers, CA 92285, USA  Rachel Matson U.S. Naval Observatory, Washington, D.C. 20392, USA  Elisabeth Matthews Max Planck Institute for Astronomy, Königstuhl 17, D-69117 Heidelberg, Germany  Andrew W. Mayo Astronomy Department, 501 Campbell Hall 3411, University of California, Berkeley, CA 94720, USA  Centre for Star and Planet Formation, Natural History Museum of Denmark & Niels Bohr Institute, University of Copenhagen, Øster Voldgade 5-7, DK-1350 Copenhagen K., Denmark  Tsevi Mazeh  School of Physics and Astronomy, Tel Aviv University, Tel Aviv, 6997801, Israel  Kim K. McLeod Department of Astronomy, Wellesley College, Wellesley, MA 02481, USA  Edward J. Michaels  Waffelow Creek Observatory, 10780 FM 1878, Nacogdoches, TX 75961, USA  Teo Močnik Gemini Observatory/NSF’s NOIRLab, 670 N. A‘ohoku Place, Hilo, HI 96720, USA  Mayuko Mori Department of Multi-Disciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan  Georgia Mraz  Department of Physics and Astronomy, Union College, 807 Union St., Schenectady, NY 12308, USA  Jose A. Muñoz Departamento de Astronomía y Astrofísica, Universidad de Valencia, E-46100 Burjassot, Valencia, Spain  Observatorio Astronómico, Universidad de Valencia, E-46980 Paterna, Valencia, Spain  Norio Narita Komaba Institute for Science, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan  Astrobiology Center, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, Tenerife, Spain  Louise Dyregaard Nielsen European Southern Observatory, Karl-Schwarzschild-Straße 2, D-85748 Garching bei München, Germany  Observatoire de Genève, Département d’Astronomie, Université de Genève, Chemin Pegasi 51b, 1290 Versoix, Switzerland  Hugh Osborn Physikalisches Institut, University of Bern, Gesellsschaftstrasse 6, 3012 Bern, Switzerland  Enric Palle  Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, Tenerife, Spain  Departamento de Astrofísica, Universidad de La Laguna (ULL), 38206, La Laguna, Tenerife, Spain  Aviad Panahi School of Physics and Astronomy, Tel Aviv University, Tel Aviv, 6997801, Israel  Riccardo Papini  Wild Boar Remote Observatory, San Casciano in val di Pesa, Firenze, 50026 Italy  American Association of Variable Star Observers, 49 Bay State Road, Cambridge, MA 02138, USA  Alex S. Polanski Department of Physics and Astronomy, University of Kansas, 1251 Wescoe Hall Drive, Lawrence, KS 66045, USA  Visiting Graduate Student Fellow, NASA Exoplanet Science Institute – Caltech/IPAC, 1200 E. California Blvd, Pasadena, CA 91125 USA  Adam Popowicz Silesian University of Technology, Department of Electronics, Electrical Engineering and Microelectronics, Akademicka 16, 44-100 Gliwice, Poland  Francisco J. Pozuelos Instituto de Astrofísica de Andalucía (IAA-CSIC), Glorieta de la Astronomía s/n, 18008 Granada, Spain  Samuel N. Quinn Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Don J. Radford Brierfield Observatory, Bowral, NSW, Australia  Phillip A. Reed Department of Physical Sciences, Kutztown University, Kutztown, PA 19530, USA  Howard M. Relles Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Malena Rice Department of Astronomy, Yale University, New Haven, CT 06511, USA  Paul Robertson Department of Physics & Astronomy, University of California Irvine, Irvine, CA 92697, USA  Joseph E. Rodriguez Center for Data Intensive and Time Domain Astronomy, Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA  Lee J. Rosenthal Department of Astronomy, California Institute of Technology, Pasadena, CA 91125, USA  Ryan A. Rubenzahl NSF Graduate Research Fellow  Department of Astronomy, California Institute of Technology, Pasadena, CA 91125, USA  Nicole Schanche NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA  Department of Astronomy, University of Maryland, College Park, MD 20742, USA  Joshua Schlieder NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA  Richard P. Schwarz Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Ramotholo Sefako South African Astronomical Observatory, P.O. Box 9, Observatory, Cape Town 7935, South Africa  Avi Shporer Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Alessandro Sozzetti INAF – Osservatorio Astrofisico di Torino, via Osservatorio 20, I10025, Pino Torinese, Italy  Gregor Srdoc  Kotizarovci Observatory, Sarsoni 90, 51216 Viskovo, Croatia  Chris Stockdale Hazelwood Observatory, Australia  Alexander Tarasenkov Faculty of Physics, Moscow State University, 1 bldg. 2, Leninskie Gory, Moscow 119991, Russia  Thiam-Guan Tan Perth Exoplanet Survey Telescope, Perth, Western Australia  Curtin Institute of Radio Astronomy, Curtin University, Bentley, Western Australia 6102  Mathilde Timmermans  Astrobiology Research Unit, Université de Liège, 19C Allée du 6 Août, 4000 Liège, Belgium  Eric B. Ting NASA Ames Research Center, Moffett Field, CA 94035 USA  Judah Van Zandt Department of Physics & Astronomy, University of California Los Angeles, Los Angeles, CA 90095, USA  JP Vignes  American Association of Variable Star Observers, 49 Bay State Road, Cambridge, MA 02138, USA  Ian Waite Centre for Astrophysics, University of Southern Queensland, West Street, Toowoomba, QLD 4350 Australia  Noriharu Watanabe Department of Multi-Disciplinary Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro, Tokyo 153-8902, Japan  Lauren M. Weiss Department of Physics and Astronomy, University of Notre Dame, Notre Dame, IN 46556, USA  Justin Wittrock George Mason University, 4400 University Drive, Fairfax, VA, 22030 USA  George Zhou Centre for Astrophysics, University of Southern Queensland, West Street, Toowoomba, QLD 4350 Australia  Carl Ziegler Department of Physics, Engineering and Astronomy, Stephen F. Austin State University, 1936 North St, Nacogdoches, TX 75962, USA  Shay Zucker Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv 6997801, Israel Benjamin J. Hord<EMAIL_ADDRESS>
###### Abstract

JWST has ushered in an era of unprecedented ability to characterize
exoplanetary atmospheres. While there are over 5,000 confirmed planets, more
than 4,000 TESS planet candidates are still unconfirmed and many of the best
planets for atmospheric characterization may remain to be identified. We
present a sample of TESS planets and planet candidates that we identify as
“best-in-class” for transmission and emission spectroscopy with JWST. These
targets are sorted into bins across equilibrium temperature $T_{\mathrm{eq}}$
and planetary radius $R{{}_{\mathrm{p}}}$ and are ranked by transmission and
emission spectroscopy metric (TSM and ESM, respectively) within each bin. In
forming our target sample, we perform cuts for expected signal size and
stellar brightness, to remove sub-optimal targets for JWST. Of the 194 targets
in the resulting sample, 103 are unconfirmed TESS planet candidates, also
known as TESS Objects of Interest (TOIs). We perform vetting and statistical
validation analyses on these 103 targets to determine which are likely planets
and which are likely false positives, incorporating ground-based follow-up
from the TESS Follow-up Observation Program (TFOP) to aid the vetting and
validation process. We statistically validate 23 TOIs, marginally validate 33
TOIs to varying levels of confidence, deem 29 TOIs likely false positives, and
leave the dispositions for 4 TOIs as inconclusive. 14 of the 103 TOIs were
confirmed independently over the course of our analysis. We provide our final
best-in-class sample as a community resource for future JWST proposals and
observations. We intend for this work to motivate formal confirmation and mass
measurements of each validated planet and encourage more detailed analysis of
individual targets by the community.

††facilities: Adams Observatory, ASP, Brierfield Private Observatory, Campo
Catino Astronomical Observatory, Catania Astrophysical Observatory, Caucasian
Mountain Observatory, CHAT, CROW Observatory, Deep Sky West, Dragonfly, El
Sauce, ExTrA, FEROS, Fred L. Whipple Observatory, Gaia, Gemini (‘Alopeke,
Zorro), George Mason University, HATSouth, Hazelwood Observatory, Keck,
Kutztown University Observatory, LCOGT, Lewin Observatory, Lick Observatory,
Lookout Observatory, MASTER-Ural, MEarth-S, Mt. Stuart Observatory, MuSCAT,
MuSCAT2, MuSCAT3, Observatori Astronòmic de la Universitat de València,
Observatori Astronòmic Albanyà, Observatorio del Roque de los Muchachos,
Observatory de Ca l’Ou, Palomar Observatory, PEST, Privat Observatory Herges-
Hallenberg, PvDKO, RCO, SAINT-EX, Salerno University Observatory, Solaris
SLR2, SPECULOOS, SUTO-Otivar, TESS, TRAPPIST, University of Louisville
telescopes at the University of Southern Queensland’s Mt. Kent Observatory and
at Mt. Lemmon Observatory, Union College Observatory, Villa ’39, VLT, WASP,
WCO, Wellesley College Whitin Observatory, WIYN††software: AstroImageJ,
Astropy (Astropy Collaboration et al., 2013, 2018, 2022), astroquery (Ginsburg
et al., 2019), BANZAI (McCully et al., 2018), DEATHSTAR, Jupyter (Kluyver et
al., 2016), Lightkurve (Lightkurve Collaboration et al., 2018), matplotlib
(Hunter, 2007), numpy (Van Der Walt et al., 2011), pandas (McKinney, 2010),
TESS Transit Finder (Jensen, 2013), Tesscut (Brasseur et al., 2019),
TRICERATOPS (Giacalone & Dressing, 2020; Giacalone et al., 2021), VESPA
(Morton, 2012, 2015)

single=true, list/sort=true, cite/cmd= cite/group=true, cite/group/cmd=
patch/longtable=false TSMshort=TSM, long=Transmission Spectroscopy Metric
ESMshort=ESM, long=Emission Spectroscopy Metric TESSshort=TESS, long=the
Transiting Exoplanet Survey Satellite JWSTshort=JWST, long=JWST TOIshort=TOI,
long=TESS Object of Interest, long-plural-form=TESS Objects of Interest
TOIsshort=TOIs, long=TESS Objects of Interest TFOPshort=TFOP, long=the TESS
Follow-up Observation Program TICshort=TIC, long=the TESS Input Catalog
SPOCshort=SPOC, long=Science Processing Operations Center QLPshort=QLP,
long=Quick Look Pipeline, cite=huang2020qlplightcurves DAVEshort=DAVE,
long=the Discovery And Vetting of Exoplanets, cite=kostov2019adave
ZTFshort=ZTF, long=the Zwicky Transient Facility, cite=bellm2019ztf
PDCSAPshort=PDC-SAP, long=Presearch Data Conditioning Simple Aperture
Photometry, cite=smith2012kepler, stumpe2012kepler, stumpe2014multiscale
FPPshort=FPP, long=false positive probability NFPPshort=NFPP, long=nearby
false positive probability

##  1 Introduction

Since the first exoplanets were discovered by Wolszczan & Frail (1992) and
Mayor & Queloz (1995), over 5,000 exoplanets have been confirmed, opening up a
wide array of planets of varying sizes, temperatures, and masses for study.
The rate of exoplanet discovery has notably accelerated over time, originating
with serendipitous or targeted observations and culminating in the concerted
efforts of ground-based surveys such as the Wide Angle Search for Planets
(WASP; Pollacco et al., 2006), the Hungarian-made Automated Telescope Network
(HATNet; Bakos et al., 2004), and HATSouth (Bakos et al., 2013) and space-
based observatories such as the COnvection, ROtation and planetary Transits
satellite (CoRoT; Auvergne et al., 2009; Moutou et al., 2013), Kepler (Borucki
et al., 2010), K2 (Howell et al., 2014), and the Transiting Exoplanet Survey
Satellite (TESS, Ricker et al. 2015).

Although the exoplanet discovery process can reveal important properties of
planets like mass and radius, further observations and analysis are required
to understand the conditions on the planets themselves and examine the
planet’s atmospheric composition and dynamics. The first observation of an
exoplanetary atmosphere was conducted by Charbonneau et al. (2002), and since
then, in a parallel to the diversity of the types of exoplanets, spectroscopic
characterization has revealed a wide variety of atmospheric compositions and
aerosol properties as well (e.g., Sing et al., 2016; Welbanks et al., 2019;
Mansfield et al., 2021; Changeat et al., 2022; August et al., 2023).

Transmission and emission spectroscopy have proven to be the workhorses of
exoplanetary atmospheric characterization. These methods utilize the
absorption of stellar flux transmitted through the exoplanetary atmosphere and
the thermal emission from the exoplanet to probe the atmospheric
characteristics of the planet. Exoplanet atmospheric characterization and
spectral modeling have greatly expanded our understanding of the formation and
evolution of planets, the physical and chemical processes that shape planetary
atmospheres, and atmospheric aerosol properties (e.g. Madhusudhan, 2019;
Mollière et al., 2022; Wordsworth & Kreidberg, 2022) as well as the range of
diverse conditions within each of these individual topics. As the outermost
layer of a planet, the atmosphere is the easiest component of an exoplanet to
probe in detail and can be used to infer other planetary properties.

