# The TESS Objects of Interest Catalog from the TESS Prime Mission

Natalia M. Guerrero Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  S. Seager Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Department of Aeronautics and Astronautics, MIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Chelsea X. Huang Juan Carlos Torres Fellow  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Andrew Vanderburg NASA Sagan Fellow  Department of Astronomy, The University of Wisconsin-Madison, Madison, WI 53706, USA  Department of Astronomy, The University of Texas at Austin, Austin, TX 78712, USA  Aylin Garcia Soto Department of Physics and Astronomy, Dartmouth College Hanover, NH 03755  Ismael Mireles Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Katharine Hesse Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  William Fong Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Ana Glidden Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Avi Shporer Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  David W. Latham Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Karen A. Collins Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Samuel N. Quinn Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Jennifer Burt Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA  Diana Dragomir Department of Physics and Astronomy, University of New Mexico, 1919 Lomas Blvd NE, Albuquerque, NM 87131, USA  Ian Crossfield  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Department of Physics & Astronomy, 1082 Malott,1251 Wescoe Hall Dr., Lawrence, KS 66045  Roland Vanderspek Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Michael Fausnaugh Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Christopher J. Burke Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  George Ricker  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Tansu Daylan Kavli Fellow  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Zahra Essack Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Maximilian N. Günther Juan Carlos Torres Fellow  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Hugh P. Osborn NCCR/PlanetS, Centre for Space & Habitability, University of Bern, Bern, Switzerland  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Joshua Pepper Department of Physics, Lehigh University, 16 Memorial Drive East, Bethlehem, PA 18015, USA  Pamela Rowden School of Physical Sciences, The Open University, Milton Keynes MK7 6AA, UK  Lizhou Sha Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Steven Villanueva Jr. Pappalardo Fellow  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Daniel A. Yahalomi Department of Astronomy, Columbia University, 550 W 120th St., New York NY 10027, USA  Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Liang Yu Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Sarah Ballard Bryant Space Science Center, Department of Astronomy, University of Florida, Gainesville, FL 32611, USA  Natalie M. Batalha Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95060, USA  David Berardo Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Ashley Chontos NSF Graduate Research Fellow  Institute for Astronomy, University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA  Jason A. Dittmann 51 Pegasi b Fellow  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Gilbert A. Esquerdo Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Thomas Mikal-Evans Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Rahul Jayaraman Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Akshata Krishnamurthy Department of Aeronautics and Astronautics, MIT, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Dana R. Louie Department of Astronomy, University of Maryland, College Park, MD 20742, USA  Nicholas Mehrle Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Prajwal Niraula Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Benjamin V. Rackham 51 Pegasi b Fellow  Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Joseph E. Rodriguez Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Stephen J. L. Rowden Department of Biochemistry, Hopkins Building, Downing Site, Tennis Court Road, University of Cambridge, Cambridge, CB2 1QW, UK  Clara Sousa-Silva 51 Pegasi b Fellow  Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  David Watanabe Planetary Discoveries, Fredericksburg, VA 22405, USA  Ian Wong 51 Pegasi b Fellow  Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Zhuchang Zhan Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Goran Zivanovic Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Jessie L. Christiansen Caltech/IPAC, 1200 E. California Blvd. Pasadena, CA 91125  David R. Ciardi Caltech/IPAC, 1200 E. California Blvd. Pasadena, CA 91125  Melanie A. Swain Caltech/IPAC, 1200 E. California Blvd. Pasadena, CA 91125  Michael B. Lund Caltech/IPAC, 1200 E. California Blvd. Pasadena, CA 91125  Susan E. Mullally Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Scott W. Fleming Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  David R. Rodriguez Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA  Patricia T. Boyd NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA  Elisa V. Quintana NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA  Thomas Barclay NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA  University of Maryland, Baltimore County, 1000 Hilltop Cir, Baltimore, MD 21250, USA  Knicole D. Colón NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA  S. A. Rinehart NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA  Joshua E. Schlieder NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA  Mark Clampin NASA’s Goddard Space Flight Center, Greenbelt, MD 20771, USA  Jon M. Jenkins NASA Ames Research Center, Moffett Field, CA 94035, USA  Joseph D. Twicken SETI Institute, Mountain View, CA 94043, USA  NASA Ames Research Center, Moffett Field, CA 94035, USA  Douglas A. Caldwell  SETI Institute, Mountain View, CA 94043, USA  NASA Ames Research Center, Moffett Field, CA 94035, USA  Jeffrey L. Coughlin SETI Institute, Mountain View, CA 94043, USA  NASA Ames Research Center, Moffett Field, CA 94035, USA  Chris Henze  NASA Ames Research Center, Moffett Field, CA 94035, USA  Jack J. Lissauer NASA Ames Research Center, Moffett Field, CA 94035, USA  Robert L. Morris SETI Institute, Mountain View, CA 94043, USA  NASA Ames Research Center, Moffett Field, CA 94035, USA  Mark E. Rose NASA Ames Research Center, Moffett Field, CA 94035, USA  Jeffrey C. Smith SETI Institute, Mountain View, CA 94043, USA  NASA Ames Research Center, Moffett Field, CA 94035, USA  Peter Tenenbaum  SETI Institute, Mountain View, CA 94043, USA  NASA Ames Research Center, Moffett Field, CA 94035, USA  Eric B. Ting NASA Ames Research Center, Moffett Field, CA 94035, USA  Bill Wohler SETI Institute, Mountain View, CA 94043, USA  NASA Ames Research Center, Moffett Field, CA 94035, USA  G. Á. Bakos Department of Astrophysical Sciences, Princeton University, NJ 08544, USA  Jacob L. Bean Department of Astronomy & Astrophysics, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637, USA  Zachory K. Berta-Thompson Department of Astrophysical and Planetary Sciences, University of Colorado, Boulder, CO 80309, USA  Allyson Bieryla Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Luke G. Bouma Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544, USA  Lars A. Buchhave DTU Space, National Space Institute, Technical University of Denmark, Elektrovej 328, DK-2800 Kgs. Lyngby, Denmark  Nathaniel Butler  School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287, USA  David Charbonneau Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  John P. Doty Noqsi Aerospace Ltd, 15 Blanchard Avenue, Billerica MA, 01821  Jian Ge  Bryant Space Science Center, Department of Astronomy, University of Florida, Gainesville, FL 32611, USA  Matthew J. Holman Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Andrew W. Howard California Institute of Technology, Pasadena, CA 91125, USA  Lisa Kaltenegger Carl Sagan Institute, Cornell University, 302 Space Science Building, Ithaca, NY 14850, USA  Stephen R. Kane Department of Earth and Planetary Sciences, University of California, Riverside, CA 92521, USA  Hans Kjeldsen Stellar Astrophysics Centre, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, 8000 Aarhus C, Denmark  Laura Kreidberg Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany  Douglas N.C. Lin Department of Astronomy & Astrophysics, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA  Charlotte Minsky Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA  Norio Narita Astrobiology Center, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  JST, PRESTO, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan  Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, Tenerife, Spain  Martin Paegert Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  András Pál Konkoly Observatory, Research Centre for Astronomy and Earth Sciences, Konkoly-Thege Miklós út 15-17, 1121 Budapest, Hungary  Eötvös Loránd University, Department of Astronomy, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary  ELTE Eötvös Loránd University, Institute of Physics, Pázmány Péter sétány 1/A, 1117 Budapest, Hungary  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Enric Palle Instituto de Astrofísica de Canarias (IAC), 38205 La Laguna, Tenerife, Spain  Dimitar D. Sasselov Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Alton Spencer Western Connecticut State University, Danbury, CT, 06811  Alessandro Sozzetti INAF - Osservatorio Astrofisico di Torino, Via Osservatorio 20, 10025 Pino Torinese, Italy  Keivan G. Stassun Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37235, USA  Department of Physics, Fisk University, Nashville, TN 37208, USA  Guillermo Torres Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Stephane Udry Geneva Observatory, University of Geneva, 51 ch des Maillettes, 1290 Vwesoix, Switzerland  Joshua N. Winn Department of Astrophysical Sciences, Princeton University, 4 Ivy Lane, Princeton, NJ 08544, USA

(Accepted Feb. 17, 2021)

###### Abstract

We present 2241 exoplanet candidates identified with data from the Transiting
Exoplanet Survey Satellite (TESS) during its two-year prime mission. We list
these candidates in the TESS Objects of Interest (TOI) Catalog, which includes
both new planet candidates found by TESS and previously-known planets
recovered by TESS observations. We describe the process used to identify TOIs
and investigate the characteristics of the new planet candidates, and discuss
some notable TESS planet discoveries. The TOI Catalog includes an
unprecedented number of small planet candidates around nearby bright stars,
which are well-suited for detailed follow-up observations. The TESS data
products for the Prime Mission (Sectors 1–26), including the TOI Catalog,
light curves, full-frame images, and target pixel files, are publicly
available on the Mikulski Archive for Space Telescopes.

catalogs, exoplanets, transit method

††journal: ApJS††software: QLP (Huang et al., 2020a, b), SPOC, (Jenkins et
al., 2020; Twicken et al., 2016), tica (Fausnaugh et al. (in prep.)), FITSH
(Pál, 2012), astronet (Yu et al., 2019), TEC, TEV, batman (Kreidberg, 2015),
nebuliser (Irwin, 1985), emcee (Foreman-Mackey et al., 2013), vartools
(Hartman & Bakos, 2016), go (Meyerson, 2014), TensorFlow (Abadi et al., 2015),
h5py (Collette, 2013), Astropy (Astropy Collaboration et al., 2013; Price-
Whelan et al., 2018), astroquery (Ginsburg et al., 2019), matplotlib (Hunter,
2007), pandas (pandas development team, 2020), Scipy (Virtanen et al., 2020),
Numpy (Oliphant, 2006–)

##  1 Introduction

The Transiting Exoplanet Survey Satellite (TESS; Ricker et al. 2015) is an
MIT-led NASA Astrophysics Explorer Mission designed to detect transiting
exoplanets around the nearest, brightest stars. During its two-year Prime
Mission111TESS launched on April 18, 2018 and the TESS Prime Mission ran from
July 25, 2018 - July 4, 2020., TESS observed $\sim 70\%$ of the celestial
sphere in 26 observing “sectors,” resulting in observing times ranging from
$\sim$1 month near the ecliptic to $\sim$1 year near the ecliptic poles.

The primary goal of the TESS mission is to identify hundreds of small planets
($R_{p}<4R_{\earth}$) and determine the planetary mass for 50 of these small
planets with follow-up spectroscopy. Simulations of the Prime Mission
(Sullivan et al., 2015; Huang et al., 2018a; Barclay et al., 2018; Barclay,
2020) predict how many planets TESS will detect for a range of periods up to
100 days. These simulations predict that TESS will find thousands of planets
with periods less than 50 days.

TESS builds on NASA’s Kepler (Borucki et al., 2010) and repurposed K2 missions
(Howell et al., 2014). Kepler and K2 discovered nearly 3,000 confirmed
exoplanets with thousands more awaiting confirmation or validation222NASA
Exoplanet Archive, accessed 14 August 2020. While the original Kepler mission
stared deeply into a single field of 116 square degrees to produce a
statistical sample of exoplanets333Kepler targets were typically 700-1100 pc
distant (Berger et al., 2018) in the observation “cone,” TESS is conducting a
survey of nearby, bright stars. These targets are the most accessible to both
ground and space-based follow-up, necessary for measuring planet mass and
density (i.e. Huang et al., 2018b; Dragomir et al., 2019), as well as
characterizing atmospheres. Select TESS-discovered planets will likely be good
targets for observing with the upcoming James Webb Space Telescope (JWST) for
atmospheric characterization. Some TESS targets are already being
characterized by the Hubble Space Telescope (HST), with 12 HST programs
awarded observing time for this purpose to date, e.g. TOI-1231 b (Kreidberg et
al., 2020), AU Mic b (Newton et al., 2019a; Cauley et al., 2020), and many
others.

By reaching down to small planets transiting bright stars that are suitable
for follow-up measurements, TESS is bridging one of the gaps in exoplanet
science. The wide, red bandpass of the TESS cameras (600-1000 nm) makes TESS
capable of detecting Earth-sized and super-Earth-sized exoplanets ($\lesssim
1.75R_{\earth}$) transiting M-dwarf stars, which are significantly smaller and
cooler than our Sun. M-dwarfs make up the majority (around $75\%$) of stars in
our solar neighborhood444http://www.recons.org/census.posted.htm (Henry et
al., 2018; Dole, 1964). For FGK stars, TESS can find planets down to about
$R_{p}=0.8{R_{\oplus}}$, depending on the planet period and star brightness.

During the Prime Mission, two basic types of data were collected: 1) small
summed image subarrays (“postage stamps”) centered on 20,000 pre-selected
targets every two minutes and 2) summed full-frame images (FFIs), measuring
$24^{\circ}\times 24^{\circ}$, collected from each of the four TESS cameras
every thirty minutes. During a typical observing sector, which lasts $\sim$26
days, $\sim$19,000 sets of postage stamps and $\sim$ 1,200 sets of four FFIs
are collected. These data are processed into calibrated light curves by two
data processing pipelines described in Section 4: the Science Processing
Operations Center (SPOC) pipeline for the postage stamps, and the Quick Look
Pipeline (QLP) for the FFIs. These pipelines identify potential transiting
planets by searching for periodic flux decreases, known as Threshold Crossing
Events (TCEs), in both the short (two-minute) and long (30-minute) cadence
data.

The TESS Science Office (TSO) examines TCEs using the TESS light curve and
other information to identify planet candidates which would benefit from
follow-up observation. The light curves are first run through software that
eliminates obvious non-planetary signals; the remaining light curves are
manually vetted to identify a set of TESS Objects of Interest, or TOIs.
Threshold-crossing events (TCEs) which fall under other categories (such as
stellar eclipsing binaries, variable stars, instrument systematics, and non-
planet transients) are not included in the TOI Catalog, but are included in
the comprehensive TCE Catalog, to be archived at the Mikulski Archive for
Space Telescopes (MAST). Follow-up observations, both imaging and radial-
velocity, are then used to confirm the planet identification and measure
planetary parameters.

