# The TESS-Keck Survey: Science Goals and Target Selection

Ashley Chontos NSF Graduate Research Fellow  Institute for Astronomy,
University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA  Joseph M.
Akana Murphy NSF Graduate Research Fellow  Department of Astronomy and
Astrophysics, University of California, Santa Cruz, CA 95064, USA  Mason G
MacDougall Department of Physics & Astronomy, University of California Los
Angeles, Los Angeles, CA 90095, USA  Tara Fetherolf Department of Earth and
Planetary Sciences, University of California, Riverside, CA 92521, USA  Judah
Van Zandt Department of Physics & Astronomy, University of California Los
Angeles, Los Angeles, CA 90095, USA  Ryan A. Rubenzahl NSF Graduate Research
Fellow  Department of Astronomy, California Institute of Technology, Pasadena,
CA 91125, USA  Corey Beard Department of Physics & Astronomy, University of
California Irvine, Irvine, CA 92697, USA  Daniel Huber Institute for
Astronomy, University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA
Natalie M. Batalha Department of Astronomy and Astrophysics, University of
California, Santa Cruz, CA 95060, USA  Ian J. M. Crossfield  Department of
Physics & Astronomy, University of Kansas, 1082 Malott,1251 Wescoe Hall Dr.,
Lawrence, KS 66045, USA  Courtney D. Dressing Department of Astronomy,
University of California Berkeley, Berkeley CA 94720, USA  Benjamin Fulton
NASA Exoplanet Science Institute/Caltech-IPAC, MC 314-6, 1200 E California
Blvd, Pasadena, CA 91125, USA  Andrew W. Howard Department of Astronomy,
California Institute of Technology, Pasadena, CA 91125, USA  Howard Isaacson
Department of Astronomy, University of California Berkeley, Berkeley CA 94720,
USA  Centre for Astrophysics, University of Southern Queensland, Toowoomba,
QLD, Australia  Stephen R. Kane Department of Earth and Planetary Sciences,
University of California, Riverside, CA 92521, USA  Erik A. Petigura
Department of Physics & Astronomy, University of California Los Angeles, Los
Angeles, CA 90095, USA  Paul Robertson Department of Physics & Astronomy,
University of California Irvine, Irvine, CA 92697, USA  Arpita Roy Space
Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
Department of Physics and Astronomy, Johns Hopkins University, 3400 N Charles
St, Baltimore, MD 21218, USA  Lauren M. Weiss Institute for Astronomy,
University of Hawai‘i, 2680 Woodlawn Drive, Honolulu, HI 96822, USA  Aida
Behmard NSF Graduate Research Fellow  Division of Geological and Planetary
Science, California Institute of Technology, Pasadena, CA 91125, USA  Fei Dai
Division of Geological and Planetary Sciences 1200 E California Blvd,
Pasadena, CA, 91125, USA  Paul A. Dalba NSF Astronomy and Astrophysics
Postdoctoral Fellow  Department of Earth and Planetary Sciences, University of
California, Riverside, CA 92521, USA  Steven Giacalone Department of
Astronomy, University of California Berkeley, Berkeley, CA 94720, USA
Michelle L. Hill Department of Earth and Planetary Sciences, University of
California, Riverside, CA 92521, USA  Jack Lubin Department of Physics &
Astronomy, University of California Irvine, Irvine, CA 92697, USA  Andrew Mayo
NSF Graduate Research Fellow  Department of Astronomy, University of
California Berkeley, Berkeley, CA 94720, USA  Teo Močnik Gemini
Observatory/NSF’s NOIRLab, 670 N. A‘ohoku Place, Hilo, HI 96720, USA  Alex S.
Polanski  Department of Physics & Astronomy, University of Kansas, 1082
Malott,1251 Wescoe Hall Dr., Lawrence, KS 66045, USA  Lee J. Rosenthal
Department of Astronomy, California Institute of Technology, Pasadena, CA
91125, USA  Nicholas Scarsdale Department of Astronomy and Astrophysics,
University of California, Santa Cruz, CA 95060, USA  Emma V. Turtelboom
Department of Astronomy, University of California Berkeley, Berkeley CA 94720,
USA Ashley Chontos<EMAIL_ADDRESS>
###### Abstract