Although space- and ground-based resources for atmospheric characterization
have become more abundant since the first transmission spectrum was taken,
these resources remain in high demand. The premier atmospheric
characterization tools have largely been the Hubble Space Telescope and, until
its retirement in 2020, the Spitzer Space Telescope, both of which have
historically been heavily oversubscribed. High-resolution spectrographs on
ground-based telescopes have become increasingly important in the study of
exoplanet atmospheres, but these are often limited by what is visible in the
night sky and signal-to-noise ratios.

The highly-anticipated JWST launched in 2021 (Gardner et al., 2006, 2023) with
promises of greatly improved capabilities for transit and eclipse exoplanet
atmospheric characterization (e.g. Deming et al., 2009; Greene et al., 2016;
Stevenson et al., 2016) owing to its large aperture and infrared (IR)
instrument complement. Although still early in its mission, JWST has already
begun delivering on these promises with its first year of exoplanet results
(e.g. Tsai et al., 2023; Ahrer et al., 2023; Greene et al., 2023; Kempton et
al., 2023). This is not even to mention JWST’s capabilities for the
spectroscopy of directly imaged exoplanets (e.g., Miles et al., 2023) which is
impressive but outside the scope of this work. But time on JWST is in high
demand, and this, coupled with the review process for general observer
programs, has resulted so far in a patchwork of exoplanet atmospheric
observations.

Figure 1: All of the JWST exoplanet targets that are approved for transmission
or emission spectroscopy observations in Cycles 1 and 2 across planetary
equilibrium temperature and radius. Yellow stars represent approved JWST
targets that were discovered by TESS while those represented by blue circles
represent planets not discovered by TESS. As evidenced by the plot, TESS-
discovered planets constitute a large proportion of approved JWST Cycle 1 and
2 targets for transmission or emission spectroscopy and cover a wide range of
parameter space.

When it comes to identifying targets for atmospheric characterization
observations, there is a critical synergy between JWST and TESS. Touted from
the very beginning as a “finder scope for JWST,” the almost-all sky survey
strategy of TESS was intended to find a myriad of new planets around bright,
nearby stars that would be amenable to atmospheric characterization with JWST
(Deming et al., 2009), in contrast to the dimmer, more distant host stars of
Kepler planetary systems. So far, TESS has discovered more than 300 confirmed
planets, with more than 4,000 planet candidates classified as unconfirmed TOI
without either a false positive or confirmed planet
disposition111https://exoplanetarchive.ipac.caltech.edu/docs/counts_detail.html.
There is currently no published false positive rate for TESS, although recent
work estimates that it could be somewhere between 15$\%$ and 47$\%$ depending
on the mass of the planet and host star (Zhou et al., 2019; Kunimoto et al.,
2022). Therefore, it is probably that many of these 4,000 TOI are false
positives. However, if even a fraction of them are true planets, this would
dramatically grow the sample of planets whose atmospheres may be well-suited
to observe and characterize with JWST.

In fact, some of the highest quality (i.e. highest signal-to-noise)
atmospheric characterization exoplanet targets likely still lie among the
unconfirmed TOI list, since TESS has unique capabilities for finding small
planets orbiting bright stars in particular. The JWST-TESS synergy is
demonstrated especially by the fact that $\sim$37$\%$ of JWST Cycle 1 and
$\sim$56$\%$ of JWST Cycle 2 exoplanet targets are TESS discoveries. This high
proportion of TESS-discovered JWST targets is displayed in Figure 1. With JWST
already flying, it is of the utmost importance to systematically and
expeditiously identify the best JWST targets to provide a uniform coverage of
parameter space.

In an effort to better streamline use of JWST for atmospheric characterization
and identify which targets are likely to exhibit the most clearly detectable
features in their atmospheric spectra, we present a set of “best-in-class”
targets for transmission and emission spectroscopy. Our best-in-class sample
consists of the targets ranked in the top five according to the TSM and ESM
from Kempton et al. (2018) within each cell of a grid spanning the
$R_{\mathrm{p}}$-$T_{\mathrm{eq}}$ space, which is described in Section 2.
$R_{\mathrm{p}}$-$T_{\mathrm{eq}}$ axes were chosen since radius is expected
to be a proxy for metallicity (Baraffe et al., 1998; Fortney et al., 2013)
while temperature correlates to chemistry and aerosol formation (Gao et al.,
2020) and both parameters are easy to estimate for transiting exoplanets.
Metallicity and atmospheric chemistry can both provide insights to the
formation, physical processes, and composition of a planet’s atmosphere and
are important to probe. We account for the technical capabilities of JWST’s
instruments through the inclusion and calculation of various additional
metrics (e.g. stellar host magnitude, expected atmospheric signal size, and
observability metrics benchmarked against JWST’s instrumental capabilities)
for each target and further incorporate these values into our rankings, thus
tuning our best-in-class sample to JWST specifically.

In our rankings, we initially make no distinction between confirmed planets
and unconfirmed TOI in order to assess how the TESS planet candidates fit in
with the overall sample and to identify which TOI might displace known planets
as best-in-class atmospheric characterization targets. For each unconfirmed
TOI on our best-in-class list, we perform cursory vetting and statistical
validation to determine which targets are likely false positives and which are
worthy of additional follow-up prior to future atmospheric characterization
observations with JWST. We note that while we only statistically “validate”
planets rather than label them as “confirmed”, we consider them to be planets
for the purposes of our best-in-class sample (Torres et al., 2004, 2011). Our
aim is to produce a sample of planets (or likely planets) well-suited for JWST
atmospheric characterization to serve as a community resource for upcoming
JWST proposal cycles and future observing programs aimed at regions of
planetary parameter space where the highest SNR targets have yet to be
identified. Under mass assumptions that we describe in Section 2, these
targets are expected to be well-suited for JWST.

In Section 2, we outline our methodology for obtaining our best-in-class
sample including the data origin, the metrics calculated, and the specific
boundaries in parameter space that were used when defining each class of
planets. In Section 3, we describe the follow-up observations obtained to aid
in our vetting and validation analyses of each unconfirmed TOI contained in
our best-in-class sample. Section 4 details our vetting procedures, the
follow-up and independent resources that were used in our consideration of
false positive scenarios for each unconfirmed TOI, and the criteria against
which each target was compared. Section 5 walks through our statistical
validation procedures including our implementation of statistical validation
software and the disposition categories that we sorted each unconfirmed TOI
into based on the results of our vetting and validation analyses. In Section 6
we summarize the results of our vetting and statistical validation including
which unconfirmed TOI were statistically validated and which we considered
likely false positives. Our findings are summarized in Section 7.

##  2 Grid Generation

Figure 2: Our best-in-class targets for transmission (top) and emission
(bottom) spectroscopy as of November 3, 2022 sorted by equilibrium
temperature, $T_{\rm eq}$, and planetary radius, $R_{\rm p}$. Target names are
shown with the respective spectroscopy metrics (ESM or TSM) in brackets next
to the name. Targets are sorted within each cell by spectroscopy metric in
descending order. Approximate stellar type of the host star is denoted by the
colored circle to the left of each name, determined by the reported effective
temperature. Targets are color-coded by mass status: green targets are
confirmed planets with mass measurements $>$5$\sigma$, yellow targets are
confirmed planets with mass measurements $<$5$\sigma$, and orange targets are
unconfirmed TOI. Figure 3: A schematic outline of our analysis procedure. From
the initial query of the Exoplanet Archive and generation of the best-in-class
sample, each target went through every step of the procedure to check for
factors that could indicate a false positive to arrive at a final disposition.
Not every vetting step applied to every target due to lack of follow-up, so
each vetting step was applied when possible but skipped when not.

Identifying targets across $R_{\mathrm{p}}$-$T_{\mathrm{eq}}$ space that are
well-suited to atmospheric characterization with JWST is critical to our
understanding of exoplanet atmospheres. By sampling across this parameter
space, we expect to cover a range of metallicities as well as atmospheric
chemistry and aerosol regimes that would allow us to tease out trends and test
models on the population level. This could include a mass-metallicity
relation, an aerosol-$T_{\mathrm{eq}}$ relation, or a transition between
planets that have CO vs. CH4 in their atmospheres as the dominant carbon
carrier. To accomplish this, we’ve divided up the
$R_{\mathrm{p}}$-$T_{\mathrm{eq}}$ parameter space into a grid, sorted each
planet and planet candidate into cells within this grid, and ranked each
target according to its expected signal-to-noise ratio approximated via its
TSM or ESM. The samples for both transmission and emission spectroscopy can be
found in Figure 2 and a visual outline of our sample generation is shown in
the top box of Figure 3.

###  2.1 Provenance of Sample Parameters and TSM $\&$ ESM Calculation

In order to obtain a standardized list of planets and planet candidates to
consider when determining which are the best-in-class for atmospheric
characterization with JWST, we relied on the data tables maintained by the
NASA Exoplanet Archive222https://exoplanetarchive.ipac.caltech.edu/ and the
parameter values contained therein. The Exoplanet Archive collates parameter
sets for confirmed and unconfirmed planets and acts as a single repository for
published parameter values for each target. For the confirmed planets, we
downloaded the Planetary Systems table which contains every planet that has a
published validation or confirmation and the accompanying set of parameter
values with a single parameter set labeled as the default by the archive staff
for each planet. For the unconfirmed TOI, we downloaded the TESS Candidates
table from the Exoplanet Archive, which updates directly from the TESS TOI
Catalog (Guerrero et al., 2021) with new targets and refined parameter values
from the TESS mission. These two tables were both downloaded on November 3,
2022. The highest TOI number alerted at this time was TOI-5863.

We elected to use the parameter set denoted as the default set of values for
each of the planets in the Planetary Systems table throughout our analysis. In
the case that the default parameter set was incomplete and missing values for
critical parameters necessary to our analysis, values were pulled from other,
non-default parameter sets for each planet, if they existed. Critical values
included $R_{\rm p}$, $R_{*}$, $T_{*}$, $a$, $J$ magnitude, and $K$ magnitude.
Values with lower uncertainties from other parameter sets were given priority
for inclusion in the final parameter set.

We calculated the TSM and ESM for each planet according to the prescription
outlined in Kempton et al. (2018), specifically equations 1 and 4. The
calculation of TSM and ESM assumes cloud-free atmospheres, solar composition
for planets larger than 1.5 R⊕, and a pure H2O steam atmosphere for planets
smaller than 1.5 R⊕. These two values represent analytical metrics that
quantify the expected signal-to-noise in transmission and thermal emission
spectroscopy for a given planet and can be used to identify which planets are
best-suited for atmospheric characterization with JWST relative to one
another. We maintained two separate samples for best-in-class targets: one for
transmission spectroscopy driven by TSM and the other for emission
spectroscopy driven by ESM. Both of these initially started with the same
overall sample of planets and planet candidates downloaded from the Exoplanet
Archive and were each shaped by the observational constraints unique to each
respective sample. Figure 4 illustrates the parameter space coverage of our
combined best-in-class samples.

Even after pulling values from other parameter sets, some targets did not
contain finite values for all of the parameters necessary to calculate the
spectroscopy metrics and the observability criteria with which we defined and
ranked our sample. For targets without a value for the ratio between semi-
major axis and stellar radius, $a$/$R_{*}$, we converted both the semi-major
axis $a$ and the stellar radius $R_{*}$ to units of meters and took the ratio
of the two. In the case that $a$ was missing but $a$/$R_{*}$ was a finite
value, $a$/$R_{*}$ was multiplied by $R_{*}$ to calculate $a$. A similar
procedure was performed for the ratio between the planet and stellar radii,
$R_{\mathrm{p}}$/$R_{\mathrm{*}}$. We preferred to use the reported ratios if
they existed to reduce the propogation of potential errors in generating these
ratios from the reported values of their individual components. Reported mass
and equilibrium temperature values were used when reported, but were
calculated later in the procedure if unavailable. All targets that still
lacked full parameter sets to perform the necessary calculations were removed
from the sample. We checked each parameter set to ensure that $R_{\rm p}$/R∗
$<$ 1 and targets with values that did not conform to this criterion were
replaced with a value from another parameter set, if available.

For planets from the Planetary Systems table and candidates from the TOI list
that did not have published masses, we calculated masses using a mass-radius
distribution adapted from the mean of the Chen & Kipping (2017) mass-radius
distribution. Specifically, we set the S3 coefficient to be 0.01 rather than
-0.044, to ensure that each radius value corresponded to a unique mass, while
minimally affecting the shape of the curve as presented in Chen & Kipping
(2017). We used this distribution up to planetary radii of 15 R⊕, fixing the
mass of planets larger than this threshold to 1 MJup. Above this radius, the
scatter of the mass-radius distribution is large and results in a mean that is
nearly constant in mass across radius. This is the same procedure that is used
by the Exoplanet Archive to calculate expected masses.

We divided the sample into three categories: confirmed planets with $>$
5$\sigma$ mass measurements, planets marked as confirmed on the Exoplanet
Archive with $<$ 5$\sigma$ mass measurements, and unconfirmed planet
candidates without any mass measurement. Batalha et al. (2019) showed that
different mass confidence levels result in different precision with which an
exoplanet’s atmosphere can be characterized. A stratification of these targets
based on mass measurement will also allow the community to better prioritize
follow-up resources for the best-in-class targets and allowed us to identify
which targets are unconfirmed and in need of statistical validation.