Over the course of the Prime Mission, the SPOC pipeline has produced flux time
series (i.e. light curves) for $>200,000$ stellar objects at a two-minute
cadence. The QLP has produced light curves for $\sim$16 million stars brighter
than TESS-band magnitude, $T_{\rm mag}$$=$13.5, observed in the Full-Frame
Images (FFIs) at 30-minute cadence. Additionally, the community produces
millions of time series (Bouma et al., 2019; Caldwell et al., 2020; Feinstein
et al., 2019; Lund et al., 2015, 2017; Montalto et al., 2020; Nardiello et
al., 2019; Oelkers & Stassun, 2019) from the FFIs which are not evaluated in
this work.

In the Prime Mission, the TSO examined $55,281$ TCEs from $27,822$ two-minute
targets, resulting in $17,296$ candidates for manual vetting. The TSO examined
$\sim 105,400$ TCEs from $82,490$ unique FFI targets, resulting in $14,629$
candidates for manual vetting. Taking into account the overlap in the data
sets, the total number of TCEs subject to manual vetting was 32,814 TCEs on
16,489 unique targets. In total, data collected during the Prime Mission
yielded 2241 TOIs; of the 1676 TOIs not later ruled out as False Positives,
1575 TOIs had periods less than 50 days, and 654 TOIs had radii
$R_{p}<4{R_{\oplus}}$.

In this work, we first give an overview of TESS’s observing strategy (Section
2). Then, we discuss TESS’s data collection and processing (Section 3).
Additional details about the TESS instrument, observing strategy, and data
processing are described in greater detail in the TESS Instrument Handbook
(Vanderspek et al., 2018), data release
notes555https://archive.stsci.edu/tess/tess_drn.html, and associated
references. We also provide a brief description of light curve generation
(Section 4) and the TCE search process (Section 5). The initial stages of the
TOI triage and vetting process are discussed in Section 6 and Section 7. The
process by which TOIs are delivered to the community is described in Section
8. The TOI Catalog is described in Section 9. Lastly, we turn to a discussion
of the TESS mission planet candidates found to date, highlighting systems of
particular scientific interest (Section 10).

##  2 Observing Strategy

TESS is equipped with four wide-field cameras, each with a field-of-view (FOV)
of $24^{\circ}\times 24^{\circ}$ and a pixel angular size of
$21^{\prime\prime}$ square. The four FOVs are arranged to cover a
$24^{\circ}\times 96^{\circ}$ region of the sky, the long axis of which is
oriented parallel to a line of ecliptic longitude. TESS observes each
$24^{\circ}\times 96^{\circ}$ field over the course of two orbits (about 27.4
days). Each $\sim 27$-day observation period is called a sector.

Figure 1: The TESS two-year target observation map. Each point in this map
represents a target observed at two-minute cadence. The color of the dot
represents the number of times it was observed. The U-shaped red curve shows
the galactic plane, with the position of the galactic center shown by the red
star. Over the course of its two-year primary mission, TESS observed 26
sectors for approximately 27 days each, covering $\sim 70\%$ of the sky. Each
sector is a $24^{\circ}\times 96^{\circ}$ field of view. The TESS “continuous
viewing zones” ($\sim 351$ days of observation) at each ecliptic pole are
clearly visible, as are the regions of the sky observed during more than one
sector. In Year 2, during Sectors 14-16 and 24-26, the camera boresight was
shifted from the nominal $+54^{\circ}$ orientation northward to an
$+85^{\circ}$ ecliptic latitude, due to excessive contamination by stray
Earth-light and moonlight in cameras 1 and 2 in those sectors. These gaps will
be filled in part during the first Extended Mission.

The instrument FOV is generally centered on an ecliptic latitude of
$-54^{\circ}$ (Year 1) or $+54^{\circ}$ (Year 2), with six sectors in Year 2
centered at $+85^{\circ}$ (see Figure 1). The ecliptic longitude of the FOV is
adjusted by $\sim 27^{\circ}$ from one sector to the next, which results $\sim
27$ days of observation over a large range of ecliptic longitude and an
overlap region within $12^{\circ}$ of the ecliptic poles that is observed for
an entire year.

Figure 1 shows the two-year sky coverage map for TESS. TESS’s Prime Mission
covered 13 sectors in the Southern Ecliptic Hemisphere during the first year
and 13 sectors in the Northern Ecliptic Hemisphere during the second year. The
observing period is longest for targets near the ecliptic poles, which were
each observed for all 13 sectors.

TESS collects an FFI from each camera every 30 minutes over the course of each
observation sector. In addition, a set of stars is observed every two minutes
in smaller “postage stamps”. A postage stamp typically measures $11\times 11$
pixels, but is expanded for brighter stars. These two-minute cadence “target
stars” are chosen from the TESS Input Catalog (TIC, Stassun et al., 2018,
2019) based on the Candidate Target Lists (CTLs) contributed by mission
stakeholders (Vanderspek et al., 2018). In order of priority, the categories
are 1,920 engineering stars (Photometer Performance Assessment, PPA); all
bright ($I<6$) stars; additional potential exoplanet host stars; asteroseismic
targets; Guest Investigator targets; and Director’s Discretionary
Targets666Full details of how the targets are selected can be found in the
instrument handbook (§9.4, Vanderspek et al. 2018). In total, up to 20,000
stars from these categories are observed at two-minute cadence in each
sector.777Initially, the target list for two-minute cadence data was limited
to the requirement-specified 16,000 target stars. However, after the first
three sectors, the Science Operations Center (SOC) team expanded the target
list to 20,000 targets beginning with Sector 4, based on tests of the
spacecraft compression and pipeline throughput.

##  3 Data Collection and Processing

TESS is in a unique 13.7-day, highly-inclined, highly-elliptical orbit in
resonance with the Moon: an observation sector consist of two TESS orbits
around the Earth. Science data, in the form of 30-minute FFIs and two-minute
postage stamps, are collected continuously during each orbit. At orbit
perigee, the data are downlinked from the spacecraft to the Deep Space Network
(DSN) and forwarded to the Payload Operations Center (POC) at MIT for
decompression and reformatting.

POC processing reformats the uncompressed data for use by two independent data
processing pipelines: the Science Processing Operations Center (SPOC) pipeline
and the Quick Look Pipeline (QLP). These pipelines each calibrate the pixel
data and use them to produce light curves. In Section 4, we provide a brief
overview of the pipelines.

In a typical sector, TESS downlinks roughly 40 GB of compressed target data
and 54 GB of compressed FFI data through the DSN. The postage stamp data
products per sector typically expand to 380 GB of target pixel files, 290 GB
of collateral pixels (used for calibration), and 18 GB of light curve files
and various other data products. The FFI data per sector typically expand to
370 GB of raw FFIs and 710 GB of SPOC-calibrated data. MAST archives the FFI
data and target pixel data as well as the ancillary data products SPOC
creates. The full collection of full frame images used to search for TOIs from
the Prime Mission are available at MAST via10.17909/t9-17nt-6c71 (catalog
10.17909/t9-17nt-6c71) for Sectors 1-13 and10.17909/t9-t6cs-8p81 (catalog
10.17909/t9-t6cs-8p81) for Sectors 14-26. The full collection of two-minute
cadence data used to search for TOIs from the Prime Mission are available at
MAST via10.17909/t9-rm11-zz03 (catalog 10.17909/t9-rm11-zz03) for Sectors 1-13
and10.17909/t9-p87b-kk02 (catalog 10.17909/t9-p87b-kk02) for Sectors 14-26.

##  4 Light Curve Generation

The SPOC pipeline converts the postage stamp data into two-minute cadence
light curves, while the QLP produces light curves at 30-minute cadence from
the FFIs. Both pipelines are described in the following sections. Additional
details can be found in Jenkins et al. (2020); Twicken et al. (2016), and
Jenkins et al. (in prep.) for the SPOC pipeline, Huang et al. (2020a, b) for
the QLP.

###  4.1 SPOC Light Curve Generation

The SPOC pipeline (Jenkins et al. (2020); Twicken et al. (2016) and Jenkins et
al. (in prep.)) processes the two-minute postage stamp data on a per-sector
basis. The pipeline calibrates the pixel data; extracts photometry and
centroids for each target star; and identifies and removes instrumental
signatures. The SPOC pipeline then searches each light curve for TCEs, fits
each TCE with a limb-darkened transit light curve model, and performs
diagnostic tests to assess the planetary nature of each TCE (Jenkins et al.,
2016). The SPOC pipeline also calibrates the FFIs at the pixel level.

####  4.1.1 Calibration

The SPOC pipeline calibration module (CAL) operates first on both the postage
stamp and FFI data to transform the digital counts from the CCDs into flux
units ($e^{-}s^{-1}$, Clarke et al., 2020).

The sequence of operations is as follows: 1) the module subtracts 2-D fixed
pattern noise (2-D black model); 2) estimates and removes the CCD bias voltage
(or black level) in each readout row; 3) corrects for non-linearity and gain;
4) measures and removes vertical smear flux due to the shutterless operation
of the CCDs, along with the dark current from each column; and finally, 5)
corrects for the flat field. Note that the non-linearity, gain, and flat field
are pre-flight, on-the-ground measurements. Uncertainties are propagated
through each numerical operation and provided with the calibrated pixel
values.

####  4.1.2 Photometry

The pipeline module, Compute Optimal Apertures (COA), identifies the optimal
aperture for each two-minute target (Bryson, 2008; Bryson et al., 2010, 2020).
The module uses the TIC, models for the instrument, such as the Pixel Response
Function (PRF), and observations to generate synthetic star scenes for each
sector. The star scenes predict the average flux from each star in each pixel
of the target star’s postage stamp. For sectors 1-13, the pipeline used
stellar parameters from TICv7 (Stassun et al., 2018). For sectors 14 onward,
the pipeline used parameters from TICv8 (Stassun et al., 2019). The module
analyzes the stellar scene to identify the aperture that maximizes the SNR of
the flux measurement. Additionally, the module estimates the contamination due
to nearby stars in the apertures, and the fraction of the flux from the target
star contained in the optimal aperture.

The aperture size is magnitude-dependent. For bright, saturated targets the
photometric apertures can be quite large ( 2500 pixels). For stars that are
not saturated, typical numbers of optimal aperture pixels in Year 1 range from
20 pixels at $T_{\rm mag}$$\,\approx\,$7, to 11 pixels at $T_{\rm
mag}$$\,\approx\,$10, to 6 pixels at $T_{\rm mag}$$\,\approx\,$13\. The
photometric apertures for target stars with ${T_{\rm mag}}<11$ were somewhat
larger in Year 2 to reduce light curve variations due to pointing jitter. The
crowding in each photometric aperture is estimated from the TIC magnitudes and
(proper-motion-corrected) coordinates of nearby stars and a Pixel Response
Function that is interpolated at the location of the given target. Flux levels
are adjusted for crowding in the pre-search data conditioning (PDC) light
curves. The CROWDSAP keyword in the FITS files reports the average fraction of
the flux in the photometric aperture that is from the target star. As CROWDSAP
decreases, the photometry becomes less reliable, especially for values
$\lesssim$ 0.8. The FLFRCSAP keyword reports the average fraction of the flux
of the target star captured in the photometric aperture.

The photometric analysis module (PA) identifies background pixels, then
estimates and subtracts a local background from each target star postage
stamp.888This is a new feature of the module for TESS as the postage stamps
are typically 121 or more pixels (11 by 11), compared to Kepler’s nominal 32
pixels per postage stamp, which were too small to allow for local background
estimation. The module then sums the pixels in the optimal aperture to
estimate the brightness of each target star in each frame (Twicken et al.,
2010; Morris et al., 2020). The module also extracts brightness-weighted
centroids for each target star in each frame. A subset of 120 bright,
unsaturated stars are fitted to the local PRF to extract high-precision
centroids that are used to calculate the World Coordinate System (WCS)
coefficients for all targets and for the FFIs. Uncertainties are propagated
through the numerical operations and reported with the flux and centroid
measurements.

The presearch data conditioning module (PDC) (Stumpe et al., 2012; Smith et
al., 2012; Stumpe et al., 2014) identifies and corrects for instrumental
effects that are highly correlated across the star fields on each CCD. The
module first conducts a singular value decomposition (SVD) analysis of the
quietest half of the target stars on each CCD to identify the correlated
instrumental signatures. The module then fits the most significant
eigenvectors to the quiet stars’ light curves and formulates empirical prior
probability density distributions for the fit coefficients. These
distributions provide constraints in a Maximum A Posteriori (MAP) fit for the
flux time series for each target star. The module also identifies and records
outliers, corrects for the finite flux fraction of the target star’s PRF, and
corrects for the crowding contamination from nearby stars. The SPOC pipeline
propagates uncertainties for the photometry and centroids. The uncertainties
for the flux time series are dominated by shot noise from the brightest
targets at ${T_{\rm mag}}\ \approx 2$, where the ratio of the flux uncertainty
to the star’s shot noise is $\sim$1.05, out to approximately $T_{\rm mag}$ =
9, where the flux uncertainty is 25% above that expected for shot noise from
the star. The ratio of the uncertainties to the shot noise rises to 1.78 at
$T_{\rm mag}$ = 12, and attains a value of $\sim$4 at $T_{\rm mag}$ = 15. The
uncertainties do not account for image motion on timescales less than 2
minutes, or for stellar variability.

The uncertainties in the flux-weighted centroids (which are calculated for all
stars) vary as a function of magnitude, with typical values of $1\times
10^{-4}$ pixels at $T_{\rm mag}$ = 6, $5\times 10^{-4}$ pixels at $T_{\rm
mag}$ = 9, and $2.7\times 10^{-3}$ pixels at $T_{\rm mag}$ = 12. The
uncertainties for the row centroids of stars brighter than $T_{\rm mag}$ = 6
grow with brightness due to saturation and bleed. The uncertainties do not
account for short timescale pointing errors, however, and the typical short
timescale scatter at the 2-min level is $\sim 5\times 10^{-3}$ pixels after
modeling out the pointing history.