Space-based transit missions such as Kepler and TESS have demonstrated that
planets are ubiquitous. However, the success of these missions heavily depends
on ground-based radial velocity (RV) surveys, which combined with transit
photometry can yield bulk densities and orbital properties. While most Kepler
host stars are too faint for detailed follow-up observations, TESS is
detecting planets orbiting nearby bright stars that are more amenable to RV
characterization. Here we introduce the TESS-Keck Survey (TKS), an RV program
using $\sim$100 nights on Keck/HIRES to study exoplanets identified by TESS.
The primary survey aims are investigating the link between stellar properties
and the compositions of small planets; studying how the diversity of system
architectures depends on dynamical configurations or planet multiplicity;
identifying prime candidates for atmospheric studies with JWST; and
understanding the role of stellar evolution in shaping planetary systems. We
present a fully-automated target selection algorithm, which yielded $103$
planets in $86$ systems for the final TKS sample. Most TKS hosts are inactive,
solar-like, main-sequence stars ($4500$ K $\leq$ $\rm T_{eff}$ $<6000$ K) at a
wide range of metallicities. The selected TKS sample contains 71 small planets
($R_{\mathrm{p}}$ $\leq$ 4 $R_{\oplus}$), 11 systems with multiple transiting
candidates, 6 sub-day period planets and 3 planets that are in or near the
habitable zone ($\rm S_{inc}$$\leq$ 10 $\rm S_{\oplus}$) of their host star.
The target selection described here will facilitate the comparison of measured
planet masses, densities, and eccentricities to predictions from planet
population models. Our target selection software is publicly
available111https://github.com/ashleychontos/sort-a-survey and can be adapted
for any survey which requires a balance of multiple science interests within a
given telescope allocation.

surveys – telescopes – catalogs – techniques: photometric, spectroscopic,
radial velocities – methods: statistical, observational – stars: fundamental
parameters, statistics – planets and satellites: detection

††facilities: MAST, TESS, Keck I: HIRES††software: All code used in this paper
is available at https://github.com/ashleychontos/sort-a-survey. We made use of
the following publicly-available Python modules: astroquery, exoplanet
(Foreman-Mackey, 2019), evolstate (Huber, 2017; Berger et al., 2018), and
tesspoint.

##  1 Introduction

Measurements of planet sizes in combination with masses, via the transit and
radial velocity (RV) methods, continues to be the most fruitful synergy for
investigating the properties (in particular the bulk compositions) of
exoplanets. Early landmark discoveries using both transits and RVs included
the first rocky exoplanets, Kepler 10-b (Batalha et al., 2011) and CoRoT-7-b
(Léger et al., 2011), as well as the density transition at 1.5 $R_{\oplus}$
(Weiss & Marcy, 2014), separating primarily rocky planets from the lower
density, volatile-rich sub-Neptune-sized planets (Rogers, 2015).

Kepler identified thousands of new transiting candidates (Borucki et al.,
2011a, b; Batalha et al., 2013; Burke et al., 2014; Rowe et al., 2015;
Mullally et al., 2015; Coughlin et al., 2016; Thompson et al., 2018), but most
targets were faint and therefore had limited constraints from spectroscopic RV
surveys. Fortunately this has been mitigated with TESS (Transiting Exoplanet
Survey Satellite; Ricker et al., 2015), a nearly all-sky survey looking for
transiting planets orbiting bright nearby stars. The first TESS discovery of a
rocky super-Earth orbiting the naked-eye star $\pi$ Men (Huang et al., 2018)
was a prime example of the goals from the new mission.

A fundamental goal of exoplanet demographics is to compare observed
populations of exoplanet properties to population synthesis models in order to
inform planet formation theories (Mordasini, 2018). This was accomplished with
Kepler for planet radii (Mulders et al., 2019) and is now becoming possible
for densities with TESS, but the selection function for RV follow-up is
typically much more complex than for transit surveys. For example, RV surveys
frequently drop stars with rapid rotation or increased stellar activity, which
is typically done on a case-by-case basis and is thus difficult to reproduce
in later studies.