Additionally, we calculated the mass of the host star for each TOI based on
the star’s log $g$ and radius because stellar mass is not included in the
Exoplanet Archive’s TOI table. Using the host star’s reported effective
temperature, we also assigned each host star an approximate stellar type for
reference. We then calculated the equilibrium temperatures $T_{\mathrm{eq}}$
of each planet – both TOI and confirmed – according to Equation 3 of Kempton
et al. (2018). This was done to ensure a uniform data set for $T_{\rm eq}$
since the definition of equilibrium temperature varies with each data set on
the Exoplanet Archive, with different assumptions regarding surface albedo and
atmospheric heat distribution serving as variables with no set standard. Since
$T_{\rm eq}$ is integral to our determination of the best targets for
transmission and emission spectroscopy, we elected to calculate the value for
each planet and planet candidate to ensure a uniform comparison. Our
calculation of $T_{\rm eq}$ assumes zero albedo and full day-night heat
redistribution.

Figure 4: The spread of targets in our best-in-class samples. Left: the
orbital periods and planetary radii of the combined TSM and ESM best-in-class
samples. Right: the effective temperature of the host stars and the planetary
radii of the same combined best-in-class sample. Also shown are the marginal
distributions for each variable. The edges of the radius bins are represented
by the gray dashed horizontal lines. Although only selected to adequately
cover the planetary radius and equilibrium temperature parameter space, the
best-in-class sample exhibits good coverage of multiple different parameter
spaces and can be considered a representative subset of exoplanetary targets.

###  2.2 Observability Cuts

While useful for relative comparisons between targets, the TSM and ESM only
predict the signal to noise but do not account for other observability
considerations such as the absolute signal size relative to the instrumental
noise floor or the target being within an instrument’s brightness limits. To
incorporate the observability of our sample with JWST into our best-in-class
rankings, we also calculated the expected sizes of transmission spectral
features and secondary eclipse depth for transmission and emission
spectroscopy, respectively.

####  2.2.1 Observability in Transmission

We again follow the prescription outlined in Kempton et al. (2018), expressing
the size of expected spectral features at one scale height as

$\frac{2R_{\rm p}}{R_{*}^{2}}\times\frac{kT_{\rm eq}}{\mu g}$ (1)

where $R_{\mathrm{p}}$ is the planetary radius, $R_{\mathrm{*}}$ is the radius
of the host star, $k$ is the Boltzmann constant, $T_{\mathrm{eq}}$ is the
equilibrium temperature of the planet, $\mu$ is the mean molecular weight of
the atmosphere, and $g$ is the surface gravity of the planet. For planets with
$R_{\mathrm{p}}$ $>$ 1.5 R⊕, we assume $\mu$ = 2.3 (in units of proton mass,
mp) while for planets with $R_{\mathrm{p}}$ $<$ 1.5 R⊕, we assume $\mu$ = 18
proton masses, following the assumption that all planets in a given radius bin
have the same atmospheric composition as made by Louie et al. (2018). We
calculated $g$ using the expression $g$=$GM_{\mathrm{p}}$/$R_{\mathrm{p}}^{2}$
where $G$ is the gravitational constant and $M_{\mathrm{p}}$ and
$R_{\mathrm{p}}$ are the mass and radius of the planet, respectively. The
second term of Equation 1 represents the scale height of the planetary
atmosphere, $H$. This is used as a proxy for spectral feature size as it
represents the depth into that atmosphere that is probed at a specific
wavelength, which in turn determines the measured wavelength-dependent
differences in transit depth.

We assumed a depth of 2$H$ when calculating expected spectral feature size
based off of the spread in the sizes of H2O features observed using the Hubble
Space Telescope’s NIR WFC3 instrument (Stevenson, 2016). The average size of
these features was reported to be $\sim$1.5$H$, but at longer wavelengths such
as those probed by JWST, the size of spectral features for molecules such as
H2O increases (e.g., Coulombe et al., 2023), so we elected to assume a depth
slightly above the average reported by Stevenson (2016). Assuming a larger
expected spectral feature also allows for us to capture more planets for
comparison within our sample as well as to account for differences in cloud
cover or the mean molecular weight of exoplanet atmospheres.

In fact, for all constraints applied to our sample, we chose liberal
thresholds in order to allow for more targets to appear in our best-in-class
sample, especially in parameter spaces where there otherwise would be no
promising targets. This was done not only for illustrative purposes, but also
to attempt to account for some of the variance in parameters governing
exoplanet atmospheres and potentially improved observational capabilities
going forward.

To ensure that all best-in-class targets would be observable with JWST, we
imposed a requirement for a 2$\sigma$ spectral signal size assuming a noise
floor of 10 ppm for the NIRCam, NIRISS, and NIRSpec instruments on JWST. These
instruments are all ideal for transmission spectroscopy since their wavelength
coverage includes prominent transmission spectral features. We note the TSM
was benchmarked for use with NIRISS (Kempton et al., 2018).

####  2.2.2 Observability in Emission

We perform a similar procedure for the secondary eclipse depth in order to
determine which targets are amenable for emission spectroscopy with the MIRI
instrument onboard JWST. The expected secondary eclipse depth can be estimated
using the expression

$\frac{B_{7.5}(T_{\rm day})}{B_{7.5}(T_{*})}\times\left(\frac{R_{\rm
p}}{R_{*}}\right)^{2}$ (2)

where $B_{\mathrm{7.5}}$ is the Planck function evaluated for a given
temperature at a representative wavelength of 7.5 $\mu$m, $T_{\mathrm{day}}$
is the dayside temperature of the planet as calculated by 1.1 $\times$
$T_{\mathrm{eq}}$, $T_{\mathrm{*}}$ is the effective temperature of the host
star, and $R_{\mathrm{p}}$/$R_{\mathrm{*}}$ is the ratio of the planetary and
stellar radii. We calculate the dayside temperature as 1.1 $\times$
$T_{\mathrm{eq}}$ to account for the dayside hotspot on the planet, following
the analysis by Kempton et al. (2018) that tuned this relation according to a
suite of global circulation and 1D atmospheric models. The 7.5 $\mu$m was
chosen as the representative wavelength since it is the center of the
“conservative” MIRI LRS bandpass on JWST as data beyond 10 $\mu$m are often
unreliable (Bell et al., 2023; Kempton et al., 2023) and 7.5 $\mu$m is still
near the peak of the MIRI LRS response function (Rieke et al., 2015; Kendrew
et al., 2015). We imposed a requirement that the secondary eclipse depth be
measurable to the 3$\sigma$ level assuming a noise floor of 20 ppm for the
MIRI instrument on JWST. There were more small planets contained within the
emission spectroscopy sample and so we were able to adopt a more conservative
3$\sigma$ threshold rather than the 2$\sigma$ threshold applied to the
transmission spectroscopy sample. We also imposed an ESM $>$ 3 requirement on
our emission spectroscopy sample to remove targets that would produce small
secondary eclipses even under ideal observing conditions with JWST. Like TSM
with NIRISS, ESM was benchmarked for use with MIRI, which is ideal for
emission spectroscopy among JWST’s instruments thanks to its longer wavelength
coverage that maximizes the ratio between the flux of the planet and that of
the host star.

###  2.3 Additional Cuts and Organizing the Sample

We applied additional observability cuts to the sample to ensure that each of
our best-in-class targets would be observable by JWST and would produce
significant spectral detections. For transmission spectroscopy targets, we
restricted the $J$ magnitude of the host star to $>$ 6.0 while for emission
spectroscopy targets we restricted the $K$ magnitude of the host stars to $>$
6.4. These values represent the approximate maximum brightnesses at which the
NIRCam long-wavelength channel grism spectroscopy (which can observe the
brightest stars of the near-infrared spectroscopic modes, Beichman et al.,
2014) and MIRI Low Resolution Spectroscopy (LRS, Kendrew et al., 2016) modes
will not saturate, respectively, according to v2.0 of the JWST exposure time
calculator (Pontoppidan et al., 2016). We also removed any planets or planet
candidates with impact parameter $b$ $>$ 0.9 to remove grazing transits that
could produce unreliable transit depths.

We then divided our full sample of targets that are observable with JWST into
bins of planetary radius and equilibrium temperature to determine which
targets are best for atmospheric characterization in their class. This
division included both confirmed planets and unconfirmed planet candidates.
The edges of these bins in planetary radius were chosen in order to match the
cutoffs used in Kempton et al. (2018), setting the minimum and maximum radii
to include the smallest and largest transiting planets at the time the
Exoplanet Archive was queried. The temperature bin edges were chosen to
capture the ultra-hot Jupiters at $T_{\rm eq}$ $>$ 2250 K, the carbon
equilibrium chemistry transition from CO (and CO2) to CH4 around 800 K
(assuming an otherwise solar C/O ratio, Fortney et al., 2013), and roughly
equal spacing otherwise. The coldest temperature bin in our sample was chosen
to encompass the habitable zone.

###  2.4 Description of Best-in-Class Grids

The planets contained within each bin in radius and temperature space were
then sorted and ranked by TSM and ESM for the transmission spectroscopy and
emission spectroscopy samples, respectively. This ranking was agnostic to
confirmation status and the existence of a well-constrained mass, resulting in
a combination of confirmed planets and unconfirmed planet candidates within
each grid cell. The top five targets in each bin are considered the best-in-
class for that portion of parameter space. Our rankings of the transmission
and emission spectroscopy targets are contained within the grids shown in
Figure 2.

Almost every bin for both the transmission and emission target samples has at
least one unconfirmed planet candidate, with most bins dominated by
unconfirmed candidates. While certainly not all of the planet candidates are
true planets, if even a fraction of the them are, these rankings indicate that
there is a large number of TESS planet candidates that are both (i) among the
best currently known targets for atmospheric characterization with JWST from a
signal-to-noise ratio perspective; and (ii) required to provide a uniform
coverage of the $R_{\mathrm{p}}$-$T_{\mathrm{eq}}$ space.

##  3 Follow-up Observations

In order to determine which of the TESS-discovered planet candidates in our
best-in-class samples are true planets, we first collated all of the follow-up
observations for each target. These follow-up observations provided valuable,
independent information on the validity of each planet candidate as true
planets. We worked closely with TFOP333https://tess.mit.edu/followup subgroups
(SGs) to compile available photometric, spectroscopic, and imaging follow-up
observations for each target. These observations were used in initial vetting
to determine whether each target was a likely false positive or if it could
proceed to more in-depth vetting and validation. TFOP follow-up observations
and the constraints that they impose on the system were incorporated into our
vetting and statistical validation procedures where possible (see Sections 4
and 5). The follow-up resources used in vetting and validating the best-in-
class planet candidates are summarized here with a representative sample of
the specific observations used for individual targets detailed in Table 3
located in Appendix B and a full, machine-readable version available from the
online version of this article. An outline of where follow-up observations
were used in our vetting procedures can be found in the middle panel of Figure
3.

###  3.1 Ground-based Photometry

TFOP’s Sub Group 1 (SG1; Collins, 2019) performed ground-based photometry for
almost all of the targets in our best-in-class samples in order to clear the
background fields of eclipsing binaries (EBs), to check if the candidate
transit signal could be identified as on target, and to check the chromaticity
of the transit shape and depth. This ground-based photometry was taken by a
variety of observatories over a span of multiple years. The TESS Transit
Finder, which is a customized version of the Tapir software package (Jensen,
2013), was used to schedule the transit follow-up observations included here.
Below we detail the observatories, instruments, and data reduction methods
used to obtain the ground-based photometry for our samples. Unless otherwise
noted, all image data were calibrated and photometric data were extracted
using AstroImageJ (Collins et al., 2017). Further discussion on the use of
ground-based photometry in vetting and validation can be found in Sections 4.3
and 5.

####  3.1.1 LCOGT

The Las Cumbres Observatory Global Telescope (LCOGT; Brown et al., 2013) 2.0
m, 1.0 m and 0.4 m network nodes are located at Cerro Tololo Inter-American
Observatory in Chile (CTIO), Siding Spring Observatory near Coonabarabran,
Australia (SSO), South Africa Astronomical Observatory near Sutherland South
Africa (SAAO), Teide Observatory on the island of Tenerife (TEID), McDonald
Observatory near Fort Davis, TX, United States (McD), and Haleakala
Observatory on Maui, Hawai’i (HAl). The MuSCAT3 multi-band imager (Narita et
al., 2020) is installed on the LCOGT 2 m Faulkes Telescope North at Haleakala
Observatory. The image scale is $0\farcs 27$ per pixel resulting in a
$9.1\arcmin\times 9.1\arcmin$ field of view. The 1 m telescopes are located at
all nodes except Haleakala and are equipped with $4096\times 4096$ SINISTRO
cameras having an image scale of $0\farcs 389$ per pixel, resulting in a
$26\arcmin\times 26\arcmin$ field of view. The 0.4 m telescopes are located at
all nodes and are equipped with $2048\times 3072$ pixel SBIG STX6303 cameras
having an image scale of 0$\farcs$57 pixel-1, resulting in a $19\arcmin\times
29\arcmin$ field of view. All LCOGT images were calibrated by the standard
LCOGT BANZAI pipeline (McCully et al., 2018), and differential photometric
data were extracted using AstroImageJ (Collins et al., 2017).