The SPOC pipeline has evolved over the course of Year 1 to adapt to changes in
the instrument and target list. In Sectors 1-3, the spacecraft jitter impacted
aperture photometry, but could be largely corrected using cotrending999The
pointing errors for TESS are not stationary and exhibit sporadic excursions on
timescales $\ll$2 min that change the effective point spread function in each
two-minute interval, making it difficult to fully correct such behavior on
two-minute integrations.. An on-flight software patch reduced the spacecraft
jitter for Sector 4 onward (Vanderspek et al., 2018). However, systematic
noise from spacecraft jitter continues to be a source of systematic error
removed by PDC, along with rapidly varying scattered light features that occur
when the Earth or Moon are within 25∘ of the boresight of any camera. The
updated SPOC codebase, R4.0, processed Year 2 Sectors 20-26 and reprocessed
the Year 1 data and Year 2 Sectors 14-19. The update automatically flags
scattered light features at the target star/cadence level, which improves the
specificity of the data gapping and preserves more data.

###  4.2 QLP Light Curve Generation

The Quick Look Pipeline (QLP; Huang et al. (2020a, b)) generates light curves
from the FFIs, complementary to the SPOC pipeline which produces light curves
from the postage stamp data. The QLP separately performs its own calibration
of the raw FFIs. For each sector, the QLP produces about half a million light
curves. In addition, the QLP always uses all sectors of available data to
create light curves (i.e. the QLP runs over multiple sectors of data
together).

####  4.2.1 Calibration

The QLP corrects the raw FFIs for amplifier bias, smear caused by shutter-less
frame-transfer exposure, flat-fields, fixed-pattern noise, and non-linear
response. Amplifier bias and smear corrections are calculated from overscan
columns and virtual rows associated with individual FFIs. Flat-fields, fixed-
pattern noise, and non-linearity are corrected using models constructed from
pre-launch measurements and on-orbit commissioning data. Vanderspek et al.
(2019) describe these various instrumental effects. The associated calibration
procedures and code are included in the tica package and are described more
fully in (Fausnaugh et al., 2020). The calibrated images are in units of
number of electrons.

####  4.2.2 Photometry

The QLP uses a circular aperture photometry method to extract light curves for
all stars in the TIC with TESS-band magnitudes brighter than 13.5. The
apertures centers are based on a predetermined astrometric solution derived
for each observed frame using stars with TESS magnitudes between 8-10. The
light curves are extracted using five circular apertures with radii of 1.75,
2.5, 3.0, 3.5, 8.0 pixels. The aforementioned process as well as some of the
further steps (see below) are implemented using the various tasks of the FITSH
package (Pál, 2012).

The procedure to produce the target flux time series and background time
series are as follows. The photometric reference frame is computed using the
median of 40 frames with minimal scattered light. The reference frame provides
a high signal-to-noise image to compute the difference images, using a direct
subtraction of the photometric reference frame from the observed frames. The
final flux time series of the star is calculated as the sum of the constant
flux of the star and the variability of the star over time. The constant flux
is taken from the theoretical calculation in the TIC and the zero-point
magnitude measured for TESS. This method effectively de-blends the light curve
from contamination by an additional star inside the aperture. We measure the
variability over time in the difference images. The flux measured in the
aperture in the difference images is a sum of the stellar variability and the
background variability. To calculate the background variability (from uniform
faint background sources), we draw annuli around the target star in the
difference images. The annuli are drawn to have approximately the same area as
the aperture. The background variability at a given time point is calculated
as the median of the background pixels in the annulus in the difference image
after iterative outlier rejection. We estimate the background time series
independently as a sum of some constant background flux and background
variability over time. The constant term is calculated by subtracting the star
flux (from the TIC catalog) from the total flux in the aperture. We assume the
background per pixel is locally uniform in the vicinity of the target star,
its aperture, and background annuli; therefore, the constant background per
pixel in the aperture should be the same as in the annulus. The background
variability is calculated as the difference between the background in the
reference image annulus and in the difference image annulus.

The QLP then selects one of the five light curves extracted from the five
different apertures. Because the QLP’s primary goal is to detect transiting
planets (as opposed to preserving stellar variability), the QLP selects the
optimal aperture by minimizing photometric scatter in high-pass-filtered light
curves. An optimal aperture for stars in each magnitude range (13 linear bins
between TESS magnitudes of 6-13.5) is then selected by determining which
aperture size produces the smallest photometric scatter in the magnitude bins.
The photometric precision roughly follows the prediction from the Sullivan et
al. (2015) estimation for the majority of the stars and has a lower noise
floor101010The on-orbit performance of the TESS photometers is better than
pre-flight calculations from Sullivan et al. (2015), which has been traced to
an underestimate in their assumed telescope aperture (approximately 20 ppm)
for the brightest stars when the spacecraft operates nominally. TESS light
curves usually contain low-frequency variability from stellar activity or
instrumental noise, which must be filtered before the small, short-duration
signals caused by transiting planets can be readily detected. Following
Vanderburg & Johnson (2014) and Shallue & Vanderburg (2018), the QLP removes
this variability by fitting a B-spline to the light curve and dividing the
light curve by the best-fit spline. Outlier points caused by spacecraft
momentum dumps or other instrumental anomalies are masked out prior to
detrending. To avoid distorting any transits present, the spline is
iteratively fit, $3\sigma$ outliers are removed, and the spline is refit while
interpolating over these outliers (see Fig. 3 in Vanderburg & Johnson, 2014).

##  5 Transit Search

To search for transits, both the SPOC and QLP pipelines phase-fold processed
light curves with a large number of trial periods to search for repeating
transit-like drops in brightness. Any drop in brightness that passes a
specified threshold requirement is called a threshold-crossing event or TCE.
Each TCE has an associated Data Validation (DV) Report that provides a
detrended and phase-folded light curve in addition to auxiliary data products.

###  5.1 SPOC Transit Search

For each sector, the SPOC pipeline conducts a search for transiting planet
signatures in the systematic error-corrected light curves from
PDC111111PDCSAP_FLUX column in the light curve file using a module called
Transiting Planet Search (TPS). Targets with transit-like features that exceed
the threshold for statistical significance of 7.1$\,\sigma$, have two or more
transit signals, and that pass a number of other statistical vetoes (Seader et
al., 2013, 2015; Twicken et al., 2016) are then processed by the Data
Validation module (DV). The module runs a suite of tests on each transit
signature to help inform the classification of the events. The SPOC pipeline
also conducts periodic multi-sector searches, allowing for the detection of
much-longer-period transiting planet signatures as described in Section 5.1.2.

####  5.1.1 Transiting Planet Search Algorithm

The Transiting Planet Search algorithm (TPS) searches for signatures of
transiting planets by adaptively characterizing the power spectral density of
the observation noise, then estimating the likelihood of a transit over a
range of trial transit durations and orbital periods as a function of time
over the observations, quantified as the Single Event Statistic (SES; Jenkins,
2002; Jenkins et al., 2010, 2020).

TPS folds the SES time series over a range of trial orbital periods to
identify the strongest peak in the folded SES time series and designates TCEs
in the light curves with three requirements: a Multiple Event Statistic (MES)
in excess of 7.1, a passing grade on a series of transit consistency tests,
and at least two transits121212The Kepler Science Pipeline transit search
required a minimum of three transits to declare a TCE (Jenkins et al., 2020).
The MES represents the transit SNR multiplied by the correlation coefficient
between the true signal waveform and the trial waveform best matched over the
grid-search parameter space (duration, period, epoch).

TPS also provides an estimate of the effective noise “seen” by a transit of a
given duration, called the combined differential photometric precision (CDPP –
Christiansen et al., 2012; Jenkins et al., 2020). The CDPP can be used to
infer a lower limit for transit depth detectable at a given duration and
number of transits.

####  5.1.2 Data Validation

The Data Validation (DV) module fits a limb-darkened transit model to the
light curve for each target with a TCE and employs the same whitening filter
as is used by TPS to account for non-stationary correlation structure in the
observation noise (Li et al., 2019). A number of diagnostic tests are
conducted to inform the vetting process (Twicken et al., 2018).

The DV module produces a one-page PDF summary report for each TCE, a PDF full
report, and a PDF mini report for each target. The full report for each target
with TCEs includes the results of all the tests along with diagnostic figures
and run time warnings and errors. The mini-report for each target with TCEs
combines the one-page summary along with the most informative diagnostic tests
and graphics from the full report. An example one-page summary is shown in
Figure 2. The DV module also produces a FITS file of the time series it
analyzes as well as those presented in the diagnostic plots, and an XML file
with numerical model fit and diagnostic test results (Tenenbaum & Jenkins,
2018).

Figure 2: SPOC Data Validation (DV) summary report plots for TOI 270 b. Top
row: The detrended TESS light curve with transits marked by blue triangles.
Sector boundaries are marked with red vertical dashed lines. Momentum dumps
(described in Section 7.3) are marked with green vertical dashed lines. Second
row left: The TESS light curve folded on the planet candidate’s orbital
period. The black points are all data points, cyan points show data binned at
$\nicefrac{{1}}{{5}}$ the fitted transit duration, and the red line is a model
fit to the transit. Second row right: The best candidate for a secondary
transit found in the phase-folded light curve. Third row left: Zoom-in on the
phase-folded light curve. Third row right: The whitened transit fit is
indicated by the red line; the binned residuals from the fit are displayed in
green, and the out-of-phase residuals from the fit are shown in magenta.
Fourth row left: A comparison of the phase-folded odd transits with the phase-
folded even transits. The red dashed lines indicate transit depths. The title
line reports the significance of the difference between the odd and even
transits. Fourth row center: The centroid offset plot showing the right
ascension and declination offsets of a transit source with respect to the
position of the target star (as determined by the out-of-transit image
centroid). The centroid offsets for each sector are shown with green crosses;
the mean centroid offset over all sectors is shown with a magenta cross. The
location of the target star is marked by a red star. The 3$\sigma$ radius of
uncertainty for the transit source offset is shown by a blue circle. Fourth
row right: Fitted planet parameters and data validation diagnostic test
results. After its initial detection and vetting using these data products, a
three-planet system around TOI 270 was eventually validated by Günther et al.
(2019).

The DV module generates several metrics used in the assessment of the TCEs to
help eliminate background eclipsing binaries and background scattered light
features.

Difference images for each sector that represent the difference between the
out-of-transit fluxes adjacent to each putative transit and in-transit fluxes
are used to obtain the difference image centroids to identify the location of
the source of the transit-like features. These fit results are used to inform
the difference image centroid offset statistics, which are averaged over
multiple sectors where applicable and indicate whether the source of the
transit-like feature is consistent with the location of the target star.

The difference image centroid offsets are computed separately using two
approaches for target reference location: (1) the mean-proper-motion-corrected
catalog location of the target star during transit in the given sector, and
(2) the PRF-based centroid of the mean out-of-transit image in the given
sector. The first method is robust against crowding (because the difference
image features only the source of the transit signature), and has been
observed to be more reliable than the second for crowded TESS images. The
second method is not robust against crowding, but is more accurate in
uncrowded fields because the centroid bias common to the difference image and
out-of-transit image is removed.

Uncertainties in the centroid offsets are formally propagated from
uncertainties in the calibrated pixels from which the centroid offsets are
computed. In addition, an error term is added in quadrature to the propagated
uncertainties in the centroid offsets to account for systematic effects in the
centroid offsets. This was done in the Kepler Science Pipeline as well. For
Kepler, the plate scale was smaller (4 arcsec/pixel) and most targets were
observed in 17 quarters, permitting significant averaging of the quarterly
centroid offsets; the quadrature error term was set so that the difference
image centroid offsets were not significant at the 3-$\sigma$ level if they
were $<$0.2 arcsec. For TESS, the plate scale is larger (21 arcsec/pixel) and
a majority of targets are only observed in a single sector; the quadrature
error term is set so that the centroid offsets are not significant at the
3-$\sigma$ level if they are $<$ 7.5 arcsec.

The ghost diagnostic is formulated as the ratio of the detection statistic of
the flux time series obtained with the optimal photometric aperture to the
detection statistic of the flux time series obtained from a halo about each
target’s optimal aperture.

After conducting the diagnostic tests (useful, e.g., for detecting
contamination of the background by light scattered from nearby bright variable
stars), the DV module masks out the transits of the current TCE 131313One full
transit duration prior to and following each transit are masked along with the
transits in the search for additional TCEs in the SPOC pipeline. and calls TPS
to search for additional TCEs in the light curve (up to a maximum of six TCEs
per target star). DV also compares the orbital periods between TCEs associated
with a given target star to flag cases where the periods are statistically
identical; this can indicate that the TCEs are primary and secondary events in
stellar eclipsing binaries rather than transiting planet signals.

TPS and DV are also run on light curves for stars observed in multiple
sectors, increasing sensitivity to longer-period and smaller planets. Multi-
sector planet searches were run for Sectors 1-2, Sectors 1-3, Sectors 1-6,
Sectors 1-9, and for Sectors 1-13; in Year 2, for Sectors 14-16, 14-19, 14-23,
and 14-26. In these multi-sector runs, TCEs could appear in each of the
sectors searched for the multi-sector range, or in a subset. In SPOC/TPS,
targets are processed in later multi-sector runs only if they were observed in
sectors not included in a previous multi-sector run. For example, a target
observed only in Sectors 1 and 2 would not have been included in the Sector
1-3 run.

Following processing, the SPOC pipeline delivers the TCEs and data validation
products to the POC. POC conducts a final review and quality assessment, then
prepares the products for submission to
MAST141414https://archive.stsci.edu/tess/bulk_downloads.html. MAST ingests
these data, along with the FFIs, postage stamp data, light curves, and
cotrending basis vectors (Tenenbaum & Jenkins, 2018). The public data products
are accompanied by POC-produced data release notes for each sector. For
certain sectors before March 2019, MAST released the FFIs ahead of the TCEs
and data validation products. This staggered release, which efficiently
released early TESS data to the community, allowed observers to follow up
target stars in the given sector while still visible for the given season.