Large exoplanet surveys such as TESS provide the statistical insights required
to test different formation and evolution theories to observed planet
distributions, but hinges on the ability to reproduce RV survey selection
biases. Therefore, a critical ingredient for the realization of these surveys
is understanding the process for which targets were initially selected.
Indeed, recent ground-based TESS follow-up programs such as the Magellan-TESS
Survey (Teske et al., 2020) have begun to describe target selection functions,
providing a pathway to properly correct for survey biases or incompleteness.

Here we introduce the TESS-Keck Survey (TKS), a collaboration between the
California Institute of Technology, the University of California (Berkeley,
Irvine, Los Angeles, Riverside, Santa Cruz), the University of Hawai’i, the
University of Kansas, NASA, the NASA Exoplanet Science Institute and the W. M.
Keck Observatory. The survey is being conducted using the High-Resolution
Spectrograph (HIRES), which is mounted on the Keck I telescope at the W. M.
Keck Observatory on the summit of Maunakea in Hawai’i. Building on the
legacies of Kepler and K2, TKS will leverage the new population of transiting
planets orbiting nearby, bright stars to address major outstanding questions
in exoplanet astronomy. We discuss the detailed vetting and target selection
steps taken to converge on a definitive target list, including a fully-
automated target selection algorithm. We conclude by presenting our complete
target sample and summarize some of the general population characteristics.

##  2 Survey Description

###  2.1 TKS Science

TKS is structured around several science goals related to the compositions,
architectures and atmospheres of exoplanets (see Table 1). The following
subsections provide a brief review of the science motivations for the survey.

Table 1: TESS-Keck Survey Science Summary Theme |  Science Case |  ID∗ |  Description
---|---|---|---
Bulk Compositions |  Planet Radius Gap |  1A |  Probing compositions across the planet radius gap to constrain the physical mechanism(s) causing the bimodal radius distribution
|  Stellar Flux & Gaseous Envelopes |  1B |  Analyzing the diversity of gaseous envelopes and their dependence on properties like stellar mass, insolation flux, activity level, and other properties
|  Ultra-Short Period Planets |  1C |  Using ultra-short period planet compositions as a window into the refractory cores of small planets
|  Habitable-Zone Planets |  1D |  Identifying and characterizing planets orbiting in the habitable zones of their host star
|  Planet-Star Correlations |  1E |  Exploring dependencies of bulk planet properties with different stellar properties like $M_{\star}$, $\rm{[Fe/H]}$, age, etc.
Architectures & Dynamics |  Distant Giants |  2A |  Understanding the occurrence and connection between close-in small planets and distant giant planets
|  Eccentricities |  2Bi |  Characterizing the eccentricities of sub-Jovian planets to elucidate the possible formation and/or evolution pathways for dynamically hot planets
|  Obliquities |  2Bii |  Measuring spin-orbit (mis)alignments of previously unexplored parameter spaces to trace formation histories and past dynamical interactions
|  Multis |  2C |  Examining the diversity and/or uniformity of properties for planets in multi-planet systems
Atmospheres |  |  3 |  Identifying interesting planets amenable to atmospheric characterization using transmission and/or eclipse spectroscopy
Evolved |  |  4 |  Investigating the role and degree to which stellar evolution plays in shaping post-main sequence planetary systems
Technical Outcomes |  Planet Host Properties |  TA |  Homogeneously determining fundamental stellar properties derived from spectroscopy, asteroseismology, and astrometry
|  Astrophysical Doppler Noise |  TB |  Studying the relationship between Doppler jitter with various stellar astrophysical processes
Note —
∗ The keys in this table are used in the final TKS sample in Tables 5.2-5.2 to
identify which science case(s) each target will address.

####  2.1.1 Bulk Compositions

One of the most significant exoplanet discoveries by Kepler was the prevalence
of planets between the sizes of Earth and Neptune (Howard et al., 2012;
Fressin et al., 2013; Petigura et al., 2013). More recently, refined radius
measurements identified a valley in the planet distribution at a radius of
$\sim$1.8 $R_{\oplus}$ (Fulton et al., 2017; Fulton & Petigura, 2018; Van
Eylen et al., 2018).