####  3.1.2 MuSCAT

The MuSCAT (Multicolor Simultaneous Camera for studying Atmospheres of
Transiting exoplanets; Narita et al., 2015) multi-color imager is installed at
the 1.88 m telescope of the National Astronomical Observatory of Japan (NAOJ)
in Okayama, Japan. MuSCAT is equipped with three detectors for the Sloan
$g^{\prime}$, Sloan $i^{\prime}$, and Sloan $z^{\prime}_{s}$ band. The image
scale is $0\farcs 358$ per pixel resulting in a $6.1\arcmin\times 6.1\arcmin$
field of view. MuSCAT data were extracted using the custom pipeline described
in Fukui et al. (2011).

####  3.1.3 MuSCAT2

The MuSCAT2 multi-color imager (Narita et al., 2019) is installed at the 1.52
m Telescopio Carlos Sanchez (TCS) in the Teide Observatory, Spain. MuSCAT2
observes simultaneously in Sloan $g^{\prime}$, Sloan $r^{\prime}$, Sloan
$i^{\prime}$, and z-short. The image scale is $0\farcs 44$ per pixel resulting
in a in a $7.4\arcmin\times 7.4\arcmin$ field of view. The photometry was
carried out using standard aperture photometry calibration and reduction steps
with a dedicated MuSCAT2 photometry pipeline, as described in Parviainen et
al. (2019).

####  3.1.4 MEarth-S

MEarth-South (Irwin et al., 2007) consists of eight 0.4 m telescopes and
observes from Cerro Tololo Inter-American Observatory, east of La Serena,
Chile. Each telescope uses an Apogee U230 detector with a $29\arcmin\times
29\arcmin$ field of view and an image scale of $0\farcs 84$ per pixel. Results
were extracted using the custom pipelines described in Irwin et al. (2007).

####  3.1.5 El Sauce

The Evans 0.36 m Planewave telescope is located at the El Sauce Observatory in
Coquimbo Province, Chile. The telescope is equipped with a $1536\times 1024$
pixel SBIG STT-1603-3 detector. The image scale is $1\farcs 47$ per $2\times
2$ binned pixel resulting in an $18.8\arcmin\times 12.5\arcmin$ field of view.

####  3.1.6 Deep Sky West

Deep Sky West is an Observatory in Rowe, NM. The 0.5 m telescope is equipped
with a Apogee U16M detector that has a image scale of $1\farcs 09$ pixel-1
resulting in a $37\arcmin\times 37\arcmin$ field of view.

####  3.1.7 Dragonfly

The Dragonfly Telephoto Array is a remote telescope consisting of an array of
small telephoto lenses roughly equivalent to a 1.0 m refractor housed at the
New Mexico Skies telescope hosting facility, near Mayhill, NM, USA. Dragonfly
uses SBIG STF8300M detectors that have an image scale of $2\farcs 85$ pixel-1,
resulting in a $156\arcmin\times 114\arcmin$ field of view. The data were
reduced and analyzed with a custom differential aperture photometry pipeline
designed for multi-camera image processing and analysis.

####  3.1.8 SUTO-Otivar

The Silesian University of Technology Observatory (SUTO-Otivar) is an
Observatory near Motril, Spain. The 0.3 m telescope is equipped with a ZWO ASI
1600MM detector that has a image scale of $0\farcs 685$ pixel-1, resulting in
a $18\arcmin\times 13\arcmin$ field of view.

####  3.1.9 Wellesley College Whitin Observatory

The Whitin observatory is a 0.7 m telescope in Wellesley, MA. The $2048\times
2048$ FLI ProLine PL23042 detector has an image scale $0\farcs 68$ pixel-1,
resulting in a $23\farcm 2\times 23\farcm 2$ field of view.

####  3.1.10 Adams Observatory

Adams Observatory is located at Austin College in Sherman, TX. The 0.6 m
telescope is equipped with a FLI Proline PL16803 detector that has a image
scale of $0\farcs 38$ pixel-1, resulting in a $26\arcmin\times 26\arcmin$
field of view.

####  3.1.11 OAUV

The Observatori Astronòmic de la Universitat de València (OAUV) is located
near Valencia, Spain. The 0.3m telescope TURIA2 is equipped with a QHY 600
detector that has a image scale of 0$\farcs$68 pixel-1, resulting in a 109′
$\times$ 73′ field of view.

####  3.1.12 Lewin Observatory

The Maury Lewin Astronomical Observatory is located in Glendora, CA. The 0.35
m telescope is equipped with a SBIG STF8300M detector that has a image scale
of $0\farcs 84$ pixel-1, resulting in a $23\arcmin\times 17\arcmin$ field of
view.

####  3.1.13 ASP

The Acton Sky Portal private observatory is in Acton, MA, USA. The 0.36 m
telescope is equipped with an SBIG Aluma CCD4710 camera having an image scale
of $1\arcsec$ pixel-1, resulting in a $17.1\arcmin\times 17.1\arcmin$ field of
view.

####  3.1.14 WCO

The Waffelow Creek Observatory (WCO) is located in Nacogdoches, TX. The 0.35 m
telescope is equipped with a SBIG STXL-6303E detector that has a image scale
of $0\farcs 66$ pixel-1, resulting in a $34\arcmin\times 23\arcmin$ field of
view.

####  3.1.15 PvDKO

The Peter van de Kamp Observatory is located atop the Science Center at
Swarthmore College in Swarthmore, PA. The 0.62 m telescope has a QHY600 CMOS
camera, which yields a $26\arcmin\times 17\arcmin$ field of view.

####  3.1.16 TRAPPIST

The TRAnsiting Planets and PlanetesImals Small Telescope (TRAPPIST) North 0.6
m telescope (Barkaoui et al., 2019) is located at Oukaimeden Observatory in
Morocco and TRAPPIST-South 0.6 m telescope (Gillon et al., 2011) is located at
the ESO La Silla Observatory in Chile (Jehin et al., 2011). TRAPPIST North is
equipped with an Andor IKONL BEX2 DD camera that has an image scale of
0$\farcs$6 per pixel, resulting in a $20\arcmin\times 20\arcmin$ field of
view. TRAPPIST South is equipped with a FLI camera that has an image scale of
0$\farcs$64 per pixel, resulting in a $22\arcmin\times 22\arcmin$ field of
view. The image data were calibrated and photometric data were extracted using
either AstroImageJ or a dedicated pipeline that uses the prose framework
described in Garcia et al. (2022).

####  3.1.17 ExTrA

The Exoplanets in Transits and their Atmospheres (ExTrA) is sited at the ESO
La Silla Observatory in Chile and consists of an array of three 0.6 m
telescopes. Image data were calibrated and photometric data were extracted
using a custom pipeline described in Bonfils et al. (2015).

####  3.1.18 SPECULOOS-S

The SPECULOOS Southern Observatory consists of four 1 m telescopes at the
Paranal Observatory near Cerro Paranal, Chile. (Jehin et al. 2018). The
telescopes are equipped with detectors that have an image scale of 0$\farcs$35
per pixel, resulting in a $12\arcmin\times 12\arcmin$ field of view. The image
data were calibrated and photometric data were extracted using a dedicated
pipeline described in Sebastian et al. (2020).

####  3.1.19 SAINT-EX

The SAINT-EX Observatory is located in San Pedro Mártir, Mexico. The 1.0 m
telescope is equipped with an Andor detector that has an image scale of
0$\farcs$34 per pixel, resulting in a $12\arcmin\times 12\arcmin$ field of
view. The image data were calibrated and photometric data were extracted using
the SAINT-EX automatic reduction and photometry pipeline (PRINCE; Demory et
al., 2020).

####  3.1.20 CHAT

The 0.7 m Chilean-Hungarian Automated Telescope (CHAT) telescope is located at
Las Campanas Observatory, in Atacama, Chile. Image calibration and photometric
data were extracted using standard calibration and reduction steps and by a
custom pipeline which implements bias, dark, and flat-field corrections.

####  3.1.21 Observatori Astronòmic Albanyà

The Observatori Astronòmic Albanyà (OAA) is located in Albanyà, Girona Spain.
The 0.4 m telescope is equipped with a Moravian G4-9000 camera that has an
image scale of $1\farcs 44$ per $2\times 2$ binned pixel resulting in a
$36\arcmin\times 36\arcmin$ field of view.

####  3.1.22 Lookout Observatory

The Lookout Observatory is located in Colorado Springs, CO. The 0.5 m
telescope is equipped with a ZWO ASI1600MM Pro CMOS detector that has an image
scale of $1\farcs 46$ pixel-1, resulting in a $152\arcmin\times 101\arcmin$
field of view. The image data were calibrated and photometric data were
extracted using the reduction and photometry pipeline described in Thomas &
Paczkowski (2021).

####  3.1.23 Brierfield Private Observatory

The Brierfield Observatory is located near Bowral, N.S.W., Australia. The 0.36
m telescope is equipped with a $4096\times 4096$ Moravian 16803 camera with an
image scale of 0$\farcs$74 pixel-1, resulting in a $50\arcmin\times 50\arcmin$
field of view.

####  3.1.24 Caucasian Mountain Observatory

The Caucasian Mountain Observatory (CMO SAI MSU) houses a 0.6 m telescope
(RC600) and is located near Kislovodsk, Russia (Berdnikov et al., 2020). RC600
is equipped with an Andor iKon-L BV detector that has an image scale of
$0\farcs 67$ pixel-1, resulting an a $22\arcmin\times 22\arcmin$ field of
view.

####  3.1.25 Observatory de Ca l’Ou

Observatori de Ca l’Ou (CALOU) is a private observatory in Sant Martí
Sesgueioles, near Barcelona Spain. The 0.4 m telescope is equipped with a
$1024\times 1024$ pixel FLI PL1001 camera having an image scale of $1\farcs$14
pixel-1, resulting in a $21\arcmin\times 21\arcmin$ field of view.

####  3.1.26 Privat Observatory Herges-Hallenberg

The Privat Observatory Herges-Hallenberg is a 0.28 m telescope near Steinbach-
Hallenberg, Germany. It is equipped with a Moravian Instrument G2-1600
detector that has an image scale of $1\farcs$02 pixel-1, resulting an a
$27\arcmin\times 41\arcmin$ field of view.

####  3.1.27 Catania Astrophysical Observatory

The 0.91 m telescope of the Catania Astrophysical Observatory is located on
the slopes of Mt. Etna (1735 m altitude) near Catania, Italy. The custom
imaging camera uses as detector a 1024$\times$1024 KAF1001E CCD with an image
scale of 0$\farcs$66 pixel-1, resulting in a $11\farcm 2\times 11\farcm 2$
field of view444https://openaccess.inaf.it/handle/20.500.12386/764.

####  3.1.28 Campo Catino Astronomical Observatory

The Campo Catino Astronomical Observatory (OACC) is located in Guarcino,
Italy, and is equipped with a 0.8 m RC telescope and a remote 0.6m CDK
telescope located in El Sauce, Chile. In this work, iTelescope T17 was used,
which is a 0.43 m CDK telescope located at Siding Spring Observatory, equipped
with a FLI PL4710 CCD camera, providing a field of view of $15.5\arcmin\times
15.5\arcmin$ and an image scale of $0\farcs 92$ pixel-1.

####  3.1.29 RCO

The 0.4 m RCO telescope is located at the Grand-Pra Observatory in Valais
Sion, Switzerland. The telescope is equipped with a FLI 4710 detector with an
image scale of $0\farcs 73$ pixel-1, resulting in a $12.9\arcmin\times
12.9\arcmin$ field of view.

####  3.1.30 CROW Observatory

The 0.36 m telescope CROW Observatory is located in Portalegre, Portugal. It
is equipped with a SBIG ST-10XME (KAF3200ME) detector that has an image scale
of $0\farcs$66 pixel-1, resulting an a $24\arcmin\times 17\arcmin$ field of
view.

####  3.1.31 MASTER-Ural

The Kourovka observatory of Ural Federal University houses 0.4 m binocular
MASTER-Ural telescope near Yekaterinburg, Russia. Each optical tube is
equipped with an Apogee ALTA U16M detector with an image scale of 1$\farcs$85
pixel-1, resulting an a 120$\arcmin~{}\times$ 120$\arcmin$ field of view. The
image data were calibrated, and photometric data were extracted using the
reduction and photometry pipeline described in Burdanov et al. (2014).

####  3.1.32 Kutztown University Observatory

The 0.6 m telescope at Kutztown University Observatory is located near
Kutztown, PA. The SBIG STXL-6303E detector has an image scale of $0\farcs 76$
per $2\times 2$ binned pixel, resulting in an $13\arcmin\times 19.6\arcmin$
field of view.

####  3.1.33 Union College Observatory

The Union College observatory houses a 0.51 m telescope and is located in
Schenectady, New York. The SBIG STXL detector has an image scale of $0\farcs
93$ per $2\times 2$ binned pixel, resulting in an $30\arcmin\times 20\arcmin$
field of view.

####  3.1.34 George Mason University

The George Mason University 0.8 m telescope near Fairfax, VA. The telescope is
equipped with a $4096\times 4096$ SBIG-16803 camera having an image scale of
$0\farcs 35$ pixel-1, resulting in a $23\arcmin\times 23\arcmin$ field of
view.