###  5.2 QLP Transit Search

The QLP carries out a corresponding but independent analysis to the SPOC
pipeline. Complementary to SPOC pipeline, the QLP also aims to identify TCEs
along with diagnostic information.

####  5.2.1 Box-Least Squares Transiting Planet Search

The QLP begins by stitching light curves together from all available sectors
for each target. The QLP then searches the light curves for periodic transits
using the Box Least Squares algorithm (BLS; Kovács et al., 2002). The search
is performed for periods ranging from $0.1$ days to half the length of the
longest baseline expected for the given target star. The number and spacing of
frequencies searched by BLS is adapted to the total baseline in the light
curves, similar to Vanderburg et al. (2016a).

Any signal with a signal-to-pink-noise ratio (SNR, as defined by Hartman &
Bakos, 2016) $>9$ and BLS peak significance $>9$ is designated as a TCE. The
BLS peak significance is defined as the height of the BLS peak in the spectrum
compared to the noise floor of the BLS spectrum.

To search for additional transiting planets in a light curve with a detected
signal, the QLP clips out transits with the period of the strongest BLS peak,
masking one full transit duration prior to and following each transit along
with the transit. QLP re-runs the search until no further significant peaks
are found.

####  5.2.2 QLP Data Validation

The QLP data validation report provides diagnostic plots to aid in TCE
classification. The first page, shown in Figure 3, is a summary page.

Figure 3: QLP Data Validation (DV) summary report plots for $\pi$ Men c (Huang
et al., 2018b). Top row: The detrended full light curve with all available
sectors stitched together. Transits are marked by red vertical lines. Second
row left: Phase-folded light curve. The grey points are all data points and
the blue points are binned relative to the transit window size to guide the
eye. Second row right: The phase-folded light curve at phase 0.5 for a
secondary eclipse test. Third row: Even and odd transits. The model fit is
shown in orange. The depths of even transits are marked by darker dashed
lines, and odd transits by lighter dashed lines. A significant difference in
the depths ($>5{\sigma}$) would suggest that the TCE is an eclipsing binary at
twice the period. Right-hand column: Planet and stellar parameters.

Additional pages in the full QLP data validation report provide additional
metrics for decision-making. These plots are useful for diagnosing whether the
source of transit-like variability is on target or from a nearby blended
source, which is particularly important for FFI targets. For example, the QLP
compares the depth of the transit signal measured in multiple apertures of
different size. If the depth of the transit increases with aperture size, that
is usually a sign that the variation is from a nearby source and not from the
target. The QLP also searches within $155^{\prime\prime}$ of the target star
for other light curves with transits which match in period and epoch. The QLP
reports also check whether the epoch and period of the TCE matches with known
planets or false positives (Coughlin et al., 2016; Triaud et al., 2017a;
Collins et al., 2018a; Schanche et al., 2019).

##  6 TCE Triage

Most of the signals detected by the SPOC/TPS and QLP pipelines are not due to
transiting planets, but instead are caused by astrophysical phenomena such as
stellar variability, eclipsing binary stars, or non-astronomical instrumental
artifacts. We use a “triage” process to identify transit-like events in the
TCEs produced by both the SPOC and QLP pipelines. The goal of triage is to
separate out transit-like events from stellar variability and instrument
noise.

We identify the signals most likely to be planetary transits using a
combination of automatic classification algorithms and visual inspection. We
identify the best planet candidates in a two-step process: a partially
automated triage in which the least-likely TCEs to be planet candidates are
efficiently removed followed by a more labor-intensive team vetting process.

###  6.1 Triage of SPOC TCEs

TESS-ExoClass (TEC)151515https://github.com/christopherburke/TESS-ExoClass
provides an automated method for the vetting team at the TSO to identify
potential planet candidates from the postage stamp TCEs generated by the SPOC
pipeline. TEC borrows heavily from the successful automated planet candidate
vetting tools from the Kepler Robovetter (Mullally et al., 2015; Coughlin et
al., 2016; Thompson et al., 2018).

Here we summarize the TEC process. We are actively improving TEC and describe
here its latest form, which was largely stable in the latter half of the first
year of TESS observations (Sectors 6-13), and had few changes in Year 2 of the
Prime Mission. TEC changed rapidly during the first four TESS sectors as the
tests and their thresholds were tuned in order to match the manual
classification effort performed by the TSO. TEC python code is open source and
classification tables from TEC for observed TESS sectors are available.
However, due to the changing nature of the thresholds, detections from Sectors
1-6 are less uniformly selected relative to the latter sectors where TEC
thresholds and algorithms were more stable.

TEC establishes an initial filter that removes TCEs likely due to instrumental
systematics. The initial filter criteria ensure that the signal continues to
be significant when an alternative detrending is applied to the flux time
series and individual transit events have the shape expected for a limb-
darkened transiting planet. The TCEs that pass this initial triage step are
subject to additional tests. These tests identify false positives due to
stellar binaries, astrophysical variability, or instrument systematics (Brown,
2003; Coughlin et al., 2016; Mullally et al., 2018). TEC also flags TCEs with
large centroid offsets and those occurring at the same time as a spacecraft
momentum dump (Li et al., 2019).

Remaining TCEs are classified into three Tiers. Tier One corresponds to TCEs
that passed all the above criteria and did not receive any warning flags. The
Tier One TCEs represent the highest quality and most likely detections to
result in planet candidates. Any TCE which receives at least one warning flag
in the above tests is placed in the Tier Two category. However, TCEs with a
warning flag for a significant secondary eclipse or a warning flag for
coherent out-of-transit variability are assigned to the Tier Three category.
The Tier Three category is predominantly made up of stellar eclipsing binary
detections. An exception to prevent a candidate from inclusion in Tier Three
is made if the secondary eclipse could be consistent with reflected light from
a planet rather than a self-luminous body. Only the Tier One and Two lists are
sent through to the team vetting stage.

As an example of TEC processing results, for the original 1458 TCEs generated
by the SPOC pipeline for Sector 12, 540 TCEs passed the initial TEC triage
stage. Of those 540, 45 TCEs were assigned to the high quality Tier One list;
315 TCEs were assigned to the Tier Two list; and 180 were assigned to Tier
Three.

###  6.2 Triage of QLP TCEs

TCEs generated by the QLP number in the tens of thousands per sector, as
compared to typically 1500 TCEs per sector from the SPOC pipeline. An
aggressive triage process is therefore needed to cull these signals to a few
hundred high-quality potential planet candidates to be reviewed by eye. We use
Astronet-Triage (Yu et al., 2019), a version of the AstroNet convolutional
neural network (CNN) classifier (Shallue & Vanderburg, 2018) modified to work
on TESS data, to separate eclipse-like events from instrument systematics and
stellar variability. Yu et al. (2019) uniformly labeled QLP TCEs (i.e.,
careful manual vetting of an inclusive sample of TCEs) from sectors 1-5 to use
as input training data for the new AstroNet-Triage CNN.

For each sector, Astronet-Triage ingests tens of thousands of TCEs brighter
than $T_{\rm mag}$$=$ 13.5 and selects several thousand (3000-4000) targets as
potential planet candidates.

Next, this number is further reduced by setting a brightness threshold of
$T_{\rm mag}$$=$ 10.5, prioritizing target stars most accessible to ground-
based observers. In addition, the TCEs with estimated planet radii greater
than 35 ${R_{\oplus}}$ are removed. For TCEs with unknown stellar radius (and
therefore unknown planetary radius), a transit depth cut is implemented at
6$\%$ for stars hotter than 3500 K or stars cooler than 4000 K with a proper
motion of less than 10 milliarcseconds/year. In sectors 1-11, stellar
parameters for imposing these cuts (and for the QLP DV reports) came from Gaia
DR2 (Gaia Collaboration et al., 2016, 2018). If Gaia DR2 data were
unavailable, the most recent TIC catalog was used. For sectors 1-11, this was
TICv7; for Sector 12 onward, the pipeline referred to TICv8 for stellar
parameters. Following the release of TICv8, QLP reprocessed the light curves
for the Year 1 TOIs using the updated stellar parameters.

Following these cuts, only a few hundred TCEs remain for visual inspection.
The QLP generates DV reports for these targets, including centroid
information, difference image comparison, multi-aperture comparison, and an
MCMC fit to the folded transit (Foreman-Mackey et al., 2013). Also, at this
stage, the QLP flags candidates which match in period and epoch against
catalogs of known eclipsing binary false positives (Collins et al., 2018b;
Triaud et al., 2017b).

##  7 Vetting

Vetting refers to visual inspection of DV products at the MIT TSO to determine
which TCEs will become part of the TOI Catalog. Group vetting is the main
vetting process, while individual vetting is used to reduce the number of
surviving planet candidates for cases where computer algorithm triage has
passed a large number of candidates. The vetting process was “rehearsed” using
K2 C17 data before TESS science operations began (Crossfield et al., 2018).
Individual vetting was heavily used in sectors 1-5 before TEC and AstroNet-
Triage were available. The vetters are a rotating team of about 40 volunteers
from the TSO and wider exoplanet community at various levels of expertise. New
vetters are trained extensively by the TSO. Figure 4 describes the flow of
TCEs through the vetting process. In addition to inspecting TCEs from the two
pipelines, the vetters examine candidates identified by the community (CTOIs,
as explained in Section 8.4). The goal of vetting is to provide planet
candidates to the community but is not intended to be 100% complete.

Figure 4: Flow chart of the TOI triage and vetting process. Orange blocks are
sources of TCEs. Green blocks are process steps. Purple diamonds are decision
points. Blue blocks are outcome products. The TOI process triages TCEs from
the pipeline of record and the community, then vets any TCE with at least one
Planet Candidate disposition from individual vetting. The TOI process produces
the TOI List, which is a subset of the list of vetted TCEs.

###  7.1 Individual Vetting

Individual vetting is visual inspection of TCE data products for
classification by a team of vetters working independently. Individual vetting
is used if a large number of TCEs make it past triage, with the goal of
reducing the number of TCEs that must be inspected during the more definitive
and labor-intensive group vetting process. In individual vetting, the vetters
use custom software known as the TESS Exoplanet Vetter (TEV) to disposition
TCEs. Dispositions are based on the DV products, including the raw light
curve, phase-folded detrended light curve, odd-even plots, difference image
and out of transit images, finder charts of nearby stars, and estimated planet
radius based on reported stellar parameters (Twicken et al., 2018). TEV also
provides vetters with a text box to provide comments for Group Vetting to
review.

Each target is vetted by at least three and no more than five vetters. To
ensure all potential planet candidates are considered, any TCE vetted as a
planet candidate (PC) or undecided (U) by any vetter is sent through to Group
Vetting, where it will be carefully reviewed by a small group with more
expertise. Individual vetters are trained to be generous in their
dispositioning so that edge cases are preserved and sent to Group Vetting.
TCEs vetted unanimously as not a planet candidate or undecided are assigned a
disposition of eclipsing binary (EB), stellar variability (V), or instrument
noise/systematic (IS) based on the majority rule and exit the vetting process.
Table 1 lists all disposition labels. In cases of a tie between non-planetary
dispositions, eclipsing binary is used. TCEs that leave the vetting process as
EB, V, or IS are not included in the final TOI Catalog.

Table 1: Definitions for Vetting Dispositions.

Disposition | Definition
---|---
PC | Planet Candidate; U-shaped transit
EB | Eclipsing Binary; V-shaped transit
V | Stellar Variability; sinusoidal transit
IS | Instrument Noise or Systematic; often ramp- or transit-shaped
U | Undecided; ambiguously transit-like

Note. — The vetting process uses features of the transit light curve, as well
as other observables, to determine the disposition of each TCE.

Early in the mission, before TCE triage was largely automated by TEC and
AstroNet-Triage, individual vetting was part of the main process for
identifying TOIs. By Sector 6, however, automatic triage had significantly
reduced the need for individual vetting. Individual vetting remains useful for
teaching vetters the characteristic appearance of TESS data and training them
to make accurate classifications.

###  7.2 Group Vetting

The TCEs that pass through the triage process (and individual vetting, if
applicable) proceed to a Group Vetting session where a team of at least three
vetters visually inspect each TCE. Each sector typically yields about 200 TCEs
for Group Vetting. The TSO vetting teams of experienced experts meet in up to
four sessions per week, handling about 50 TCEs in two hours per session. Group
vetters include persons with expertise on historical Kepler data analysis, and
new vetters who are brought on board through extensive training, starting with
participation in individual vetting.

The main goal of Group Vetting is to promote suitable TCEs to the TOI list and
to remove false positives from consideration. Vetting relies on a team of
vetters who can visually discern subtleties in the data better than computers
and can impose physicality and consistency priors. Vetting by eye also enables
us to keep track of performance differences between the data pipelines. It is
possible that Group Vetting will be partially automated in the future, but the
TSO has focused on enhancing the automated triage stage so far.

Group Vetting reviews key metrics in the DV reports that can expose false
positives. For example, off-target, transit-like signals may be distinguished
from on-target planets by examining the location of the centroid in and out of
transit. Movement of the centroid in the difference images to a star 1 pixel
away or more shows that the pipeline triggered on a TCE that is likely caused
by a background eclipsing binary. Off-target TCEs can also be revealed by
comparing the depth of transits in light curves extracted from differently
sized apertures on the QLP DV reports, or using the ghost diagnostic flag in
the SPOC/TPS DV reports. If the transit depth increases with aperture size,
the TCE signal usually comes from a nearby source and can be removed from
consideration for the TOI Catalog.

During Group Vetting, supplementary information is considered such as
parameters from Gaia DR2 (which were implemented by SPOC in Sector 14-26), and
information from ExoFOP-TESS161616ExoFOP-TESS, or the Exoplanet Follow-up
Observing Program, is the website which supports community follow-up of TESS
targets by organizing community-gathered follow-up data and derived
astrophysical parameters, https://exofop.ipac.caltech.edu/tess/ about known
planets and/or previous observations.