The dominant theory for explaining the radius valley is that the two
populations originated from a single continuous distribution and an outcome
sculpted by post-formation pathways. An example is photoevaporation, a process
that strips away the atmosphere for less massive planets that receive high-
energy incident flux from their host star (Owen & Wu, 2013, 2017). Another
mechanism is core-powered mass-loss, which is also able to reproduce the
observed planet radius distribution based solely on the internal cooling of a
planet (Ginzburg et al., 2018; Gupta & Schlichting, 2019, 2020). However, a
major caveat is that the simulated planet populations used to test these
theories require assumptions about the underlying mass distribution, which is
degenerate with core compositions.

Multi-planet transiting systems with planets that straddle the radius valley
are benchmarks for discerning between these theories. Owen & Campos Estrada
(2020) used 104 planets in 73 systems and found that only two planets were
significantly ($>3\sigma$) inconsistent with the photoevaporation model.
However, all planets used in the analysis were faint Kepler systems, where
less than half of the population had actual mass constraints. TESS planets are
more amenable to RV characterization and are therefore an ideal population to
test these theories. A recent example was demonstrated in Cloutier et al.
(2020) for TOI-732, an M-dwarf with a pair of planets that straddle the gap,
but more systems around a larger diversity of host star spectral types are
needed.

Figure 1: Stellar obliquity versus host star age for confirmed planetary
systems (Figure adapted from Zhou et al. 2020). Tidal realignment and other
obliquity-exciting mechanisms such as Kozai-Lidov or secular processes operate
on different timescales. Young systems with well-known ages may elucidate the
relative importance of these mechanisms. TOI-1726 c (Dai et al., 2020),
WASP-107 b (Rubenzahl et al., 2021) and simulated upcoming TKS measurements
(red) add crucial points to distinguish various obliquity-exciting scenarios.

TKS will measure precise ($>$5$\sigma$) masses of small planets ($<4$
$R_{\oplus}$) in various environments to address some of these questions. In
particular, TKS aims to probe planet compositions across the radius gap in
order to constrain the underlying physical mechanism(s), including timescales
for which the bimodal distribution becomes more distinct. Additionally, by
targeting a range of small planets ($1$ $R_{\oplus}$ $<$ $R_{\mathrm{p}}$ $<4$
$R_{\oplus}$) at various incident fluxes, TKS can specifically test the
photoevaporation hypothesis. The compositions for ultra-short period planets
(P$<$1 day) will be used as a window into the refractory core of small planets
(Dai et al., 2021). TKS will also measure masses for cooler planets in order
to identify any that could have possible Earth-like compositions, especially
for planets that are amenable to subsequent atmospheric characterization.
Finally, TKS will make use of the entire population of small planets with
precisely measured masses to investigate any dependencies or correlations of
bulk planet properties with stellar properties.

####  2.1.2 System Architectures & Dynamics

A striking feature of the solar system is its dynamically cool architecture;
i.e., planets have nearly circular, nearly co-planar orbits that are well-
aligned with the rotation axis of the Sun.

On the contrary, large-scale exoplanet surveys have since introduced us to
planets in a rich diversity of system architectures and dynamical
configurations. For instance, it is now believed that orbital excitation is a
common outcome of various planet formation channels (Ford & Rasio, 2008) or
dynamical perturbation scenarios (Goldreich & Schlichting, 2014). While planet
detections from radial velocity surveys have yielded well-constrained
eccentricity measurements for many giant planets, sub-Jovian planet dynamics
and dynamical histories remain elusive. Current observations point towards
smaller planets having lower eccentricities (Van Eylen & Albrecht, 2015; Van
Eylen et al., 2019), but the number of such systems with precise
eccentricities is low.

Another dynamical property is the obliquity, or the angle between the orbital
plane of the system with respect to the stellar rotation axis. There are many
proposed mechanisms for spin-orbit misalignments in planetary systems that
operate on different timescales (Figure 1). Some processes tilt planet orbits
during the disk-hosting stage ($\sim$3 Myr, Batygin et al., 2011); while the
Kozai-Lidov mechanism operates typically on tens of Myr timescales, depending
on the system configuration (Fabrycky & Tremaine, 2007). Another prominent
theory occurs through secular interactions between planets and is expected to
happen in hundreds of Myr (Wu & Lithwick, 2011). Therefore, obliquity
measurements of stars with well-known ages could distinguish these theories.
Young planets, especially those have established cluster membership, have the
best age estimate and provide the strongest constraint on various obliquity-
exciting theories.