####  3.1.35 Mt. Kent CDK700

The University of Louisville’s MKO CDK700 telescope is located near Toowoomba,
QLD, Australia at the University of Southern Queensland’s Mt. Kent
Observatory. It is a remotely operated Planewave Instruments 0.7-meter
corrected Dall-Kirkham telescope with an Apogee U16M camera incorporating an
OnSemi KAF-16803 CCD with $4096\times 4096$ 9 $\mu\mathrm{m}$ 0.41′′ pixels
and a $28^{\prime}\times 28^{\prime}$ field of view.

####  3.1.36 Mt. Lemmon ULMT

The University of Louisville Manner telescope (ULMT) is located near Tucson,
AZ USA at Mt. Lemmon Observatory. It is a remotely operated RC Optical Systems
0.61-meter Ritchie-Chrétien telescope with a focal plane scale of 43
${}^{\prime\prime}\mathrm{mm}$ with SBIG STX 16803 and Apogee U16M cameras
incorporating OnSemi KAF-16803 CCDs with $4096\times 4096$ 9 $\mu\mathrm{m}$
0.39 ′′ pixels for a $27^{\prime}\times 27^{\prime}$ field of view .

####  3.1.37 Mt. Stuart Observatory

The Mt. Stuart Observatory near Dunedin, New Zealand. The 0.32 m telescope is
equipped with a $3072\times 2048$ SBIG STXL6303E camera with an image scale of
0$\farcs$88 pixel-1 resulting in a $44\arcmin\times 30\arcmin$ field of view.

####  3.1.38 Fred L. Whipple Observatory

The Fred Lawrence Whipple Observatory houses a 1.2 m telescope and is located
on Mt. Hopkins in Amado, AZ. The Fairchild CCD 486 detector has an image scale
of $0\farcs 672$ per $2\times 2$ binned pixel, resulting in a $23\farcm
1\times 23\farcm 1$ field of view.

####  3.1.39 Hazelwood Observatory

The Hazelwood Observatory is located near Churchill, Victoria, Australia. The
0.32 m telescope is equipped with a SBIG STT3200 camera with an image scale of
0$\farcs$55 pixel-1, resulting in a $20\arcmin\times 14\arcmin$ field of view.

####  3.1.40 PEST

The Perth Exoplanet Survey Telescope (PEST) is located near Perth, Australia.
The 0.3 m telescope is equipped with a $5544\times 3694$ QHY183M camera.
Images are binned 2x2 in software giving an image scale of 0$\farcs$7 pixel-1
resulting in a $32\arcmin\times 21\arcmin$ field of view. Prior to 23 March
2021 PEST was equipped with a $1530\times 1020$ SBIG ST-8XME camera with an
image scale of 1$\farcs$2 pixel-1 resulting in a $31\arcmin\times 21\arcmin$
field of view. A custom pipeline based on
C-Munipack555http://c-munipack.sourceforge.net was used to calibrate the
images and extract the differential photometry.

####  3.1.41 Salerno University Observatory

The Salerno University Observatory houses a 0.6 m telescope and is located in
Fisciano, Italy. The telescope is equipped with a FingerLakes Instrument
Proline L230 that has a $21\arcmin\times 21\arcmin$ field of view with
$0\farcs 61$ pixel-1.

####  3.1.42 Villa ’39

The Villa ’39 Observatory is located in Landers, CA. The 0.35 m telescope is
equipped with KAF16803 detector that has an image scale of $0\farcs 94$
pixel-1 resulting in a $32.5\arcmin\times 32.5\arcmin$ field of view.

####  3.1.43 Solaris SLR2

The SLR2 is one of four automated telescopes of the Solaris network, owned and
operated by the N. Copernicus Astronomical Center of the Polish Academy of
Sciences. SLR2 is a 0.5-m telescope located in SAAO, equipped an Andor Ikon-L
camera having an image scale of $0\farcs 367$ pixel-1, resulting in a
$12\arcmin\times 12\arcmin$ field of view.

####  3.1.44 Wild Boar Remote Observatory

The Wild Boar Remote Observatory is a private observatory located in San
Casciano in val di Pesa (Firenze), Italy. It has a remotely-operated 0.23 m
Schmidt-Cassegrain telescope equipped with an Sbig ST-8 XME CCD.

####  3.1.45 Gruppo Astrofili Catanesi

The Gruppo Astrofili Catanesi is a private observatory located in Catania,
Italy. It possesses a 0.25 m Newtonian telescope with an Sbig ST-7 XME CCD.

####  3.1.46 Ground Survey and Space Data

We used archival ground-based survey data and related follow-up observations
from HATSouth (Bakos et al., 2013) and WASP (Pollacco et al., 2006) that pre-
dated the TESS mission to help disposition some of the planet candidates. We
also used results from the Gaia-TESS collaboration (Panahi et al., 2022),
which is a joint analysis of TESS photometry and unpublished Gaia time-series
photometry, to disposition some planet candidates. Additionally, we used
archival data taken by ZTF for a subset of the best-in-class TOI to determine
if their signals were on-target. To accomplish this, we implemented the code
DEATHSTAR (Ross et al., submitted) which is further described in Section 4.3.

###  3.2 Reconnaissance Spectroscopy

TFOP’s SG2 performed ground-based reconnaissance spectroscopy on a subset of
targets in our best-in-class samples. These observations are crucial to
constraining the mass of potential stellar or planetary companions to the host
star and for refining the stellar parameters to be used in future analysis.
Below we detail the observatories, instruments, and data reduction methods
used to obtain the reconnaissance spectroscopy used in our analysis. See
Section 4.4 for further discussion on how reconnaissance spectroscopy is used
in our vetting procedures.

####  3.2.1 TRES

Reconnaissance spectra were obtained with the Tillinghast Reflector Echelle
Spectrograph (TRES; Fűrész, 2008) which is mounted on the 1.5m Tillinghast
Reflector telescope at the Fred Lawrence Whipple Observatory (FLWO) located on
Mount Hopkins in Arizona. TRES is a fiber-fed echelle spectrograph with a
wavelength range of 390-910nm and a resolving power of $R\sim$44,000.
Typically, 2-3 spectra of each target are obtained at opposite orbital
quadratures to check for large velocity variation due to a stellar companion.
The spectra are also visually inspected to ensure a single-lined spectrum. The
TRES spectra are extracted as described in Buchhave et al. (2010) and stellar
parameters are derived using the Stellar Parameter Classification tool (SPC;
Buchhave et al., 2012). SPC cross correlates an observed spectrum against a
grid of synthetic spectra based on Kurucz atmospheric models (Kurucz, 1992) to
derive effective temperature, surface gravity, metallicity, and rotational
velocity of the star.

####  3.2.2 FIES

We used the FIbre-fed Echelle Spectrograph (FIES; Telting et al., 2014), a
cross-dispersed high-resolution spectrograph mounted on the 2.56 m Nordic
Optical Telescope (NOT; Djupvik & Andersen, 2010), at the Observatorio del
Roque de los Muchachos in La Palma, Spain. FIES has a maximum resolving power
of $R\sim 67,000$, and a spectral coverage that ranges from 3760 Å to 8820 Å.
The data were extracted as described in Buchhave et al. (2010).

####  3.2.3 CHIRON

We obtained high resolution spectroscopic vetting observations with the CHIRON
spectrograph for a number of the TESS planet candidates. CHIRON is a high
resolution echelle spectrograph on the SMARTS 1.5 m telescope at the Cerro
Tololo Inter-American Observatory, Chile (Tokovinin et al., 2013). We
typically make use of the spectrograph in its ‘slicer’ mode, fed via a fiber
through an image slicer to achieve a spectral resolving power of $R\sim
80,000$ over the wavelength range of $4100-8700\,\AA{}$. Spectral extraction
is performed via the official CHIRON pipeline (Paredes et al., 2021). We
derive radial velocities and spectral line profiles via a least-squares
deconvolution (Donati et al., 1997) between the observed spectra and a non-
rotating synthetic spectral template that matches the atmospheric parameters
of the target star. Radial and line broadening velocities are derived by
modeling the line profile as per Zhou et al. (2020). For some of the faintest
host stars ($V\gtrsim 12.5$), we use CHIRON in ‘fiber’ mode, which achieves a
lower resolving power of $R\sim 28,000$, but yields similar vetting
information at lower precision.

####  3.2.4 Keck/HIRES

We obtained radial velocity data using the Keck Observatory HIRES spectrometer
(Vogt et al., 1994) on the Keck I telescope atop Mauna Kea. We use the iodine
cell technique pioneered by Butler et al. (1996). Radial velocities were
measured using an iodine gaseous absorption cell as a precision velocity
reference, placed just ahead of the spectrometer slit in the converging beam
from the telescope. Doppler shifts from the spectra are determined with the
spectral synthesis technique described by Butler et al. (1996). For this
velocity analysis, the iodine region of the echelle spectrum was subdivided
into $\sim$700 wavelength chunks of 2 Å each. Each chunk provided an
independent measure of the wavelength, PSF, and Doppler shift. The final
measured velocity is the weighted mean of the velocities of the individual
chunks.

####  3.2.5 HARPS-N

HARPS-N is a fiber-fed, cross-dispersed echelle spectrograph with a spectral
resolution of 115,000 mounted at the 3.58 m Telescopio Nazionale Galileo (TNG)
in La Palma island, Spain. It covers the visible wavelength range from 3830 to
6900 Å(Cosentino et al., 2012). Spectra extraction and reduction was carried
out using the HARPS-N data reduction software (DRS). Radial velocities were
obtained by cross-correlating the spectra with a numerical mask close to the
stellar spectral type (e.g., Pepe et al. 2002a).

####  3.2.6 PFS

The Planet Finder Spectrograph (PFS; Crane et al., 2006, 2008, 2010) is
installed at the 6.5 m Magellan/Clay telescope at Las Campanas Observatory.
Targets were observed with the iodine gas absorption cell of the instrument,
adopting an exposure time of 1200 s and using a 3 × 3 CCD binning mode to
minimize read noise. Targets were also observed without the iodine cell in
order to generate the template for computing the RVs, which were derived
following the methodology of Butler et al. (1996).

####  3.2.7 CORALIE

The CORALIE high-resolution echelle spectrograph is mounted on the Swiss Euler
1.2 m telescope at La Silla Observatory, Chile (Queloz et al., 2001). The
spectrograph is fed by a 2” on-sky science fibre and a secondary B-fibre which
can be used for simultaneous wavelength calibrations with a Fabry-Perot etalon
or pointed on-sky to monitor background contamination. CORALIE has a spectral
resolution of $R\sim$60,000 and reaches an RV precision of 3 m/s when photon-
limited. Stellar RV measurements are extracted via cross-correlation with a
mask (Baranne et al., 1996; Pepe et al., 2002b), using the standard CORALIE
data-reduction pipeline. TOIs are vetted using several CCF line-diagnostics
such as bisector-span, FWHM. We also check for mask-dependent RVs, SB2, SB1
and visual binaries. False positives are routinely reported to EXOFOP-TESS and
data made available through the DACE platform666e.g.
https://dace.unige.ch/radialVelocities/?pattern=TOI-128.

####  3.2.8 Minerva-Australis

We carried out spectroscopic observations using the MINERVA-Australis facility
(Addison et al., 2019). MINERVA-Australis consists of an array of four
independently operated 0.7 m CDK700 telescopes situated at the Mount Kent
Observatory in Queensland, Australia. Each telescope simultaneously feeds
stellar light via fiber optic cables to a single KiwiSpec R4-100 high-
resolution (R = 80,000) spectrograph (Barnes et al., 2012) with wavelength
coverage from 480 to 620 nm. Radial velocities for the observations are
derived for each telescope by cross-correlation, where the template being
matched is the mean spectrum of each telescope. The instrumental variations
are corrected by using simultaneous ThAr arc lamp observations.

####  3.2.9 NRES

The Network of Robotic Echelle Spectrographs (NRES; Siverd et al., 2018) is a
set of four identical fiber-fed spectrographs on the 1m telescopes of LCOGT
(Brown et al., 2013). The NRES units are located at the LCOGT nodes at Cerro
Tololo Inter-American Observatory, Chile; McDonald Observatory, Texas, USA;
South African Astronomical Observatory, South Africa; and Wise Observatory,
Israel. The spectrographs deliver a resolving power of $R\sim$53,000 over the
wavelength range 3800-8600 Å. The data were reduced and radial velocities
measured using the BANZAI-NRES pipeline (McCully et al., 2022). We measured
stellar parameters from the spectra using a custom implementation of the
SpecMatch-Synth package777https://github.com/petigura/specmatch-syn (Petigura
et al., 2017).

####  3.2.10 FEROS

The Fiber-fed Extended Range Optical Spectrograph (FEROS; Kaufer & Pasquini,
1998) spectrograph is a high resolution (R$\sim$48,000) echelle spectrograph
installed at the MPG2.2m telescope at the ESO La Silla Observatory, Chile.
FEROS covers the spectral range between 350 and 920 nm and has a comparison
fiber to trace instrumental radial velocity drifts during the science
exposures with a thorium argon lamp. FEROS data are processed with the
automated ceres pipeline (Brahm et al., 2017) that generates precision radial
velocities and bisector span measurements starting from the raw images which
are reduced, optimally extracted and wavelength calibrated before cross-
correlating the spectrum with a G2-type binary mask.