In addition to setting the disposition of TCEs, Group Vetting assigns follow-
up priority levels for each of the five subgroups (SGs) of the TESS Follow-Up
Observing Program (TFOP). Planet candidates with $R_{p}<4{R_{\oplus}}$ are
TESS’s highest priority, and those orbiting brighter stars are more likely
than those orbiting fainter stars to be amenable to mass measurements with
precise radial velocity instruments. To assign initial priorities, we consider
the promise of each system for precise radial velocity (PRV) observations as
well as the possibility that false positives remain that cannot be excluded by
TESS data alone. We calculate the radial velocity (RV) semi-amplitude using
the Chen & Kipping (2017) planetary mass-radius relation, and then estimate
the telescope time required to measure a 5-$\sigma$ mass using a 4-m class
precise RV facility such as HARPS.

The most promising TOIs (typically with $V_{\rm mag}$ $\lesssim$ 11) are
assigned priority 1 for photometry (SG1) and reconnaissance spectroscopy (SG2)
because it is necessary to know if they are false positives or not before
investing in precise radial velocity follow-up. TOIs with $SNR<11$ are
assigned priority 2. Priority 2 targets often orbit fainter stars and have
smaller expected RV signals. Larger planet candidates ($R_{p}>4R_{\oplus}$),
or small candidates orbiting stars which are likely too faint for RV follow-up
are given priority 3 for SG1 and SG2.

The other SGs (high-resolution imaging, precise radial velocities, and space-
based photometry) have priorities initially set to 4 (meaning that
observations are on hold), and priorities are updated on ExoFOP-TESS following
results from SG1 and SG2. Known planets, discovered previously in surveys such
as WASP, HAT, and KELT, are priority 5 for all follow-up groups (no further
observations needed). Finally, we note that even low-priority planet
candidates get observed by SG1 and SG2, and that many of the follow-up steps
proceed in parallel because of the practical constraints imposed by telescope
schedules and the scientific interests of those carrying out observations.

###  7.3 False Positives

We find false positives in the TCE light curves during vetting and triage.
These false positive TCEs are not made into TOIs. However, vetting and triage
cannot catch all false positives: we expect follow-up observations will find
some fraction of TOIs to be false positives. In the Prime Mission, $565/2241$
TOIs (about $25\%$) have been identified as False Positives from follow-up
observations. We note which TOIs are false positives with a note in the
“Public comment” field of the catalog, and lower the TFOP priority for those
candidates.

Though a full description of the characteristics of TESS false positives is
beyond the scope of this paper, we list some common features of false
positives here. Some light curve features indicative of non-planet signatures
are specific to the TESS instrument and spacecraft.

For example, spacecraft momentum dumps may create signals that mimic transit-
like events in depth and duration. These momentum dumps occur when the
reaction wheels on the spacecraft are periodically de-spun to relieve momentum
built up from solar radiation pressure imbalance on the spacecraft. Momentum
dumps briefly affect the spacecraft pointing for 1-2 frames. In the beginning
of Year 1, there wer 3-4 momentum dumps per orbit. In Year 2, momentum dumps
were reduced to 1-2 times per orbit. For early sectors, the SPOC pipeline and
the QLP were not able to distinguish transit-like events due to momentum dumps
from actual transits. Visually, there is a slight distinction, in a sharp
ingress and a slow egress different from a planet transit with ingress and
egress having similar slopes. Vetters used the transit shape and the known
occurrence times to eliminate events suspected to be caused by momentum dumps.

To aid vetting, the SPOC pipeline added momentum dump markers to the detrended
light curves in the DV summary reports, starting with Sector 3. The QLP used
the quaternion information (three-dimensional spacecraft orientation,
velocity, and rotation) from the spacecraft to mask frames in which momentum
dumps were occurring, starting with Sector 1. The SPOC pipeline masks data on
cadences identified by the POC as directly associated with momentum dumps in
PDCSAP light curves. Such cadences are identified in SPOC pipeline archive
products with a dedicated reaction wheel desaturation event data quality bit
(Tenenbaum & Jenkins, 2018).

In addition to spacecraft momentum dumps, the pipelines flagged and removed
cadences affected by scattered light (Vanderspek et al., 2018). A full
detailing of these features, in addition to occasional thermal and power
events disrupting data acquisition, can be found in the TESS Data Release
Notes 171717https://archive.stsci.edu/tess/tess_drn.html.

Vetters can identify false positive TCEs by a few tell-tale signs: a visible
secondary eclipse which cannot physically be planetary in nature; a mismatch
between odd and even transit depths caused by a stellar binary at twice the
indicated period; a shift of the centroid to another nearby star; and
increasing transit depth with aperture size, indicative that a signal is from
an object nearby on the sky and not the suspected target star.

TCEs are especially prone to being false positives as TESS pixels are large
enough to include multiple stars. Blended stellar signals from a single pixel
can lead to confusion between on-target planetary transits with nearby
eclipsing binaries and some types of stellar variability. For the majority of
cases, we can recognize the contribution to a star at a minimum distance of 1
pixel away.

Some TCEs have only very subtle differences in transit depth between odd and
even transits or weak secondary eclipses that require careful review via group
vetting. Cases that cannot be resolved on TESS data alone are passed on to
TFOP as TOIs for additional observations and detective work with the goal of
improving reliability without compromising completeness. The follow-up efforts
are described in depth in Section 10.3.

We rely on follow-up observations to identify false positives which were not
caught in triage or vetting. It is very difficult, if not impossible, to
identify the star that is the source of the transit signal from the TESS data
alone when there are two (or more) stars within the same TESS pixel. In these
cases, where the transit signal could be planetary if on either star, we
release the target as a TOI and follow-up observations (by subgroup 1) are
used to identify the star showing the transit signal. Reconnaissance
spectroscopy (obtained by TFOP subgroup 2) is used to confirm the parameters
of the primary star and identify TOIs which are stellar eclipsing binaries
where both stars show lines in the spectrum (spectroscopic binary type 2). In
addition, low-precision RVs, obtained from a few spectra, are used to identify
TOIs showing large RV variation of a few 10 km/s, indicating that the
transiting object is too massive to be a planet (spectroscopic binary type 1).
The other common source of TOI False Positives is our improved understanding
of host star parameters. The inclusion of Gaia DR2 data in TICv8 gave a more
accurate determination of stellar radius compared to TICv7. For some stars,
the new TICv8 stellar radius is bigger, which changes the transiting object
radius to be much too large to be a planet.

###  7.4 Numbers and Timeline

Large numbers of TCEs are delivered per sector from each pipeline. The SPOC
pipeline produces, on average, about 1,200 TCEs per sector, out of about
20,000 two-minute cadence targets observed. Before triage, QLP produces 50,000
TCEs per sector (with a $T_{\rm mag}$$=$ 10.5 cutoff for vetting), from about
half a million FFI light curves. Each sector yields about 100 total TOIs from
the SPOC pipeline and the QLP. Of the 1,250 TOIs released during Year 1, 761
were from the postage stamps and 489 were from non-postage-stamp regions of
the FFIs. 651 TOIs had data from a single sector and 599 had data from two or
more sectors. Of the 991 TOIs released during Year 2, 494 were from the
postage stamps and 497 were from the non-postage-stamp regions of the FFIs.
274 TOIs from Year 2 had data from a single sector and 717 had data from two
or more sectors.

Of the total 2241 TOIs from the full Prime Mission, 256 TOIs were known
planets and 1,985 were new planet candidates. We detected 251 of the 1,543
known planets ($\sim 16\%$) around ${T_{\rm mag}}<13.5$ host stars originally
detected via the transit method. We expect this fraction to be small, as many
of these known planets were Kepler discoveries with the SNR of transits of
many of these planets to be too low for TESS. For the 864 known planets
detected via other methods and orbiting host stars with ${T_{\rm mag}}<13.5$,
we detected 5 as new TOIs. We expect the number of radial velocity planets
TESS re-observed to be low because of transit probabilities being low in
general for RV-detected planets. The known planet TOIs are discussed in more
depth in Section 10.4.

Given the different detrending and detection methods employed by the two
different pipelines, we do not expect the two pipelines to always detect the
same signals in the same data. Of the 230 TOIs SPOC detected in Sectors
6-13181818Because Sectors 1-5 did not use AstroNet triage, these sectors were
not considered in this comparison. with ${T_{\rm mag}}<10.5$ host stars, the
QLP BLS detected 206. The QLP AstroNet triage (see Section 6.2) excluded 58 of
these in automatic triage and visual triage rejected an additional 29 TCEs,
giving a total of 119 TOIs detected by both SPOC and QLP. Future upgrades to
AstroNet will aim to improve its recall of small and low S/N planet candidates
detected by SPOC. In sectors 14-26, of the 146 TOIs SPOC detected, QLP BLS
detected 137. The QLP AstroNet triage excluded 35 of these in automatic
triage, and visual triage rejected an additional 15, giving a total of 87 TOIs
detected by both pipelines.

TESS aims to survey nearby, bright stars rather than conduct an exhaustive
census. When one signal is detected by one pipeline but not the other, we rely
on the human vetters to determine if the transit detection is valid using the
vetting criteria described in Sections 7.1 and 7.2.

TESS data are processed rapidly once they have been downloaded from the
spacecraft. Once the raw FFIs arrive at the TSO, processing by the QLP is
completed within about 7-10 days, yielding a large set of TCEs for triage.
Concurrently, the SPOC pipeline produces calibrated FFIs and DV products for
two-minute cadence TCEs within 7-14 days from receipt of data, including a
formal review process. TEC triages the SPOC pipeline data to produce a reduced
set of TCEs in 1-2 additional days. Vetting takes 1-2 weeks per sector and an
additional 2-3 days to prepare the TOIs for release. Overall, TESS data and
TOIs generally reach the public via MAST about a month after the end of a
sector.

##  8 Releasing TOIs to the Community

Following group vetting, and a final check for accuracy, the TOIs are released
to the community on a variety of platforms.

###  8.1 TOI Delivery

The TOI Catalog includes planet candidates suitable for follow-up
observations, as well as a smaller number of previously-known exoplanets. TOIs
are released on a public-facing webpage at https://tess.mit.edu/toi-releases/
and updated automatically (twice daily) on ExoFOP-TESS. The first TOI Catalog
was delivered to MAST on December 2, 2018
191919http://archive.stsci.edu/tess/bulk_downloads/bulk_downloads_tce.html.
MAST archives a static version of the TOI Catalog on a monthly basis. The
living TOI Catalog, updated every sector and intermittently between sectors,
is described by this paper.

Targets are assigned consecutive TOI IDs. Multi-planet systems are assigned
suffixes (.01, .02, etc.) mirroring the suffixes assigned for the TCE. TEV
automatically generates a comma-separated values (CSV) file with all necessary
parameters for each TOI from the vetted TOI list. Each TOI comes with a table
of parameters, a DV summary page, and a full DV report.

Some TOIs may be held back from delivery if, for example, the target requires
reanalysis (for instance, to better constrain or confirm its orbital period or
to identify/disentangle additional candidates in the light curve).
Additionally, if a target shows a marginally-detected transit in a single
sector of data, but is expected to be in the TESS field of view in a following
sector, the team may opt to wait to see if the transit becomes more pronounced
before releasing it. Even though SPOC/TPS and QLP/BLS are not designed to
search for single-transit events, they are occasionally detected when the
transit happens to line up with either spurious noise events or to a data gap
in the phase-folded light curves examined by TPS/BLS. We do include these
single transit candidates in the TOI catalog, with the orbital period
parameter removed before release. If there is another transit of the candidate
in a later sector, we update the TOI in the catalog with the correct period.

###  8.2 TOI Updates

The online TOI Catalog at tev.mit.edu and ExoFOP-TESS is continuously updated
to provide the most recent information to the community. We update entries in
the TOI catalog in certain cases, including:

  1. 1.

TOIs which appear in several sectors accumulate more observations, which
improve the determinations of period, epoch, and other parameters. The TOI
catalog is therefore updated with parameters from the longest baseline of data
for each TOI, with a preference for SPOC postage stamp data, largely due to
its two-minute cadence and review process as the pipeline of record.

  2. 2.

TOIs on the ExoFOP-TESS platform have updated TFOP Working Group priorities as
each working group makes progress with follow-up observations.

  3. 3.

When TFOP identifies TOIs as false positives, their initial disposition in the
TOI catalog remains unchanged (to preserve the candidate’s history), but a
separate TFOP working group disposition is updated on ExoFOP-TESS. The TFOP
working group disposition is updated in the TOI catalog as well.

###  8.3 Retired “Alerts” Process

The TSO used an “Alerts” process to get candidates to the community as quickly
as possible during Sectors 1-3. The goal of the Alerts was to release TOIs for
timely follow-up before the full data sets from the sector were publicly
available on MAST. Alerts were posted on the MIT TEV Alerts Portal and on MAST
as the first high-level science product of the mission. All the TESS-DATA-
ALERTS products that were made public are available at MAST via
https://doi.org/10.17909/t9-wx1n-aw08 (catalog
https://doi.org/10.17909/t9-wx1n-aw08). Following Sector 3, the FFI data were
processed and released in advance of the completion of the TOI process.

###  8.4 Community TOIs

Community TOIs (CTOIs) are objects of interest identified by pipelines in the
community and reported on ExoFOP-TESS, often in bulk uploads not tied to the
official data release from any particular sector. ExoFOP emphasizes CTOIs
should only be reported if there is no TOI with the same period. The TOI team
periodically checks for matches on TIC ID and period between the CTOI list
hosted on ExoFOP and the comprehensive collection of TCEs considered for
vetting on the TEV platform.

169 CTOIs from the Prime Mission are now TOIs; 106 of these were recovered
from TCEs mistakenly ruled out in pre-vetting triage 202020ExoFOP-TESS,
accessed 8 Oct. 2020. All CTOIs which have been promoted to TOIs were also
TCEs in the SPOC pipeline or the QLP, and recovered from those pipelines. For
example, two CTOIs from the Planet Hunters TESS project
212121http://www.planethunters.org were promoted to CTOIs and then released as
TOIs.