TKS will shed light on these questions by measuring eccentricities and
constraining obliquities around stars of various ages. Early TKS studies have
already yielded obliquities for two new systems, including a small planet in a
multi-planet transiting system in the Ursa Major Moving Group (Dai et al.,
2020) and the nearly pole-on, superpuff WASP-107 b (Rubenzahl et al., 2021).
Additionally, TKS will increase the number of well-studied eccentric sub-
Jovians by selecting high-probability eccentric candidates based on their
modeled photo-eccentric effect (Dawson & Johnson, 2012). TKS will also explore
the architectures of systems with multiple planets (in particular within the
habitable zone) by searching for signals induced by additional planets with
non-transiting geometries or those that reside on longer orbital periods.

####  2.1.3 Spectroscopy of Exoplanet Atmospheres

Multi-wavelength observations during transit and eclipse can constrain planet
properties such as the admixtures of constituent gasses, the presence of
clouds and/or hazes, and the rate of atmospheric loss. For sub-Neptune-size
planets, atmospheric observations are particularly valuable as they can
disambiguate between degenerate models of interior bulk composition (Rogers &
Seager, 2010). In turn, derived properties from these observations may probe
the location within the protoplanetary disk where the planet formed, the
interaction between its atmosphere and the stellar radiation field, and
questions in planetary habitability (Mordasini et al., 2016). Current
measurements of sub-Jovian atmospheres have been sample-size-limited due to
the scarcity of such targets around bright stars (Crossfield & Kreidberg,
2017).

In fact, with the launch of the _James Webb Space Telescope_ on the horizon,
precision mass measurements of small TESS planets amenable to atmospheric
characterization are critical for planning future follow-up observations.
Exoplanets require precise ($\geq$5$\sigma$) mass measurements to break the
degeneracy between surface gravity and atmospheric mean molecular weight when
interpreting their transmission spectra (Batalha et al., 2019). Therefore, TKS
will follow up and measure $\geq$5$\sigma$ mass measurements in order to
identify prime targets to be later observed with JWST for atmospheric studies.

####  2.1.4 Evolved Host Stars

Subgiant stars are frequently avoided in exoplanet surveys, with the exception
of early RV surveys that aimed to probe the occurrence rates of gas-giant
planets with stellar mass (Johnson et al., 2007, 2010, 2013). For transiting
planets, larger stars bias detections towards larger planets while for RV
measurements for subgiants are noisier than their main-sequence counterparts
(Luhn et al., 2019, 2020). However, the relative time stars spend on the
subgiant branch is comparatively small and therefore, a location on an HR-
diagram conveniently provides a much more precise mass and age than that of a
main-sequence star. Specifically in the Gaia era, an effective temperature,
luminosity and metallicity alone are a powerful tool to characterize
subgiants, including the planets that orbit them.

Precise ages are valuable to place observational constraints on dynamical
timescales for exoplanets. Previous studies have suggested that the dynamical
timescales for processes like circularization or inward migration through
tidal dissipation are strongly dependent on the scaled semi-major axis of the
system ($a/R_{\star}$, see Zahn, 1977; Hut, 1981; Zahn, 1989). Since this
property is most rapidly changing for subgiant stars, stellar evolution is
expected to affect tidal circularization timescales of close-in gas-giant
planets, producing a transient population of mildly eccentric planets orbiting
evolved stars (Villaver et al., 2014). Kepler and K2 data have yielded
intriguing evidence supporting this theory, but were based on a small sample
of planets (Grunblatt et al., 2018; Chontos et al., 2019). By building up a
more statistically-significant population of planets orbiting subgiants, TKS
will investigate the role of stellar evolution in shaping post-main-sequence
planetary systems.