###  3.3 High-resolution Imaging

As part of our standard process for validating transiting exoplanets to assess
the possible contamination of bound or unbound companions on the derived
planetary radii (Ciardi et al., 2015), we also observed a subset of the
unconfirmed TOI in our best-in-class sample with a combination of near-
infrared adaptive optics (AO) imaging and optical speckle interferometry at a
variety of observatories including Gemini, Keck, Lick, Palomar, VLT, and WIYN
Observatories. The combination of the observations in multiple filters enables
better characterization for any companions that may be detected and improves
the sensitivity to different types of false positive scenarios (e.g. bound
low-mass companions, background stars, etc.). See Sections 4.5 and 5 for
further discussion on how high-resolution imaging was incorporated into our
vetting and validation analyses, respectively.

####  3.3.1 Near-Infrared AO Imaging

Near-infrared AO observations are performed with a dither pattern to enable
the creation of a sky-frame from a median of the science frames. All science
frames are flat-fielded (which are dark-subtracted) and sky-subtracted. The
reduced science frames are combined into a single combined image using an
intra-pixel interpolation that conserves flux, shifting the individual
dithered frames by the appropriate fractional pixels; the final resolution of
the combined dithers was determined from the full-width half-maximum of the
point spread function. The sensitivities of the final combined AO images were
determined by injecting simulated sources azimuthally around the primary
target every $20^{\circ}$ at separations of integer multiples of the central
source’s FWHM (Furlan et al., 2017). The brightness of each injected source
was scaled until standard aperture photometry detected it with $5\sigma$
significance. The final $5\sigma$ limit at each separation was determined from
the average of all of the determined limits at that separation and the
uncertainty on the limit was set by the rms dispersion of the azimuthal slices
at a given radial distance.

####  3.3.2 Optical Speckle Imaging

High-Resolution optical speckle interferometry was performed using the
‘Alopeke and Zorro instruments mounted on the Gemini North and South
telescopes respectively (Scott et al., 2021; Howell et al., 2021). These
identical instruments provide simultaneous speckle imaging in two bands (562
nm and 832 nm) with output data products including a reconstructed image and
robust contrast limits on companion detections (Howell et al., 2011). For each
observed source, the final reduced data products contain 5$\sigma$ contrast
curves as a function of angular separation, information on any detected
stellar companions within the angular range of $\sim 0.03\arcsec$ to
$1.2\arcsec$ (delta magnitude, separation, and position angle), and
reconstructed speckle images in each band-pass. The angular separation
sampled, from the 8 m telescope diffraction limit (20 mas) out to
$1.2\arcsec$, can be used to set spatial limits in which companions were or
were not detected.

##  4 Vetting

In order to determine the planetary nature of each target, we performed a
uniform vetting procedure on each of the unconfirmed candidates. This included
utilizing a mix of publicly-available resources and follow-up observations
obtained by TFOP. We outline our overall procedure in schematic form in the
middle panel of Figure 3. We ran each target through as many steps of our
vetting procedure as possible given the availability of resources at the time
of analysis in early 2023 since not all targets had the resources to complete
each step in our procedure.

Although our vetting procedure checked for a number of false positive
indicators, we refrained from classifying a target as a likely false positive
unless multiple false positive indicators suggested that the origin of the
transit signal could not have been a planet. Our conservative approach to
vetting passed most targets on to statistical validation and provided
invaluable information to be used in conjunction with the results from our
validation analysis to make a final determination, such as if the signal is
on-target and if there were any potentially conatimating sources contained in
the light curve’s extraction aperture. In this way, vetting served as a
complement to a more holistic determination of the planetary nature that
accounts for a larger number of factors than any individual analysis alone
could provide.

For all of our vetting, we used the orbital and planetary parameter values
posted on ExoFOP888https://exofop.ipac.caltech.edu/tess/ unless follow-up
observations revealed more accurate or precise values for a given parameter,
in which case, the parameters obtained from follow-up were used. There were
seven targets with ambiguous periods contained in our best-in-class samples
from the query of the Exoplanet Archive (TOIs 706.01, 1856.01, 1895.01,
2299.01, 4317.01, 5575.01, and 5746.01). These targets were single transits
that transited again in one or more later TESS sectors, but without measuring
two or more consecutive transits, the period could not be confidently
determined. The orbital period of TOI-5575.01 was was uniquely determined to
be 32.07 d through follow-up observations over the course of our
analysis999https://exofop.ipac.caltech.edu/tess/target.php?id=160162137. We
propagated this updated period throughout our analysis and report the planet’s
updated parameters in our final best-in-class sample. Since we are unable to
obtain the true periods of these remaining six targets without a concerted
observing campaign, we analyzed them with the reported ExoFOP periods that
represent upper limits to the true periods. Shorter periods would likely
result in higher equilibrium temperatures which, although this would boost the
TSM, could place these targets in a different temperature bin where they may
not rank in the top 5 targets in their planetary radius bin. We recognize that
the periods and therefore amenability to atmospheric characterization with
JWST may change for these targets, but we include them in our best-in-class
samples to emphasize their potential as prime JWST targets and encourage their
further study.

###  4.1 TESS SPOC Data Validation Report

$\sim$92$\%$ of the targets in our best-in-class samples were either
discovered by (or at the very least run through) the TESS SPOC pipeline
(Jenkins et al., 2016) at NASA’s Ames Research Center. This SPOC pipeline
performs a number of tasks on each target including light curve extraction to
generate Simple Aperture Photometry (SAP) light curves (Twicken et al., 2010;
Morris et al., 2020) and systematic error correction to generate PDCSAP light
curves. The pipeline also searches for potential planets as well as performs a
suite of diagnostic tests in the Data Validation (DV) module to help
adjudicate the planetary nature of each signal (Twicken et al., 2018; Li et
al., 2019). Upon running the pipeline, the outputs were reviewed by the TESS
TOI Working Group (TOI WG) to perform initial vetting. This initial vetting
has already been performed by the TOI WG for all of our targets, but we
reviewed the SPOC pipeline outputs again to ensure nothing was missed.

The DV module includes a depth test of the odd and even transits, a
statistical bootstrap test that accounts for the non-white nature of the
observation noise to estimate the probability of a false alarm from random
noise fluctuations, a ghost diagnostic test to compare the detection statistic
of the optimal aperture against that of a halo with a 1 pixel buffer around
the optimal aperture, and a difference image centroid test. At the conclusion
of these tests, the module synthesizes a summary of the results for each
individual test, including assigning a pass/fail disposition for each test. We
used the results of each of these tests in our vetting efforts to help
determine if a target was a likely planet, likely false positive, or false
alarm.

In addition to the DV module results, we also determined if the period was
ambiguous for a given target due to nonconsecutive transits from gaps in the
TESS data. Although not a false positive indicator, this was flagged for
future reference in downstream analyses. We also checked the light curves for
significant photometric modulation indicative of stellar activity that could
pose a problem in future vetting and validation analysis. In the absence of
SPOC DV results, we still inspected the light curve and ephemerides for an
ambiguous period or photometric modulation using available, published light
curves such as those from MIT’s QLP.

###  4.2 DAVE Vetting from Cacciapuoti, et al.

A subset ($\sim$15$\%$) of our targets had already been vetted not only by the
TESS TOI WG, but by an independent team using DAVE. The results of this
vetting were collated in Cacciapuoti et al. (2022) where each of the 999
targets vetted were assigned a final disposition as to the target’s planetary
nature.

DAVE is an automated vetting pipeline built upon many of the tools developed
for vetting planets in Kepler data (e.g. RoboVetter, Coughlin et al. 2014) and
has been used extensively in vetting planets for TESS (e.g. Gilbert et al.,
2020; Hord et al., 2021; Quintana et al., 2023). DAVE performs two sets of
vetting tests: 1) light curve-based vetting tests searching for odd/even
transit depth differences, secondary eclipses, and light curve modulations and
2) image-based centroid tests to check the photometric motion on the TESS
image during transit.

For the targets in our best-in-class samples that were also contained in the
Cacciapuoti et al. (2022) catalog, we included their dispositions in our
vetting analysis. Since there is overlap between the tests performed by the
TESS SPOC pipeline and DAVE, we treat the two as independent checks of one
another and review the results in comparison.

###  4.3 Reconnaissance Photometry

Due to the large 21” pixel size of TESS, ground-based photometry at higher
spatial resolutions is crucial in determining whether a transit-like feature
is occurring on-target or is the result of a background target in the
starfield that may have been blended within the TESS pixel. Stars nearby the
target are checked for deep EBs that could cause the observed transits and are
ruled out on a case-by-case basis. Any deviations from an on-time transit are
also noted. These often occur due to uncertainties in the period or mid-
transit time reported by ExoFOP but may be caused by gravitational
interactions within the system. If the period deviates significantly from the
reported period, the ephemerides are refined based on the ground-based
photometric observations. This was the case for multiple targets, especially
those with fewer sectors of TESS data or those with an ambiguous period.

In addition to checking which star the transit-like feature originates from,
ground-based photometry uses multiple filters to check for possible
chromaticity in the transit depth that would indicate an eclipsing binary
rather than a planet is causing the transit. A light curve is also extracted
from the target star with a small aperture to mitigate the contamination from
nearby stars. The transit depth is measured to ensure that it is not only
consistent across wavelength bandpasses, but is the right depth to cause the
transit observed in the TESS data.

TFOP’s SG1 synthesizes the results of the photometric observations for each
target into a single disposition describing the confidence with which a signal
can be considered on-target. We utilized these dispositions and observations
when determining which background stars to consider as potential sources of
astrophysical false positives in our vetting analysis.

In addition to the photometry gathered by SG1, we also utilized the code
DEATHSTAR (Ross et al., submitted) to search archival images from ZTF for the
transit signal.

DEATHSTAR attempts to either confirm or refute exoplanet detections with
already available ground-based data from ZTF by extracting light curves for
each star in a 2.5 arcminute field and plotting them for manual verification
of the actual signal location. In this way we can often tell if an unconfirmed
TOI is an exoplanet transiting in front of the target star or an eclipsing
binary on a nearby fainter star. DEATHSTAR creates plots for each extracted
light curve and displays them in custom sheets for us to easily find the
source of the transiting signal. We work with SG1 in checking these results
with the SG1 Observation Coordinator sheet and sending them to reduce
extraneous telescope follow-up time. For deeper transit depths on-target
(ranging from 1-3%), DEATHSTAR has been able to confirm on-target detections.
Because the target stars are bright ($J$ ¡ 13 mag) and given ZTF’s
sensitivity, we were able to check for and rule out eclipsing binaries among
the surrounding stars in the TESS apertures down to the faintest stars that
could account for the transit depth. Due to ZTF’s multiple filters ($g$, $r$,
and $i$ bands), we can constrain the chromaticity of the transit signal, which
can also indicate or help rule out false positives. In most of the cases for
these targets, the depth was much shallower than a percent, rendering the
transits undetectable by DEATHSTAR on-target, but we still cleared all the
surrounding stars in the field for being potential eclipsing binaries, showing
the transit signal must originate from the target by process of elimination.

###  4.4 Reconnaissance Spectroscopy

Although only a subset of the targets in our sample had ground-based
spectroscopic observations available, these data provided strong constraints
on the presence of bound companions in the target system that photometry is
unable to capture. Spectroscopy alone is often able to determine if the
stellar spectrum is composite which would indicate the presence of a bound
stellar mass companion. The presence of a composite spectrum with orbital
motion that is consistent with the TESS ephemeris was an automatic likely
false positive designation for the targets in our samples but only applied to
one target (TOI-4506.01).

For most targets, two spectroscopic observations were taken at opposite
quadratures assuming a circular orbit at the photometric ephemeris and
compared to the photometric ephemeris to determine if they were in-phase.
Spectroscopic data at opposite quadratures that are out of phase with the
photometric ephemeris could indicate the presence of a large stellar-mass
object instead of a planet, although this could also indicate a long-term
trend in the system due to additional bodies in the system or an eccentric
orbit rather than a false positive scenario. For reconnaissance spectroscopy
that was in-phase with the photometric ephemeris, the semi-amplitude of the
measurements at quadrature was used to constrain the mass of the object
producing the transit signal, potentially ruling out stellar masses and
providing evidence for the planetary nature of the body.

By virtue of modeling the stellar spectrum, reconnaissance spectroscopy also
has the potential to measure parameters such as the effective temperature,
metallicity, and $v$ sin$i$ of the host star. Where possible, we used these
measured values rather than those from TIC or Gaia DR3.

Similar to SG1, TFOP’s SG2 also synthesizes reconnaissance spectroscopic
observations into a disposition for each target. These dispositions capture
the confidence that the target is a planetary mass object and is suitable for
precision radial velocity observations to determine the orbit and constrain
the mass further. We broadly utilized these dispositions when vetting to
determine whether a target can be safely deemed a likely false positive or
should continue to statistical validation analysis. There were multiple cases
where reconnaissance spectroscopy existed but the stellar activity or
rotational broadening of spectral features precluded anything but upper limits
on the masses of potential companions.