In the Prime Mission, $\sim 40\%$ of the 841 CTOIs reported on ExoFOP matched
in period and epoch to TCEs with reports on TEV found by the TSO, meaning TCEs
which passed triage and were considered for vetting. For the remaining $\sim
60\%$ of CTOIs, which did not match with TCEs on TEV, about half corresponded
to TCEs from either the QLP or SPOC analysis, and the other half did not have
data products from either pipeline. For the CTOIs which did not match in
ephemeris to TCEs on TEV, but which had TCE data products from either SPOC or
QLP, many were excluded from vetting in the standard pre-vetting triage
process. For example, TCEs from the QLP below the ${T_{\rm mag}}=10.5$
threshold were not considered for vetting; other TCEs were ruled out as non-
planetary transit events in pre-vetting triage. Single-transit events reported
as CTOIs and which were also SPOC TCEs were sometimes mistakenly classified in
triage as EB events (described in more detail in Section 10.6). These rejected
TCEs are re-considered in the CTOI review process. The vetting team may
promote a CTOI to a TOI if the CTOI identifies a quality planet candidate from
the SPOC or QLP pipeline mistakenly ruled out in the triage or vetting
process. CTOIs which do not match with TCEs on TEV, and which also do not have
data products from either the QLP or SPOC pipeline, could still be valid
events, but were not found in the initial run of either pipeline, and would
need to be recovered manually in a reprocessing step.

###  8.5 Naming TESS Planets

The project will assign TESS planet names to TOIs which are confirmed planets,
either with a mass measurement or validated by another method. The TESS planet
name will have the same major number as the TOI ID. Planet suffixes (b, c,
etc.) will follow the suffixes used for publication of the planet, under the
general convention to assign first in order of validation, and then period.
Said another way, if a planet is discovered with a shorter period than planet
b in a given system, the new planet will get the next consecutive letter as a
suffix. Planet names will be assigned by the project after a TOI has been
confirmed and appears on the NASA Exoplanet Archive. TESS planet name will
also appear as an additional column in the TOI Catalog and propagated to MAST
and ExoFOP-TESS. TOIs which are known planets from other surveys and which
meet the same requirement as TOIs of mass measurement or other validation will
get a TESS planet name. Assigning TESS Planet names to known planets will
highlight TESS’s ability to recover these planets. Although the TESS Prime
Mission is focused on measuring sizes and masses for small exoplanets, the
broader exoplanet community reaps the benefits of a diverse catalog of TESS
planets which have been validated without a measured mass.

##  9 TOI Catalog Description

The TOI Catalog is a living collection of the TESS mission planet candidates
from the Prime Mission and beyond. The list includes both TESS-discovered
planet candidates (denoted PC) and known planets (denoted KP) from past
surveys. The living catalog is available online
222222https://tess.mit.edu/toi-releases/. This same living list is the version
of the catalog posted to MAST and ingested to ExoFOP-TESS to be augmented with
information from follow-up observations. Archived versions of the catalog are
available on
MAST232323https://archive.stsci.edu/missions/tess/catalogs/toi/previous/.

###  9.1 Catalog Overview

A TOI is a target initially considered by the project to be a planet candidate
based on the TESS light curve and other information available at the time it
is inspected by the TSO. As TOIs are followed up by additional observations,
the project tracks the TFOP Working Group disposition for each target in the
catalog, and whether the target is a known planet from a previous survey (KP),
promoted to a confirmed planet (CP), retracted as a false alarm (FA), or
demoted as a false positive (FP). The CP category includes planets with
confirmed masses and statistically validated planets that do not yet have
measured masses.

The columns in the TOI catalog uniquely identify each candidate, its
provenance, and, in the case of the comments column, particular issues or
notable features of a TOI. The TOI team also provides to the community a
“TOI+” table with a more expansive set of parameter columns useful for
planning follow-up observations, including stellar log _g_ , stellar radius,
planet radius, planet equilibrium temperature, stellar effective temperature,
signal-to-noise, centroid offset flag (“TRUE” when TOI had a centroid offset
greater than 5 sigma), and TFOP priorities.

We provide a sample of the TOI catalog with stellar and planetary parameters
subdivided for ease of reading in Table 2 and Table 3, respectively. The TOI
catalog columns (headers in bold-face type) are as follows:

  * •

Source Pipeline Name of pipeline which provided the TOI period, epoch, transit
duration, and transit depth. Either “QLP” or “SPOC.”

  * •

Stellar Catalog Version of TIC or other catalog (e.g. Gaia) which sources the
provided stellar parameters

  * •

TIC Unique identifier from the TESS Input Catalog.

  * •

Full TOI ID Unique identifier for each TOI planet candidate for the star.

  * •

Signal ID Identifier for each planet candidate in a system; ordered by time of
release as a TOI.

  * •

TESS name TESS planet name for confirmed planets (see Section 8.5)

  * •

TOI Disposition “PC” for planet candidate and “KP” for known planet from a
previous survey

  * •

EXOFOP Disposition Provided by ExoFOP-TESS. TFOP Working Group disposition
following additional observation (CP for confirmed planet, PC for planet
candidate, FP for false positive, FA for false alarm, and KP for known
planet). Updated on TEV each time new TOIs are released.

  * •

TIC Right Ascension  In degrees. All coordinates in equinox J2000.0, and epoch
2000.0.

  * •

TIC Declination In degrees. All coordinates in equinox J2000.0, and epoch
2000.0.

  * •

$T_{\rm mag}$ In magnitude. Includes uncertainty.

  * •

$V_{\rm mag}$ In magnitude. Includes uncertainty.

  * •

Epoch Barycentric-corrected epoch of first transit in TESS Julian Days.
$\text{BTJD}=\text{BJD}-2457000$ 242424Tenenbaum & Jenkins (2018). Includes
uncertainty.

  * •

Orbital Period In days. Can be blank, as is the case for single transits.
Includes uncertainty.

  * •

Transit Duration In hours. Transit duration from first to fourth contact.
Uncertainty column can be blank if duration is an estimate.

  * •

Transit Depth In ppm. Uncertainty column can be blank if transit depth is an
estimate.

  * •

Sectors Space-separated string of sectors contributing data to TOI parameters.

  * •

Public Comment From TOI group vetting, including whether follow-up is in
progress, if the event is a single transit, and the catalog name of the planet
if it is previously known. Can be blank.

  * •

Alerted Date TOI was released for the first time.

  * •

Edited Date of most recent update to TOI parameters.

The TOI list is provided in the AAS Journals machine-readable format and also
as a CSV file with header rows detailing the contents and creation of the
list.

       #TESS Objects of Interest
       # File Created: 2020-10-18
       # Sectors: S0001-S0026
       # Date of previous TOI list release: 2020-08-12
       Source Pipeline,Stellar Catalog,TIC,
       Full TOI ID, Signal ID, TESS Name, TOI Disposition,
       EXOFOP Disposition,TIC Right Ascension,
       TIC Declination,TMag Value,TMag Uncertainty,
       VMag Value,VMag Uncertainty,
       Orbital Epoch Value,ΨOrbital Epoch Error,
       Orbital Period Value,Orbital Period Error,
       Transit Duration Value,Transit
       Duration Error,
       Transit Depth Value,Transit Depth Error,
       Sectors,Public Comment,Alerted,Edited

Usually, all of these fields are populated in the TOI catalog with the
exception of period and period uncertainty (for single transits), the TESS
name field, and the comments field.

=0.5in

Table 2: Example selection from the TOI catalog (Part1)

Source | Stellar | TIC | Full | Signal | TESS | TOI | EXOFOP | TIC RA | TIC Dec | $T_{\rm mag}$ |  $T_{\rm mag}$ Unc. | $V_{\rm mag}$ |  $V_{\rm mag}$ Unc. |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Pipeline | Catalog |  | TOI ID | ID | Name | Disp. | Disp. | (deg) | (deg) | (mag) | (mag) | (mag) | (mag) |
spoc | TICv7 | 281459670 | 110.01 | 1 | 110 b | KP | KP | 5.618692 | -59.942551 | 11.632 | 0.018 | 12.283 | 0.057 | …
spoc | TICv7 | 355703913 | 111.01 | 1 | 111 b | KP | KP | 0.774485 | -62.469342 | 13.129 | 0.018 | 13.857 | 0.137 | …
spoc | TICv7 | 388104525 | 112.01 | 1 | 112 b | KP | KP | 55.93344 | -65.193856 | 11.547 | 0.018 | 12.314 | 0.057 | …
spoc | TICv7 | 97409519 | 113.01 | 1 | 113 b | KP | KP | 332.714323 | -30.749674 | 12.143 | 0.018 | 12.724 | 0.092 | …
spoc | TICv7 | 25155310 | 114.01 | 1 | 114 b | KP | KP | 63.37389 | -69.226789 | 10.555 | 0.018 | 10.994 | 0.012 | …
spoc | TICv7 | 281541555 | 115.01 | 1 | 115 b | KP | KP | 6.702439 | -56.316124 | 12.98 | 0.018 | 13.783 | 0.126 | …
spoc | TICv7 | 238176110 | 116.01 | 1 | 116 b | KP | KP | 357.84526 | -70.152863 | 10.991 | 0.019 | 11.98 | 0.022 | …
qlp | Gaia DR2 | 322307342 | 117.01 | 1 | 117 b | PC | KP | 15.005896 | -58.90478 | 11.734 | 0.019 | 12.312 | 0.046 | …
spoc | TICv7 | 266980320 | 118.01 | 1 | 118 b | PC | CP | 349.556843 | -56.903885 | 9.154 | 0.017 | 9.809 | 0.003 | …
spoc | TICv7 | 278683844 | 119.01 | 1 |  | PC |  | 99.237983 | -58.015237 | 9.234 | 0.018 | 10.07 | 0.003 | …
spoc | TICv7 | 278683844 | 119.02 | 1 |  | PC |  | 99.237983 | -58.015237 | 9.234 | 0.018 | 10.07 | 0.003 | …

Note. — The TOI catalog table lists the parameters of each TOI and its host
star. This table (Part I) gathers together the stellar parameters selected
from the TOI catalog. The planet parameters continue on the table on the
following page.

=2.5in

Table 3: Example selection from the TOI catalog (Part 2)

| Epoch | Epoch | Orbital Period | Orbital Period | Transit | Transit Dur. | Transit Depth | Transit Depth | Sectors | Public | Alerted | Edited
---|---|---|---|---|---|---|---|---|---|---|---|---
| Val. (TJD) | Err. (TJD) | Val. (days) | Err. (days) | Duration (hrs) | Err. (hrs) | (ppm) | Err. (ppm) |  | Comment |  |
… | 1328.040464 | 0.000446 | 3.174305 | 4.40E-05 | 2.723339 | 0.030983 | 15603.6931 | 151.40271 | 1,2 | HATS-30 b | 2018-10-22 | 2020-08-03
… | 1326.106369 | 0.001084 | 2.106098 | 6.90E-05 | 1.574766 | 0.083967 | 12430.50297 | 528.39557 | 1,2 | HATS-34 b | 2018-10-22 | 2020-08-03
… | 1327.40960 | 0.000119 | 2.499804 | 2.47E-06 | 2.885454 | 0.0141 | 15075.418 | 53.067 | 1,2,3,4,7,11 | WASP-119 b | 2019-05-07 | 2020-08-03
… | 1327.053085 | 0.000623 | 3.372877 | 0.000147 | 2.63426 | 0.043644 | 17163.60419 | 212.40587 | 1 | WASP-124 b | 2018-09-05 | 2020-08-03
… | 1327.520787 | 0.000121 | 3.288783 | 2.30E-06 | 3.408833 | 0.014136 | 7061.865079 | 20.379316 | 1,2,…,13 | WASP-126 b | 2019-05-07 | 2020-08-03
… | 1329.90069 | 0.001812 | 4.742745 | 0.000262 | 2.408768 | 0.137056 | 13420.03179 | 517.9781 | 1,2 | HATS-46 b | 2018-10-22 | 2020-08-03
… | 1326.689274 | 0.00026 | 2.798594 | 4.90E-05 | 2.366459 | 0.022664 | 16879.44984 | 101.11358 | 1 | WASP-91 b | 2018-09-05 | 2020-08-03
… | 1328.4809 | 0.00133 | 3.5855 | 0.00038 | 3.173 | 0.123 | 6130 | 3.88166 | 1,2 | HATS-68 b | 2018-09-05 | 2020-08-03
… | 1329.199631 | 0.001235 | 6.036061 | 0.000637 | 2.123832 | 0.09961 | 1704.034261 | 56.202583 | 1 | HD 219666 b | 2018-09-05 | 2020-08-03
… | 1327.61510 | 0.002023 | 5.54109 | 2.30E-05 | 2.721801 | 0.194735 | 580.71457 | 20.75554 | 1,2,…,13 |  | 2019-05-07 | 2020-08-03
… | 1328.046433 | 0.001592 | 10.691632 | 0.000083 | 3.17746 | 0.415448 | 519.241028 | 26.40276 | 1,2,…,13 |  | 2019-05-17 | 2020-08-03

Note. — The TOI catalog table lists the parameters of each TOI and its host
star. This table (Part II) gathers together the planetary parameters selected
from the TOI catalog with the stellar parameters on a previous page.

###  9.2 Known Issues with TOI parameters

TOI parameters reflect the best possible analysis available at the time of
vetting. The “Comments” field in the TOI catalog often records remarks from
the vetting team on the reported parameters.

Common remarks include:

  * •

Ambiguous period (twice/half): the flagged transits in the light curve do not
clearly determine the correct period. The period may be a multiple or a
fraction of the reported period. This often happens when transits are missed
because of data gaps.

  * •

Revised epoch: the first transit flagged in the DV Reports is not the first
true transit in the light curve and has been revised in the TOI table.