Another advantage to studying planets orbiting evolved stars is that the
likelihood of performing asteroseismology, since the amplitude of the
oscillations increases with stellar luminosity. Ensemble studies of exoplanets
orbiting asteroseismic stars have provided some of the most precise planet
properties to date, revealing features like the hot sub-Neptune desert
(Lundkvist et al., 2016) and the radius valley (Van Eylen et al., 2018). First
examples for asteroseismology-exoplanet synergies with TESS include transiting
planets around TOI 197 (Huber et al., 2019) and TOI 257 (Addison et al.,
2021), as well as asteroseismic detections in solar analogs that are prime
targets for future direct imaging missions (Chontos et al., 2020).

###  2.2 Technical Outcomes

####  2.2.1 Host Star Characterization

Since transiting exoplanets are discovered indirectly, fundamental properties
of planet hosts are essential to realize the full scientific return of TESS.
High-resolution spectroscopy allows the precise determination of effective
temperatures and chemical abundances, which combined with Gaia parallaxes
allows the precise characterization of exoplanet host star properties such as
stellar masses (Berger et al., 2018, 2020). The results of the homogeneous
stellar characterization of TESS host stars using Keck/HIRES, including TESS
planet hosts beyond the target sample described here, will be presented in a
future study.

Figure 2: SB2 vetting plots for TOI 1567 and TOI 1987 from ReaMatch (Kolbl et
al., 2015). The left panels show the $\chi^{2}$ value as a function of Doppler
shift ($\Delta RV$) for the target of interest. Any deviations from the
characteristic single star reference suggest a stellar companion and is
usually sufficient for detecting bright companions (bottom). Any asymmetries
or offsets from $\Delta RV=0$ in the right panels are typically indicative of
a faint companion. Figure 3: Flowchart summary of the TKS target vetting
(Section 3) and master target list construction (Section 4) processes.
Software packages are plotted in blue, while databases are shown in gray. All
cuts and/or decision tree steps are in yellow and red represents points where
targets were removed from the TKS selection pool.

####  2.2.2 Understanding Stellar Doppler Noise

Various stellar phenomena such as spot modulation, granulation, and
chromospheric activity, produce apparent RV shifts or “jitter” that compete
with planetary signals and thus, make more active stars unfavorable targets
for precise radial velocity (PRV) work. Fortunately, TESS enables a link
between photospheric variability and observed RV shifts by providing precise
photometry for stars which already have long-term ground-based RV monitoring.
In addition, activity studies benefit immensely when RVs and photometry are
collected contemporaneously. For such datasets, jitter may be mitigated to
reveal exoplanet signals that would otherwise be inaccessible (López-Morales
et al., 2016).

The TESS-Keck Survey will leverage TESS to better understand the astrophysical
processes causing jitter and have developed techniques to mitigate and remove
it. Our survey design incorporates jitter tests for all included stars before
designating particular targets of specific interest to this science case.

##  3 Target Vetting

###  3.1 Photometry

All TKS targets underwent a series of vetting procedures before being selected
for precision RV follow-up. Targets are drawn from the database of TESS
Objects of Interest (TOI; Guerrero et al., 2021), which are made publicly
available222https://tev.mit.edu/. Targets below a declination of $-30^{o}$
were excluded based on Keck observability and resources. A visual magnitude
cut ($V<13$) was implemented where available, otherwise the TESS magnitude was
used ($T<13$). Targets with a higher Gaia re-normalized unit weight
error333https://gea.esac.esa.int/archive/ (RUWE $>2$), indicative of an
unresolved companion, were also excluded from target selection (Belokurov et
al., 2020; Evans, 2018). Additional cuts were implemented for candidate hosts
with $\rm T_{eff}$ $>$ 6500 K and planet candidates with $R_{\mathrm{p}}$ $>$
22 $R_{\oplus}$.

All remaining targets were individually vetted by a member of the TKS
photometric “tiger” team. Data validation (DV) products from the SPOC (Science
Processing Operations Center; Jenkins et al., 2016) and the QLP (Quick Look
Pipeline; Huang et al., 2020a, b) pipelines were inspected, ruling out
possible false positive scenarios. Close analysis was given to metrics such as
the ghost diagnostic and odd-even transit differences. Additionally, TOIs with
signal-to-noise ($\rm S/N$) less than 10 were excluded, since the planet
radius uncertainty would dominate the uncertainty in the bulk density for such
targets.