###  4.5 Imaging Constraints

As a complement to ground-based photometry and reconnaissance spectroscopy,
high-resolution imaging can provide strict constraints on the presence of
stellar companions in the system or nearby background targets that could
potentially contaminate the target signal. Each target was first cross-
referenced with the Gaia DR3 catalogue to determine if there are any resolved
nearby stars within a few arcseconds of the target star. In a handful of
cases, Gaia resolved nearby stars at similar parallaxes to targets in our
best-in-class samples. While not a definite indicator of a false positive, the
presence of a nearby companion at a similar parallax invited further scrutiny
for that particular target. In those cases, we cross-referenced the nearby
star with other follow-up observations where possible to determine if the star
observed by Gaia may be the cause of anomalies and potential false positive
indicators in the ground-based photometry or reconnaissance spectroscopy.

We also utilized speckle or adaptive optics (AO) imaging available on ExoFOP
(see Section 3.3) that observed each planet candidate in a more targeted
manner at a higher angular resolution than Gaia. These observations allowed us
to search for bound companions or background stars that may contaminate the
photometry or cause the observed transit signal. These observations were also
cross-referenced with other follow-up observations to determine how strongly
false positive or dilution scenarios can be constrained or if the signal is
likely not due to a planet. The sensitivity curves that these observations
produced were also used in our statistical validation analysis (Section 5).

##  5 Validation

While vetting is an integral step in determining whether a periodic signal is
indeed due to the presence of a planet, it cannot alone demonstrate that a
signal is not a false positive. The preferred method for determining whether a
signal is a planet is a mass measurement through radial velocity (RV)
observations, however these oftentimes require a significant commitment of
resources and time on targets that may not prove to be planets.

In lieu of a mass measurement, statistics can be used to validate the target
rather than confirm it. Statistical validation of a target often only requires
photometric and imaging observations as well as planetary and orbital
parameters input into one or multiple statistical validation software
packages. Targets that are validated to a greater than 99$\%$ confidence
threshold are considered planets despite not having a mass measurement
(Morton, 2012; Giacalone et al., 2021). Since the time and observational
resources required to validate a planet are far less than required to obtain a
mass measurement, statistical validation serves as an excellent intermediate
step to weed out targets that are very likely not planets in order to better
streamline and prioritize the RV observations required to confirm a target as
a bona fide planet.

In the case of our best-in-class samples, since there are undoubtedly false
positives among the unconfirmed planet candidates, we performed statistical
validation on all candidate planets to not only determine which targets are
most likely to be true planets, but which merit follow-up with RV
observations. To do this, we run the statistical validation software vespa
(Morton, 2012, 2015) and TRICERATOPS (Giacalone & Dressing, 2020) on each of
our unconfirmed targets in both the transmission and emission spectroscopy
samples.

For all of our targets, we use the orbital and planetary parameters from
ExoFOP unless the follow-up observations reported refined parameters (Section
3), in which case the refined parameters were used. For vespa, this also
included stellar parameters. TESS photometry was used to produce the phase-
folded transits used in both vespa and TRICERATOPS. When possible, we favored
light curves produced by the TESS SPOC at the shortest cadence available since
shorter cadence TESS data have been shown to be more photometrically precise
when binned than data taken at the binned cadence itself (Huber et al., 2022).
A small subset of targets did not have SPOC PDCSAP light curves, in which case
we used light curves produced by MIT’s QLP.

###  5.1 vespa

vespa (Morton, 2012, 2015) was originally developed for use on Kepler data and
compares the input orbital and planetary parameters as well as the phase-
folded transit against a number of astrophysical false positive scenarios to
determine the likelihood that the signal can be produced by each false
positive population. Currently, vespa tests against the hypotheses that the
signal is a blended background or foreground EB (BEB), the target itself is an
EB, or the target is a hierarchical-triple system where two of the components
form an EB (HEB). To do this, vespa simulates a representative population of
each false positive scenario at the observed period and calculates the priors
of each scenario, accounting for the probability that the scenario is
contained within the photometric aperture, the probability of an orbital
alignment that would cause an observable eclipse, and the probability that the
eclipse could mimic a transit. A TRILEGAL simulation (Girardi et al., 2005,
2012) is used to simulate the background starfield for each target when
calculating the priors. The likelihoods of each scenario are then calculated
by modeling the shape of the eclipse for each instance of each false positive
population and fitting it to the observed light curve. The priors and
likelihoods are finally combined to calculate the total FPP of the input
transit signal. Signals with an FPP $<$ 0.01 are considered statistically
validated.

Beyond the phase-folded light curve and planetary and orbital parameters,
vespa can also intake sensitivity curves from high-resolution imaging to rule
out portions of the false positive parameter space. Additionally, vespa takes
the maximum photometric aperture radius as an input to use in calculations of
the BEB prior. We set this parameter to $42\arcsec$, the size of two TESS
pixels. This is very conservative since the difference image centroiding
results from the SPOC DV analysis often constrain the location of the target
star to within a fraction of a pixel of the location of the source of the
transit.

vespa assumes that the signal originates on-target, which we have attempted to
show for as many targets in our sample as possible (see Section 4). We urge
caution in the interpretation of the results from vespa in the cases where the
signal was not demonstrated to be on-target.

###  5.2 TRICERATOPS

Figure 5: Starfield around TOI-4336.01 in TESS Sector 38 used by TRICERATOPS
in its FPP and NFPP calculations. Left: plot of the positions of each star
within 2.5 arcminutes centered on the target with the color of each point
representing the TESS magnitude of the star. The overlaid grid denotes the
TESS pixel borders with pixel column and row numbers labeled on the X and Y
axis, respectively. The dashed gray circle represents a distance of 2.5
arcminutes and the red squares denote the extraction aperture used by the SPOC
when generating the PDC$\\_$SAP light curve for this TESS sector. Right: Same
as left but instead of displaying each background star near the target, TESS
data are shown. The SPOC extraction aperture is in red and the colormap
represents the flux captured by each TESS pixel.

Similar to vespa, TRICERATOPS (Giacalone & Dressing, 2020) compares the user-
provided phase-folded transit, orbital, and stellar parameters against a set
of astrophysical false positive scenarios to rule out portions of parameter
space in which the false positive scenarios can remain viable. The methodology
of TRICERATOPS is identical to vespa in many respects, however, in contrast to
vespa, TRICERATOPS was developed specifically for TESS and accounts for the
real sky background of each target out to 2.5’ as well as the TESS point
spread function and aperture used to extract the photometric light curve in
each sector of TESS data. An example of what TRICERATOPS considers in this
portion of its analysis is seen in Figure 5.

For each target, we used the extraction apertures produced by the TESS SPOC
contained within the headers of the SPOC PDC-SAP light curves queried by
lightkurve (Lightkurve Collaboration et al., 2018) on a sector-by-sector
basis. For the targets missing SPOC PDC-SAP light curves from some or all TESS
sectors they were observed in, we used a standard aperture of 5$\times$5 TESS
pixels. This is larger than any of the PDC-SAP apertures and is the
TRICERATOPS default for sectors without provided apertures.

When accounting for nearby background stars for each target, TRICERATOPS
queries the TICv8 for the stellar parameters of each star. The TIC is based
heavily on the Gaia DR2 data release, which has since been updated by Gaia
DR3. Therefore, in our analysis, we queried the RA, Dec, mass, effective
temperature, parallax, and Gaia G magnitude of the host star Gaia DR3 Catalog
for use in our analysis in lieu of using the values provided by the TIC. To
convert the Gaia magnitude to TESS magnitude, we used Equation 1 from Stassun
et al. (2019) which is valid for dwarfs, subgiants, and giants of any
metallicity. We then cross-referenced each Gaia target with the 2MASS catalog
(Skrutskie et al., 2006) to obtain J, H, and K magnitudes where available as
these magnitudes are used by TRICERATOPS in its estimation of false positive
probability.

Additionally, we included follow-up constraints into our analysis with
TRICERATOPS. When available, we included a contrast curve from high-resolution
imaging to constrain the existence of additional stellar mass companions in
the system. Unlike vespa, TRICERATOPS accepts only a single contrast curve per
target, so in the case a target possessed multiple contrast curves from
follow-up observations, we included only the contrast curve that provided the
greatest imaging contrast magnitude agnostic of bandpass to most stringently
constrain possible companions in the system. Furthermore, our photometric
follow-up allowed us to clear individual nearby stars of potentially harboring
EBs that would cause the observed transit signal on target. Background stars
that were definitively determined to not be EBs at the target period or have
an eclipse depth that could cause the observed transit on-target were
discarded from consideration as potential sources of a false positive. Targets
whose transits were observed on-target had all background stars removed from
false positive consideration. As recommended by Giacalone et al. (2021), we
ran multiple trials of the TRICERATOPS FPP calculation for each target, with a
minimum of 10 trials per target and report the mean of these FPP.

TRICERATOPS provides not only a final FPP value, but also a NFPP value that
encapsulates the probability that the signal originates from a star other than
the target. Giacalone et al. (2021) defines validated planets as signals with
FPP $<$ 0.015 and NFPP $<$ 10-3 and outlines a separate category for marginal
validations when FPP $<$ 0.5 and NFPP $<$ 10-3. We adopt these categories in
our determination of the planetary nature for our best-in-class samples. We
extend the marginal validations category to vespa, which does not explicitly
have such a distinction. In the case of vespa, we conservatively set the
marginal validation threshold to FPP $<$ 0.25, lower than that of TRICERATOPS.

Morton et al. (2023) recommends the use of TRICERATOPS in favor of vespa since
the latter is no longer maintained and has not been updated to account for the
modern astronomy landscape. We present validation using both software packages
as an independent check on one another but emphasize the results of
TRICERATOPS over those of vespa in cases where their FPP values may disagree.
This means that many of the targets in our best-in-class sample that are
classified as “Likely Planets” may actually fall within the realm of true
statistical validation when considering only the results from TRICERATOPS.

We also note that our statistical validation analysis cannot rule out the
scenario in which validated planets with $R_{\rm p}$ $>$ 9 R⊕ are actually
brown dwarfs. A measured mass is required to disentangle the brown dwarf and
planet scenarios and we encourage follow-up on all validated planets to this
effect.

##  6 Results

Figure 6: Examples of transits from targets in each disposition category. Left
column: examples of validated planets. Both transits are well-defined with
flat bottoms. Middle column: examples of marginal validations; a likely planet
(top) and a potential false positive (bottom). These targets either have a low
signal-to-noise ratio or a transit shape that can be confused with an
eclipsing binary and cannot be validated but are also not clear false
positives. Right column: examples of likely false positives. These targets
either have a very obvious V-shape, otherwise non-transit-shaped feature, or
have been deemed likely false positives during vetting (e.g. a large centroid
offset).

Of the 103 unconfirmed TESS planet candidates contained in our best-in-class
samples, 19 passed vetting and were calculated to have FPP values firmly
meeting the threshold for statistical validation from both vespa and
TRICERATOPS. Additionally, 11 of the original 103 unconfirmed planet
candidates reside in potential multiplanet systems (TOI-1468.01, TOI-1468.02,
TOI-1798.02, TOI-1806.01, TOI-2134.01, TOI-3353.01, TOI-406.01, TOI-4443.01,
TOI-4495.01, TOI-836.02, TOI-880.02). Three of these have already been
confirmed by independent teams (TOI-1468.01 and .02 and TOI-836.02). The
remaining eight TOI are able to take advantage of a “multiplicity boost” to
drive their FPP values lower. It has been shown that transit-like signals in
systems with multiple transit-like signals are more likely to be true planets,
assuming false positives are uniformly distributed throughout the sky
(Lissauer et al., 2012). This results in a decreased FPP value of up to
54$\times$ depending on the size of the planets, how crowded the field is for
signals detected with TESS, and the pipeline with which they were detected
(Guerrero et al., 2021). Additionally, these potential multiplanet systems
represent an excellent opportunity to perform comparative planetology with the
other planets in their system using JWST.

We applied this multiplicity boost to each of the eight candidates listed
above resulting in FPP values below the validation threshold for each of them.
Four of these eight already possessed FPP values from vespa and TRICERATOPS
that were low enough to be statistically validated, but the FPP values of the
other four targets (TOI-880.02, TOI-1798.02, TOI-1806.01, and TOI-4443.01)
moved from the “marginal validation” range into the “validated planet” range.
We thus arrive at a total of 23 statistically validated planets. These targets
are shown in Table 1. We strongly recommend these targets for additional, in-
depth study and confirmation to measure their masses and model their orbits
and atmospheres in preparation for potential observation with JWST.