  * •

Target period and epoch match with a nearby TCE

  * •

Transit is suspected to be due to an eclipse or transit on a nearby star
(often with an ambiguous centroid offset)

  * •

Two stars in the same pixel

  * •

Low SNR or a marginal candidate

  * •

Possible EB or stellar variability

  * •

Possible single transit

  * •

Possible grazing transit

  * •

V-shaped transit

###  9.3 Vetted TCE List

The TOI vetting team also maintains a larger TCE List of SPOC and QLP TCEs
considered for vetting, and includes the TOIs. The TCE list has the columns
listed below.

  * •

TIC ID

  * •

Candidate ID. Identifier for the signal in the light curve (1, 2, etc.)

  * •

Full TOI ID. Optional, if available.

  * •

Epoch Barycentric-corrected epoch of first transit in TESS Julian Days.
$\text{BTJD}=\text{BJD}-2457000$ 252525Tenenbaum & Jenkins (2018). Includes
uncertainty.

  * •

Orbital Period In days. Can be blank, as is the case for single transits.
Includes uncertainty.

  * •

TCE Disposition Optional.

  * •

Comment Optional. Describes provenance and reasoning behind the disposition
assigned.

  * •

Sector Tag Identifies in which sector and vetting collection a candidate
appeared.

  * •

Updated Date of most recent update of candidate parameters.

The TCE list is available online alongside the TOI
list262626https://tess.mit.edu/toi-releases/. Separately, MAST archives all
the TCEs the SPOC pipeline finds for each sector and multi-sector
run272727https://archive.stsci.edu/tess/bulk_downloads/bulk_downloads_tce.html.
The SPOC TCE list is part of the public data release for each sector.

##  10 Discussion

We now turn to a qualitative description of the planet candidates in the TOI
list The TOI list is not corrected for completeness so the conclusions we can
draw about the underlying planet populations are limited. Nevertheless, we do
present some figures of TOI parameter space distributions. The positions of
the 2241 TOIs released in the TESS Prime Mission are shown in Figure 5.

Figure 5: Sky positions of the TESS Prime Mission TOIs. RA is on the
horizontal axis and declination is on the vertical axis. The black points are
TOIs. The blue points are previously known planets (NASA Exoplanet Archive,
accessed 14 August 2020). The points within the multi-colored swaths are stars
($T_{\rm mag}<13.5$) observed in the TESS Prime Mission. Fewer TOIs appear in
Sectors 11 and 12 because the field of view was crowded and this limited the
number of viable TCEs for vetting.

###  10.1 TOI Host Stars

The TOIs are found in a diverse stellar population, from hot A stars to cool M
dwarf stars. The majority of the TOI host stars are on the main sequence,
while some are sub-giants (Figure 6).

TOIs appear around primarily FGK stars, as shown in the spectral type
distribution in Figure 7. Short-period TOIs predominate across all spectral
types, as shown in Figure 8. Figure 9 shows that for M and K-type stars, small
TOIs (Neptune-sized and smaller) predominate, whereas for F, and A-type stars,
TOIs with large radii are more common. This distribution is expected, as the
transit depth TESS measures for a planet of a certain radius is shallower for
larger stars than for smaller stars; small planets are more difficult to
detect around larger stars.

Figure 6: TOI host stars on the Gaia color-magnitude diagram. The horizontal
axis is the difference between the Gaia DR2 magnitudes in the $G_{RP}$ and
$G_{BP}$ bands and the vertical axis is the absolute G magnitude. The blue
points are the bright stars (brighter than $T_{\rm mag}$ = 13.5) TESS observed
in the Prime Mission matched in Gaia DR2. The same sample of stars is plotted
as points in multi-colored swaths in Figure 5. The grey points are TOIs. The
grey X’s are TOIs which have been found to be false positives from follow-up
observing and revised vetting. The majority of the TOI host stars fall on the
main sequence. All the TOIs identified for giant stars brighter than the red
clump have proven to be false positives, as expected. Figure 7: Spectral type
distribution for TOI host stars. The number of TOIs for each spectral type
(M,K,G,F,A) is on the vertical axis. The grey bars are the Barclay (2020)
predicted yield for each spectral type for stars brighter than ${T_{\rm
mag}}=10.5$. The orange bars are the numbers of TOIs actually identified in
the TESS Prime Mission for each spectral type. False positive TOIs have been
removed. The TOIs roughly track to the abundance of each spectral type, but
the data are not corrected for completeness. Figure 8: Period distribution for
TOIs, subdivided by host star spectral type (M,K,G,F,A). False positive TOIs
and TOIs with $P=0$ are not included. For each spectral type, TESS has
detected many TOIs in the 1-10 day period regime. These data are not corrected
for completeness. Figure 9: Radius distribution for TOIs, subdivided by host
star spectral type (M,K,G,F,A). False positive TOIs and TOIs with no $R_{p}$
are not included. The majority of TOIs on M and K-type stars are sub-Neptunes,
demonstrating TESS’s ability to detect small planets around small stars. These
data are not corrected for completeness.

###  10.2 TOI Planet Parameters

In Figure 10, we plot the TOIs in period-radius space. We include the
simulated planet distribution from Barclay (2020) to compare to the TOI
catalog. We only include predicted planets with ${T_{\rm mag}}<10.5$ host
stars, as the vetting team only searches for TOIs for QLP targets above this
magnitude threshold. The period and radius distributions for the TOIs, the
Barclay (2020) prediction, and the false positives from follow-up as of 8 Oct.
2020 are shown at the top and right of Figure 10. There are 820 TOIs with
$R_{p}<6{R_{\oplus}}$ out of 1625 with calculated planet radius values. The
majority of the TOIs have periods less than 30 days, which is expected
considering the single-sector baseline is 27 days. The median period is 4.5
days and the median planet radius is 5 $R_{\oplus}$. The TOIs appear to be in
in good qualitative agreement with the predicted yield estimations by Barclay
(2020). The majority of the false positives (plotted in grey in Figure 10)
appear at $P<6$ days and $R_{p}>5$ $R_{\oplus}$. This is expected due to the
contribution of nearby or on-target eclipsing binaries.

Looking at the TOIs in period-$T_{eq}$ space in Figure 11, we see that the TOI
Catalog and TESS confirmed planets are contributing to the population of
temperate planets orbiting their host stars at short periods ($P<10$ days).

Figure 10: TOIs in period-radius space. The period in days is on the
horizontal axis and the planet radius in Earth radii is on the vertical axis.
The solid dark orange circles are TESS planets confirmed either with a mass
measurement or another validation method. The red diamonds are known planets
from other surveys re-detected by TESS. The open orange circles are TOI planet
candidates excluding confirmed TESS planets, known planets, and false
positives. The light blue open circles are the predicted TESS planet yield
from Barclay (2020) for stars brighter than ${T_{\rm mag}}=10.5$. The banding
in the simulated points, for example at $4{R_{\oplus}}$, is an artifact of the
period-radius bins in which occurrence rates were calculated. The top
histogram shows the distribution of TOIs in period space. The histogram at
right shows the distribution of TOIs in radius space. The TOIs, including
known planets and confirmed TESS planets, are the orange histogram. The
predicted planet yield from Barclay (2020) is in light blue, and the false
positives as of 8 Oct. 2020 are in grey.  Figure 11: Orbital period vs. planet
equilibrium temperature. The planet orbital period in days is on the
horizontal axis and the planet $T_{eq}$ is on the vertical axis. The open
orange circles are planet candidate TOIs which have not been confirmed. The
solid dark orange circles are planets confirmed either with a mass measurement
or are validated by other means. The red diamonds are TOIs which were known
planets prior to the TESS mission. The light blue open circles are known
planets from outside the TESS mission (NASA Exoplanet Archive, accessed 8 Oct.
2020). Mercury, Venus, and Earth are visible in the bottom right. The TOI
catalog has greatly increased the number of temperate, short-period planet
candidates available for follow-up.

###  10.3 Follow-Up Observations of TOIs

TESS aims to provide small planets around bright stars that can be
characterized in-depth via follow-up observations within three years of the
survey onset. TESS is delivering on its promise, by discovering many small
planet candidates within 100 pc of the solar system (see Figure 12). Over the
course of the TESS Prime Mission, 2241 TOIs have been sorted through by
follow-up observers, 654 of which are planet candidates with radii smaller
than $4R_{\earth}$, so there will be no shortage of viable targets for mass
measurements.

The majority of the TOIs in the catalog are accessible to RV follow-up, as
shown in Figure 13. To date, 25 planets282828NASA Exoplanet Archive, accessed
8 Oct. 2020 discovered by TESS smaller than 4 $R_{\oplus}$ have published mass
measurements in peer-reviewed publications and dozens more such measurements
are underway. An additional 21 larger planets29292928 have measured masses,
again with many more mass measurements underway. With numerous new planet
candidates and confirmed planets added to the TOI catalog in the Prime
Mission, TESS has made progress towards its primary goal, to deliver fifty
small ($R_{p}<4{R_{\oplus}}$) planets with measured masses to the community.

The TOI catalog also identifies planets which will be candidates for in-depth
atmospheric characterization with transmission and emission spectroscopy. Many
of the small planets TESS discovered have host stars with apparent H-band
magnitudes brighter than 9 (Figure 14) and for fainter host stars, have large
transit depths.

Figure 15 shows the transmission spectroscopy metric (TSM) (Kempton et al.,
2018) values for TOIs in a range of radius bins. The TSM is the signal-to-
noise expected for spectral features in a transmission spectrum. Kempton et
al. (2018) estimated TSM threshold values that would provide a statistical
sample of the best TESS targets across the full radius range for the JWST
observations, based on the predicted TESS yield from Sullivan et al. (2015).
For a quantitative calculation of the observing time needed to reach a certain
TSM with JWST, see Kempton et al. (2018); Louie et al. (2018). Figure 15 shows
updated TSM threshold values from the Prime Mission TOI catalog. Although the
updated TSM thresholds are lower than predicted for sub-Neptunes (with radii
below $4{R_{\oplus}}$), this may be in part due to an overprediction of the
number of short-period planets in yield estimations. With the current TOI
catalog, more observing time than expected may be required to characterize a
statistical sample of small planets. However, additional planets may be found
with further vetting. The highest TSM planets have already provided a rich
source of targets suitable for atmospheric characterization with HST: pi Men c
(King et al., 2020); LTT 1445A b (Sing et al., 2019); TOI 1726.01 and 1726.02
(Zhang et al., 2020); AU Mic b (Newton et al., 2019a; Cauley et al., 2020);
TOI-674 b (Crossfield et al., 2017); DS Tuc A b (Dos Santos et al., 2020); HD
219666 b (Beatty et al., 2019; Fu et al., 2019); TOI-270 c and d (Mikal-Evans
et al., 2019); TOI-1231b (Kreidberg et al., 2020); and LP 98-59 b, c, and d
(Barclay et al., 2019).

Figure 12: Planet radius (in ${R_{\oplus}}$) vs. host star distance (in pc).
Solid dark orange circles are TESS planets confirmed either with a mass
measurement or other validation method; orange open circles are TOIs (with
known false positives removed); the solid red diamonds are known planets from
other surveys re-detected by TESS; and blue open circles are known exoplanets
from outside the TESS mission (NASA Exoplanet Archive, accessed 8 Oct. 2020).
Point size is scaled by transit depth. Figure 13: Estimated RV semi-amplitudes
for TOIs. RV semi-amplitude (in meters per second) is on the vertical axis and
TESS magnitude is on the horizontal axis. False positives (of all sizes) from
follow-up and revised vetting are small gray points in the background. Planets
(both TESS confirmed and known planets) and TESS planet candidates which are
smaller than $4{R_{\oplus}}$ are filled circles; those larger than
$4{R_{\oplus}}$ are open circles. The color bar is host star $T_{\rm eff}$.
The semi-amplitude calculation uses estimated planet masses derived from radii
based on the probabilistic Chen and Kipping mass-radius relation (Chen &
Kipping, 2017) and uses stellar masses and radii from TICv8 when available.
For radii not in TICv8, we use Gaia DR2 or assume 1 $R_{\odot}$. If the
stellar mass is not in TICv8, it is approximated to equal the radius of the
star, with the exception of evolved stars, for which we assign a maximum
stellar mass of 1.5 $M_{\odot}$. The dashed line indicates an approximate
threshold for measurable RV signals, using 1 m/s as the lower limit for
detectability, increasing with photon noise for fainter host stars. Most TESS
candidates fall above this threshold for mass measurement, but we note that in
practice, stellar activity correction will be needed for many of the smallest
signals.  Figure 14: Host star magnitude (in Hmag) vs. planet radius (in
$R_{\earth}$). The solid dark orange circles are TESS planets confirmed either
with a mass measurement or validated by other means; the open orange circles
are TOIs (non-validated, but with known false positives removed); the solid
red diamonds are known planets from other surveys re-detected by TESS; and the
pale blue open circles are known exoplanets (dominated by validated planets,
i.e. without mass measurements) from outside the TESS mission (NASA Exoplanet
Archive, accessed 8 Oct. 2020). The point size is scaled by transit depth.
TESS is discovering planet candidates with bright host stars amenable to
follow-up observations. We plot the host star’s $H_{\rm mag}$ because
currently, atmospheric follow-up with the HST Wide Field Camera 3 instrument
is often carried out in the H band. Figure 15: The Transmission Spectroscopy
Metric (TSM) (Kempton et al., 2018) as a function of planet radius for TESS
candidate planets from the Prime Mission (including known planets, but
excluding false positives). The solid blue lines indicate the threshold TSM
required to obtain a sample of 37 terrestrial planets, 100 small sub-Neptunes,
100 large sub-Neptunes, and 50 sub-Jovians over the entire TESS mission. The
dashed blue lines indicate the predicted threshold TSM from Kempton et al.
(2018). These results suggest that the TSM values for TESS sub-Neptunes
($R_{p}<4R_{\oplus}$) are lower than predicted by (Kempton et al., 2018), and
that more observing time than expected may be required to build a statistical
sample of planets in each category. The difference between the actual and
predicted values, however, may be in part due to an overprediction of short-
period planets in the yield simulations. The actual TSM values may shift as
the TESS data continue to be vetted and more TOIs are found.