A special set of selection rules were implemented for out-of-transit centroid
offsets, where targets with a $<3\sigma$ offset would always pass this vetting
step. For centroid offsets between $3\sigma$ and $5\sigma$, a target would
still pass if the offset was $<21^{\prime\prime}$, unless the centroid offset
was at a nearby star according to the DV report. For $>5\sigma$ offsets, the
target would still pass if $T<7.5$, since the out-of-transit centroid offsets
are unreliable if a target is saturated.

Finally, a search for close companions was performed for the remaining targets
using their Gaia DR2 coordinates and the MAST (Mikulski Archive for Space
Telescopes)
Portal444https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html. Any
targets with bright ($\Delta V<5$), close (separation $<2"$) companions were
not considered for further follow-up. Additionally, any star with a $<1"$
companion was removed irregardless of its magnitude since it would be
challenging to resolve in poorer weather conditions and could possibly
contaminate flux on the slit during observations.

###  3.2 Spectroscopy

Once a TOI passed all photometric vetting steps, the target was queued for a
reconnaissance (or recon) spectrum. A recon spectrum is an iodine-free
exposure with an $\rm S/N$ $\approx 40/\mathrm{pixel}$ to check for rapid
rotation and spectroscopic false positives. Recon spectra are processed using
SpecMatch (Petigura, 2015) and ReaMatch (Kolbl et al., 2015).

SpecMatch estimates reliable spectroscopic stellar parameters {$\rm{[Fe/H]}$,
$\rm T_{eff}$, $v\sin i$, $\log g$} from spectra with $\rm S/N$ $\lesssim$
$40/\mathrm{pixel}$ (Petigura, 2015). We used results from SpecMatch-Synth,
which uses synthetic model atmopheres for stars with effective temperatures
$4700-6500\mathrm{K}$. SpecMatch-Emp (Yee et al., 2017), which relies on a
comparison with empirical templates, was used for late-type stars ($\rm
T_{eff}$ $\rm\lesssim 4700\,K$). Smaller planet candidates orbiting stars with
elevated $v\sin i$ ($\geq$ 8 $\rm km\,s^{-1}$) were not typically considered
due to the increased number of observations required to achieve a similar mass
precision (see §4.2 for more details).

Table 2: Spectroscopic false positives or high $v\sin i$ targets not suitable for PRV work identified through TKS, as described in Section 3.2. TIC ID | TOI | Disposition∗ | Note
---|---|---|---
405904232 | 1312 | SB1 |
129539786 | 1367 | – |  $v\sin i$ $=$ 12 $\rm km\,s^{-1}$
148782377 | 1415 | – |  $v\sin i$ $=$ 17 $\rm km\,s^{-1}$
411608801 | 1494 | – |  $v\sin i$ $=$ 15 $\rm km\,s^{-1}$
376637093 | 1516 | – |  $v\sin i$ $=$ 10 $\rm km\,s^{-1}$
259151170 | 1567 | SB2 |
285677945 | 1571 | – |  $v\sin i$ $=$ 20 $\rm km\,s^{-1}$
138017750 | 1608 | – |  $v\sin i$ $=$ 44 $\rm km\,s^{-1}$
184679932 | 1645 | SB1 |  $v\sin i$ $=$ 30 $\rm km\,s^{-1}$
468828873 | 1672 | SB2 |
58542531 | 1683 | SB2 |
103448870 | 1687 | – |  $v\sin i$ $=$ 15 $\rm km\,s^{-1}$
461662295 | 1711 | SB1 |
356978132 | 1755 | – |  $v\sin i$ $=$ 11 $\rm km\,s^{-1}$
450327768 | 1788 | – |  $v\sin i$ $=$ 12 $\rm km\,s^{-1}$
330799746 | 1818 | SB1 |  $v\sin i$ $=$ 10 $\rm km\,s^{-1}$
20182165 | 1830 | – |  $v\sin i$ $=$ 29 $\rm km\,s^{-1}$
349088467 | 1987 | SB2 |  $v\sin i$ $=$ 110 $\rm km\,s^{-1}$
Note –
∗ Dispositions in this table represent the false positive classification
suggested by initial TKS reconnaissance observations and therefore
do not necessarily reflect the most up-to-date TFOP dispositions.