Table 1: All of the statistically validated planets in both the transmission and emission spectroscopy best-in-class samples. Empty values for the TSM and ESM indicate that the target was not considered best-in-class for transmission or emission spectroscopy, respectively. Planet Name |  $T_{\rm eq}$ [K] |  $R_{\rm p}$ [R⊕ ] | Period [d] | TSM | ESM
---|---|---|---|---|---
TOI-128.01 | 1345 | 2.22 | 4.94 | 90 |
TOI-238.01 | 1454 | 1.86 | 1.27 |  | 8
TOI-261.01 | 1722 | 3.04 | 3.36 | 79 |
TOI-332.01 | 1946 | 3.28 | 0.78 | 62 | 11
TOI-406.01 | 344 | 1.96 | 13.18 | 55 |
TOI-654.01 | 749 | 2.37 | 1.53 |  | 9
TOI-880.02 | 1163 | 2.78 | 2.57 | 119 |
TOI-907.01 | 1847 | 9.62 | 4.58 |  | 28
TOI-1135.01 | 1074 | 9.34 | 8.03 | 243 |
TOI-1194.01 | 1405 | 8.89 | 2.31 | 217 | 69
TOI-1347.01 | 1793 | 2.70 | 0.85 | 79 | 12
TOI-1410.01 | 1396 | 2.94 | 1.22 | 118 | 20
TOI-1683.01 | 929 | 2.64 | 3.06 | 101 |
TOI-1798.02 | 2122 | 1.41 | 0.44 |  | 6
TOI-1806.01 | 337 | 3.41 | 15.15 | 60 |
TOI-2134.01 | 676 | 3.10 | 9.23 | 196 |
TOI-3353.01 | 1264 | 2.67 | 4.67 | 90 |
TOI-4443.01 | 1639 | 1.72 | 1.85 | 97 |
TOI-4495.01 | 1383 | 3.63 | 5.18 | 74 |
TOI-4527.01 | 1363 | 0.91 | 0.40 |  | 13
TOI-4602.01 | 1380 | 2.55 | 3.98 | 111 | 11
TOI-5082.01 | 1165 | 2.55 | 4.24 | 160 | 15
TOI-5388.01 | 601 | 1.89 | 2.59 |  | 12

A total of 29 targets were deemed “likely false positives” (LFPs). These
targets all exhibited clear signs of a false positive in the vetting stage
and/or produced FPP values from both statistical validation software packages.
A target was deemed a likely false positive if the FPP from both vespa and
TRICERATOPS did not meet either the validation or marginal validation
thresholds. For one of these likely false positive targets, we were unable to
locate the transit-like event that was flagged by the TESS SPOC during our
manual inspection of the phase-folded light curve and we deemed it a false
alarm (TOI-1022.01). Most of these 29 likely false positive targets exhibited
obvious V-shaped transits indicative of an EB and a subset of them were
revealed by TFOP follow-up to have a nearby ($\leq$2”) companion star that
served as the likely cause of the signal.

There was a subset of targets with high FPP values that could be large grazing
planets or systems with a high planetary to stellar radius ratio ($R_{\rm
p}$/$R_{*}$) rather than their current LFP classification. Grazing transits or
high $R_{\rm p}$/$R_{*}$ systems often produce transits that look somewhat
V-shaped and can masquerade as a stellar eclipse rather than a planet transit.
These scenarios are limiting cases for the validation software since the
analyses rely so heavily on transit shape. Therefore, targets with high FPP
values that could potentially fall under these categories warrant further
follow-up. For our purposes, we keep these targets classified as LFPs not only
for the sake of a uniform analysis, but also because grazing transits are non-
ideal candidates for transit and eclipse spectroscopy. However, we flag them
here for future study and as examples of the limitations of statistical
validation.

A third category of validation emerged for targets with FPP values that did
not quite meet the threshold for validation but also were not clear false
positives. These 33 targets were deemed to be marginal validations and had at
least one or both FPP values from vespa and TRICERATOPS that met the marginal
validation criteria described in Section 5. This category was further
subdivided into “likely planets” (LPs) and “potential false positives” (pFPs).
LPs were targets with either both FPP values residing in the marginal
validation zone or one FPP in the marginal validation zone and the other
meeting the threshold for validation. pFPs were targets with one marginal
validation FPP and one FPP that indicates a false positive.

The results of our vetting analysis agree with these distinctions based on
FPP. Almost all of the targets in the pFP category had at least one vetting
factor that could indicate a false positive origin (e.g. V-shaped transit,
possible odd-even transit depth differences, etc.) but are not definitive
enough to warrant labeling the target a likely false positive. There were a
total of 15 targets in the LP category and 18 in the pFP category of marginal
validations. We encourage future study and follow-up of these targets to
ascertain their true nature as they could potentially be prime targets for
atmospheric characterization with JWST. Examples of transits from each
disposition category are shown in Figure 6.

The remaining 4 targets produced inconclusive vetting and validation results.
This category is distinct from marginal validations in that in most of these
inconclusive cases vespa and TRICERATOPS disagree significantly on the status
of each target or there are additional factors precluding an adequate vetting
or validation analysis. The targets TOI-1355.01, TOI-1954.01, and TOI-4552.01
were validated by one statistical validation software while the other software
produced an FPP that did not meet the threshold for even a marginal
validation.

In the case of TOI-1355.01, the discrepant FPP may be due to overly
constraining photometric follow-up observations. The transit shape is slightly
V-shaped, and our follow-up observations rule out a large portion of parameter
EB and BEB parameter space, but those are the models that fit the phase-folded
transit the best (resulting in FPP values with large uncertainties from
TRICERATOPS). This target may be a grazing planet, which would explain the
V-shape as well as the small parameter space for EBs and BEBs.

In the case of TOI-1954.01, very little follow-up exists and the target is in
a crowded field, both of which likely combine to cause the discrepancy between
vespa and TRICERATOPS. For TOI-4552.01, the signal is shallow and the light
curve exhibits some variability which is likely causing variability in the FPP
values calculated by the different validation software packages.

The final inconclusive case is TOI-4597.01. This target was statistically
validated by vespa but TRICERATOPS was unable to run on it. This is likely due
to the short periodic oscillations that appear in the light curve as a result
of stellar activity or variability. A clear transit exists, but we are unable
to complete our vetting and validation analysis without properly modeling the
variability in the light curve to produce a clean transit. This is beyond the
scope of this work as it would require a physically-motivated model to
subtract from the light curve that our vetting and validation procedure is
incapable of. We encourage follow-up analysis of these four inconclusive
targets to determine their true nature.

Figure 7: Our best-in-class targets for transmission (top) and emission
(bottom) spectroscopy after performing our vetting and validation analysis on
the sample. Similar to Figure 2, target names are displayed in the cell
corresponding to the parameter space they occupy next to their TSM or ESM
value in brackets with approximate host stellar type denoted by the colored
circle, as determined by reported effective temperature. Each target’s
background color corresponds to its mass measurement and validation status:
green targets are confirmed planets with mass measurements $>$5$\sigma$,
yellow targets are confirmed planets with mass measurements $<$5$\sigma$ and
TOI that were independently confirmed over the course of our analysis, blue
targets have been statistically validated by our analysis, orange targets are
marginal validations (LPs and pFPs), red targets were deemed likely false
positives (LFPs) by our analysis, and gray targets were deemed to have an
inconclusive validation. Targets with an asterisk next to their name have an
ambiguous period and their TSM values are liable to change as their
$T_{\rm{eq}}$ values were calculated on the reported periods not the true
periods.

There were an additional 14 targets from our samples that were confirmed by
independent teams over the course of our analysis. These targets are TOI-179 b
(Desidera et al., 2022; de Leon et al., 2023), TOI-622 b (Psaridi et al.,
2023), TOI-836 b (Hawthorn et al., 2023), TOI-969 b (Lillo-Box et al., 2023),
TOI-1099 b (Barros et al., 2023), TOI-1468 b $\&$ c (Chaturvedi et al., 2022),
TOI-1853 b (Naponiello & et al., accepted), TOI-3235 b (Hobson et al., 2023),
TOI-3884 b (Almenara et al., 2022), TOI-4463 A b (Yee et al., 2023), GJ 806 b
(TOI-4481 b; Palle et al., 2023), HD 20329 b (TOI-4524 b Murgas et al., 2022),
and TOI-4860 b (Almenara et al., 2023; Triaud et al., 2023). This high number
of targets in our best-in-class sample being confirmed in such a short period
of time is very positive for the prospects for atmospheric characterization
with JWST. Indeed, the goal of our synthesis of the best-in-class sample is to
highlight and elevate targets potentially well-suited to such observations for
follow-up to measure their masses and confirm their planetary nature.

For the targets that had masses measured independently over the course of our
analysis, we recalculated their TSM and ESM values according to their updated
planet parameters and reranked them within their respective bins. In the cases
of TOI-179 b, TOI-836 b, TOI-969 b, TOI-1099 b, TOI-1853 b, TOI-3235 b,
TOI-4463 A b, and TOI-4860 b, the updated parameters differed significantly
from those originally listed on the NASA Exoplanet Archive, resulting in TSM
and ESM values much lower than calculated and warranting removal from either
the best-in-class transmission or emission spectroscopy samples. In the cases
of TOI-836 b, TOI-969 b, TOI-1099 b, TOI-1853 b, and TOI-4463 A b, this
amounted to removal from the entire best-in-class sample.

Our best-in-class sample also includes six targets with ambiguous periods –
TOI-706.01, TOI-1856.01, TOI-1895.01, TOI-2299.01, TOI-4317.01, TOI-5575.01,
and TOI-5746.01. These targets were originally discovered as single transits
before transiting again in later sectors of TESS data. The periods reported on
ExoFOP represent the upper limit on their periods since additional transits of
these targets could have fallen in gaps in the TESS data and their true
periods may be shorter. We performed our vetting and validation using the
stated periods but knowing that future observations could reveal shorter
periods that would alter the TSM and ESM values as well as their observability
with JWST. We choose not to discard these targets from our best-in-class
samples to emphasize their potential as ideal JWST targets and emphasize the
need for additional follow-up on them. Only TOI-4317.01 had low enough FPP
values to be considered statistically validated, but due to its ambiguous
period, we place it in the “likely planet” category and we caution that a
deeper analysis is required for this target to identify the true period and
therefore its true planetary status. We note that the planets with long or
ambiguous periods in our sample should have their orbital periods further
scrutinized as the TESS observing strategy makes it difficult to determine
such long orbital periods and they may change depending on individual
circumstances.

The final best-in-class sample is displayed in Figure 7, which mirrors Figure
2 but now includes updated dispositions for all targets in our samples, both
confirmed and unconfirmed. Additional information on each target can be found
in Table 2 in Appendix A. An extended machine-readable version of this table
is also available in the online version of this article.

There were a number of targets in our best-in-class sample that are also in
the process of being validated by independent teams. These include TOI-4226.01
(Timmermans, in prep.) and TOI-4317.01 (Osborn et al., in prep.). We direct
the reader to these upcoming publications for a more in-depth analysis and
exploration than is available here and to treat such in-depth analyses as the
definitive discovery papers for these individual targets. Dressing et al. (in
prep.) is also conducting a parallel large-scale validation effort on TOIs
261.01, 4317.01, 4527.01, 4602.01, and 5082.01, as is Mistry (in prep.) for
TOIs 238.01 and 771.01 and we direct the reader to this upcoming publication
for an additional, independent vetting and validation of these targets.
Additionally, independent teams are conducting confirmation and
characterization of TOI-1410.01 (Livingston, in prep.), TOIs 1194.01, 1347.01,
and 1410.01 (Polanski, in prep.), and TOI-880.02 (Nielsen, in prep.) and we
direct the reader to these papers for an in-depth analysis of these targets.

##  7 Summary and Conclusion

In this paper, we present a set of best-in-class planets for atmospheric
characterization with JWST through both transmission and emission
spectroscopy. Our vetting, validation, and results are summarized here:

  * •

We queried the NASA Exoplanet Archive for all transiting confirmed planets and
unconfirmed TESS candidates and calculated their TSM, ESM, and observability
with JWST.

  * •

We divided all planets into grids with bins in equilibrium temperature,
$T_{\rm eq}$, from 100 to 3000 K and planetary radius, $R_{\rm p}$, from 0.3
to 25.0 R⊕ and the top five planets and candidates were ranked by spectroscopy
metric in each bin to create a best-in-class sample for each spectroscopy
method.

  * •

The 103 unconfirmed TESS-discovered candidates from the transmission and
emission spectroscopy grids were vetted using a combination of follow-up
observations collected by TFOP and independent analyses such as the SPOC DV
reports.

  * •

We used vespa and TRICERATOPS to calculate the false positive probabilities
and determine a final disposition for each target.

  * •

Our analysis resulted in 23 validated targets, 29 likely false positives, 33
targets that were marginally validated, and 4 inconclusive validations. Of our
original targets, 14 were independently confirmed over the course of our
analysis.

  * •

This final sample represents the best-in-class targets for atmospheric
characterization with JWST and deeper analysis on each target is highly
encouraged.

The best-in-class sample presented in this paper is meant to represent an
initial look at many of the targets with the potential to yield high quality
spectra from JWST. We hope that this work paves the way for future studies of
a similar sort. We highly encourage independent analysis of each target
presented here to discern the true nature of each and build a catalog of
planets that can reliably provide exquisite atmospheric data from JWST.

We recognize that this best-in-class sample will undoubtedly change over time
as new targets supplant previous ones in the JWST observability rankings, as
targets are shown to be false positives, or as the orbital and planetary
parameters of targets are refined with further observation. Since the date on
which we queried the NASA Exoplanet Archive to generate our sample, $\sim$750
new TESS candidates have been discovered. It is possible that anywhere from
5-10 of these new discoveries could belong possess TSM or ESM values that
place them within the best-in-class sample. These targets primarily appear in
the bins containing the largest planets and the hottest planets. As TESS
probes fainter stars, new detections are even more biased towards large, hot
planets as they possess a sufficient signal-to-noise to be detected around
faint stars.

This sample may also change based on the assumptions used to generate it. Our
analysis calculated the ESM value for planets in all portions of parameter
space even though it was originally developed by Kempton et al. (2018) for