###  10.4 Previously-Known Exoplanets in the TOI Catalog

By systematically observing the sky, TESS is monitoring many of the stars
already known to harbor exoplanets (blue stars in Figure 5). For known
transiting exoplanets, TESS provides improved transit ephemerides. The
improvements reduce the transit timing uncertainty in the JWST era from tens
of minutes down to a few minutes. The TOI catalog includes 256 Known Planet
TOIs observed in the Prime Mission.

The known exoplanet host stars were included as a special catalog in the
originally released version of the TIC (Stassun et al., 2018, 2019). Several
selected programs from the TESS Guest Investigator Program for Cycle 1 and
Cycle 2 included known exoplanet host stars, ensuring that they would be
monitored with two-minute cadence by TESS.

Using data from the NASA Exoplanet Archive303030Accessed 8 Oct. 2020 (Akeson
et al., 2013) and cross-matching with the TESS camera locations, we estimate
that $\sim 920$ known hosts of transiting exoplanets brighter than $T_{\rm
mag}$ = 13.5 were observed by TESS during the Prime Mission. We detected 251
of the 1,543 total known planets around ${T_{\rm mag}}<13.5$ host stars
originally detected via the transit method. This smaller number is expected,
as many of these known planets were discovered by Kepler and the SNR for
transits of many of these planets is too low for TESS. We released TOIs for 5
of the 864 known planets detected via other methods and orbiting host stars
with ${T_{\rm mag}}<13.5$. We expect the number of TOIs initially detected by
radial velocity measurements to be low because of low transit probabilities
for radial velocity targets (Dalba et al., 2019; Kane et al., 2021).

###  10.5 Multi-Planet Systems and Multi-Planet False Positive Probability

Figure 16: Multi-planet TOI systems. The horizontal axis is period in days,
and the TOIs are ordered from longest to shortest minimum period. The color of
each planet marker maps to the effective temperature of each system’s host
star and the planet markers are scaled by planet radius. The multi-planet
systems TESS has discovered have host stars across a wide range of spectral
types.

The TOI planet candidates includes several dozen systems containing up to four
planet candidates each. This is despite TESS’s shorter observing baseline
compared to Kepler and K2. The architectures of the TOI multi-planet systems
are summarized in Figure 16.

Early on in the Kepler mission, Latham et al. (2011); Lissauer et al. (2011)
noticed that planet candidates in systems with multiple transiting planet
candidates (i.e. multi-transiting systems) were considerably more likely to be
genuine transiting exoplanets than those in single planet candidate systems
(i.e. singly-transiting systems). This observation was formalized into a
statistical framework (Lissauer et al., 2012) and used in the validation of
hundreds of planetary candidates in multi-transiting systems (Lissauer et al.,
2014; Rowe et al., 2014). Here, we investigate whether multi-transiting
systems discovered by TESS are similarly more likely to be genuine transiting
planets than their singly-transiting counterparts.

We followed a procedure similar to Lissauer et al. (2012) to determine how
much more likely multi-transiting candidates are to be true positives than
singly-transiting candidates. In particular, we calculated a “multiplicity
boost” - a multiplicative factor that can be applied to the false positive
probability of any given transiting planet candidate to reflect the a priori
higher likelihood of a candidate in a multi-transiting system to be a real
planet.

We consider only TOIs found by the SPOC pipeline because the search target
list for these TOIs is well-defined. Over the Prime Mission, TESS observed
232,705 unique stars for at least one sector at two-minute cadence. From the
stars observed at two-minute cadence, the TOI process identified 1023 total
planet candidates in 930 systems. There were 855 single-planet-candidate
systems, 61 systems with two planet candidates, 10 with three candidates, and
4 with four candidates.

The multiplicity boost is given by the ratio of probability any star is a TOI
and the probability that any TOI has more than one planet candidate. In this
case, the multiplicity boost is roughly (75/930) / (930/232705) $\approx 20$.
This is lower than the multiplicity boost estimated from both Kepler and K2
(about 30, Lissauer et al., 2012; Sinukoff et al., 2016; Vanderburg et al.,
2016b), likely because of TESS’s higher false positive rate (due to larger
pixels), and the increased difficulty in detecting and disentangling multi-
transiting systems given TESS’s shorter observing baselines.

The multiplicity boost is stronger when we restrict our analysis to only
planets smaller than 6 $R_{\oplus}$, which are intrinsically more common and
therefore have lower false alarm rates. From the two-minute postage stamps, we
identified 630 planet candidates smaller than 6 $R_{\oplus}$in 545 systems.
The majority of TESS multi-planet systems (69/76) have more than one small
planet candidate: 56 systems with two planet candidates, 9 with three, and 4
with four planet candidates. Calculating the multiplicity boost for this set
of smaller planet candidates gives (69/545)/(545/232705) $\approx$ 54\. The
presence of multiple small planet candidates is a strong indicator that the
system is genuine.

We advise caution when applying the multiplicity boost to candidate multi-
transiting systems in crowded regions like the galactic plane because the
likelihood of many false positives in the same pixel is higher. Our
multiplicity boost was calculated as an average over the whole sky but is
likely lower in crowded regions. We also note that these estimates for the
multiplicity boost do not apply to planet candidates detected only by the QLP.
Because the QLP searches all stars observed in the full frame images,
regardless of their evolutionary state and suitability for detecting
transiting planets (such as red giants), QLP planet candidates have a higher
false positive rate. Any multiplicity boost present for multi-planet systems
detected only by the QLP will likely be much weaker than the boost for SPOC
pipeline-detected multi-planet systems. So far, the QLP has only detected one
system with multiple candidates (TOI 1130, Huang et al. 2020c) for which the
stars were not pre-selected for two-minute cadence observations and searched
by the SPOC pipeline, so we cannot estimate a multiplicity boost for
candidates only detected by the QLP.

###  10.6 Single Transits

Planet candidates which transit only once during TESS observations are of
interest because they can be followed up to yield rare, long-period transiting
planets. The QLP and SPOC pipelines search for at least two transits to make a
TCE and therefore do not purposefully generate a list of single-transit planet
candidates. However, occasionally the pipeline finds a single transit and
folds it on a transit-like noise feature elsewhere in the light curve. When
this happens, we include these single transit events in the TOI Catalog with
$P=0$. It is possible for a TOI which is a single-transit planet candidate
with a single sector of data to be updated with a measured period if
additional transits are found in later sectors.

There are separate, dedicated efforts to purposefully identify and follow-up
single transits from the QLP and SPOC and also from these independent
searches. In the latter case of single-transit planet candidates from
independent searches, these candidates fall into the category of community
TOIs.

The follow-up efforts for single-transit planet candidates fall into two
categories. The first is photometric follow-up of a list of target stars with
single transits (for example Cooke et al., 2018). The second is a dedicated
radial-velocity campaign for a list of target stars, using an initial estimate
of the period (Seager & Mallén-Ornelas, 2003) by using transit and stellar
properties and assuming a circular orbit (e.g., Villanueva et al., in prep.).

###  10.7 Some Notable TESS Firsts

Here we present a few highlights of TESS exoplanet discoveries, selected
because they are TESS “firsts” or appear in an otherwise scarce parameter
space in stellar parameters or planet parameters.

#####  $\pi$ Mensae c

The mission’s first planet discovery (Huang et al., 2018b), $\pi$ Men c (TOI
144), delivered on TESS’s promise to find small planets around bright stars.
$\pi$ Men c orbits a very bright host star (${V_{\rm mag}}=5.7$). The planet
is a sub-Neptune with an average density too low
($M=4.82_{-0.86}^{+0.84}~{}M_{\oplus}$, $R=2.042\pm 0.050~{}R_{\oplus}$,
$\rho={2.97}_{-0.55}^{+0.57}$ $\rho_{\oplus}$) for the planet to be
predominately rocky. Instead the planet likely has a thick envelope. The quick
$\pi$ Mensae c mass determination was possible due to a rich radial-velocity
data set already archived (Jones et al., 2002). The system was observed in
multiple sectors in TESS Year 1. Additional observations and analysis have
provided more precise mass measurement for planets b and c, their large orbit
misalignment, and possible theories for the formation of the system (Kane et
al., 2020; Xuan & Wyatt, 2020; Kunovac Hodžić et al., 2020; Damasso et al.,
2020; De Rosa et al., 2020). Although $\pi$ Men is a bright star, it is a
viable candidate for JWST observation.

##### TOI 125

TOI 125 is a three-planet system (Quinn et al., 2019) and was TESS’s first
multi-planet system released. The system contains three sub-Neptune planets,
and two possible additional small planets detected in further analysis with
low ($5.2$-$\sigma$ and $5.1$-$\sigma$) signal-to-noise. The system orbits a
K0 dwarf (V=10.9).

##### TOI 700 d

TOI 700 d is the first TESS discovery of an Earth-sized planet in the
habitable zone of its star. The host star is an M2 red dwarf star. The planet
is $1.144_{-0.061}^{+0.062}{R_{\oplus}}$ with a period of $P=37.42$ days and
an equilibrium temperature of $T_{eq}\sim 269$ K (Rodriguez et al., 2020;
Gilbert et al., 2020; Suissa et al., 2020). The system has two other Earth-
sized planets interior to TOI 700 d
$(R_{b}=1.037_{-0.064}^{+0.065}{R_{\oplus}}$ and
$R_{c}=2.65_{-0.15}^{+0.16}{R_{\oplus}})$ and lies in the southern TESS
continuous viewing zone. The system was one of the last targets _Spitzer_
observed.

##### TOI 1338

TOI 1338 b is the first TESS circumbinary planet (Kostov et al., 2020). The
planet orbits two stars with masses $1.1M_{\odot}$ and $0.3M_{\odot}$ and a
14.6-day orbit. The planet has a nearly-circular orbit, an orbital period of
95.2 days and a radius of $6.85\pm 0.19{R_{\oplus}}$. The discovery relied on
visual inspection to identify the planet transits as distinct from the
secondary transits of the eclipsing binary.

##### LHS 3844 b

The first ultra-short-period (USP) planet from TESS, LHS 3844 b (Vanderspek et
al., 2019) is a hot super-Earth orbiting its host star every 11 hours. The M
dwarf host star is 14.9 parsecs away. This candidate demonstrated TESS’s
ability to detect small planets around nearby small stars. LHS 3844 b was
later found to likely be a bare rock planet with little or no detectable
atmosphere (Kreidberg et al., 2019).

##### HD 21749

TOI 186.01, or HD 21749 b, is TESS’s first long-period planet, with $P=35.61$
days (Dragomir et al., 2019). The host star is a bright ($V_{\rm mag}$=8.5) K
dwarf at 16 parsecs distance. TOI 186.01 was initially a single-transit TOI in
Sector 1 until additional transits occurred in following sectors. A second,
shorter-period planet, TOI 186.02, or HD 21749 c, (P=7.9 days) appeared in
multi-sector analysis of SPOC pipeline postage stamp data from Sectors 1-3.
TOI 186.02 is the first Earth-sized planet ($R_{p}=0.892{R_{\oplus}}$) from
TESS. HD 21749b is sub-Neptune-sized
($R_{p}=2.61_{-0.16}^{+0.17}{R_{\oplus}}$). The two planets are on either side
of the radius valley (Fulton et al., 2017).

##### TOI 200.01

DS Tuc A b (Newton et al., 2019b) is TESS’s first young planet ($\tau\sim 45$
Myr). The host star, DS Tuc A, is part of a bright ($V_{\rm mag}$=8.5) visual
binary in the young ($\tau\sim$ 45 Myr) Tucana-Horologium association. The
planet is $5.86\pm 0.17{R_{\oplus}}$ with an 8.1 day period, a rare size-
period combination. This young planet can be a point of comparison against
older planets with future measurements of its mass and atmosphere.

##### TOI 704

TOI 704 b, or LHS 1815 b, is TESS’s first thick disk star with a transiting
planet (Gan et al., 2020). In other words, TOI 704 b’s host star has a much
higher expected maximal height ($Z_{max}$ = 1.8 kpc) above the Galactic plane
as compared to other TESS planet host stars. This TOI will enable studies on
whether interior structure and atmospheric properties of a thick disk planet
differs from planets in the thin disk (or Galactic halo). TOI 704 b transits
an M dwarf star with $P=3.8$ days and radius $1.088\pm 0.064{R_{\oplus}}$ with
a $3-\sigma$ mass upper limit of 8.7 ${M_{\oplus}}$.

##### TOI 813

Members of the public can label transit-like features of TESS light curves on
the Planet Hunters TESS platform313131https://www.planethunters.org. TOI 813 b
was the first TESS planet discovered by Planet Hunters TESS (Eisner et al.,
2020). The Planet Hunters TESS team identified the candidate and released it
as a community TOI. The planet orbits a bright, sub-giant star at a long
orbital period of 83.4 days and has a radius of $6.71\pm 0.38{R_{\oplus}}$.

###  10.8 Some Notable TESS Exoplanets

In addition to being TESS firsts, a few planets are very unusual and deserve
special attention.

##### TOI 1690 (WD 1856+534)

TOI 1690 b is a giant planet candidate transiting a white dwarf star
(Vanderburg et al., 2020). TOI 1690 b is a landmark discovery because its
existence demonstrates that planets can survive migration into orbits close to
white dwarf stars. With an orbital period of 1.4 days, TOI 1690 b
($R_{p}=10.4\pm 1{R_{\oplus}}$, $M_{p}\lesssim 14M_{Jupiter}$) is close enough
to its white dwarf host star that it must have formed at larger planet-star
separations in order to avoid destruction when the white dwarf precursor star
evolved into a red giant star.

##### TOI 849

TOI 849 b is a dense and unusually massive planet
($40.8_{-2.5}^{+2.4}{M_{\oplus}}$) for its size (smaller than Neptune,