The ReaMatch algorithm (Kolbl et al., 2015) is a spectroscopic method used to
identify faint stellar companions in double-lined spectroscopic binaries
(SB2). Figure 2 shows the SB2 vetting results from ReaMatch for TOIs 1567 and
1987. The left panels of Figure 2 show a cross-correlation of the observed,
rest-frame corrected spectrum with an NSO Solar spectrum. Significant
deviations to a single star function in the residuals are typically sufficient
to identify secondary stars which contribute a significant fraction of flux,
as for TOI-1987.

To check for possible faint secondary lines, a best-fit spectrum is matched
from the SpecMatch library, which is then broadened and diluted to match the
absorption lines of the primary. The matched spectrum is subtracted from the
observed spectrum, the residuals are renormalized and analyzed in a similar
way to the primary. The right panels of Figure 2 show the residual CCFs, where
any large deviation from $\Delta RV\sim$ $0$ $\rm km\,s^{-1}$ is evidence for
a second set of absorption lines. However, for stellar companions with similar
radial velocities to the primary, spectral lines can be blended with the
primary and therefore ReaMatch is limited to Doppler shifts $>10$ $\rm
km\,s^{-1}$.

Finally, systemic radial velocities were computed according to the methodology
of Chubak et al. (2012) and compared to Gaia to identify single-lined
spectroscopic binary (SB1). Targets for which the systemic and Gaia RV
disagreed by more than 5 $\rm km\,s^{-1}$ typically “failed” this vetting
step. However, for borderline cases (i.e. where systemic and Gaia RVs differed
between 5 and 10 $\rm km\,s^{-1}$ or other ambiguous false positive
detections), a second recon spectrum was taken to test for significant linear
trends that provided additional evidence of the SB1-like nature.

Targets that failed any of these analyses were reported to the TESS Follow-up
Observing Program555https://tess.mit.edu/followup/ (TFOP) Recon Spectroscopy
Working Group (WG) SG2. In addition, stellar properties derived from HIRES
spectra through the TKS project were/are made available to the community via
the Exoplanet Follow-up Observing Program for TESS (ExoFOP-TESS)
website666https://exofop.ipac.caltech.edu/tess/. Reported stellar parameters
include spectroscopic parameters $\rm T_{eff}$, $\log g$, $\rm{[Fe/H]}$, and
$v\sin i$, as well as stellar mass and radius derived using the spectroscopic
parameters as input to isoclassify (Huber, 2017; Berger et al., 2018). A list
of spectroscopic false positives and/or stars with rapid rotation is provided
in Table 2.

Figure 4: Median RV uncertainty versus $v\sin i$ for all stars $V<10$ with at
least 20 HIRES observations and effective temperatures between
$4000-6500\,\,\mathrm{K}$. Markers are sized by the number of HIRES
observations and colored by the effective temperature of the star. The black
dashed line represents our empirical fit to the data, which is defined by the
polynomial fit in Section 4.2 (Equation 2).

###  3.3 High Resolution Imaging

Multiplicity has historically been disfavored during follow-up target
selection processes since nearby companions can contaminate the spectrum
through the slit, resulting in less precise RVs. Similar to the Kepler
efforts, the TFOP High-Resolution Imaging WG (hereafter referred to as SG3)
has been reporting any stellar companions to ExoFOP TESS (e.g., Ziegler et
al., 2020). For TOIs that had adaptive optics, speckle or lucky imaging
information available, we excluded TOIs for which the data or SG3 comments
indicated the presence of a bright ($\Delta V<5$ or $\Delta K<5$) and nearby
(separation $<2$”) contaminant. For targets that had not yet been followed up
by SG3, this was not a requirement for the target selection process.

There are two TOIs that failed this vetting step but made it to our final TKS
target sample: TOI-1288, which only marginally failed by the presence of two
faint companions, and TOI-1443, which was added to an individual science case
that did not preferentially select against binaries.

##  4 Master Target List

###  4.1 TOI Information

In addition to the target vetting, we constructed a master target list that
provided additional information for TESS targets. This is shown in Figure 3,
which also starts from a TOI release but runs in parallel to the target
vetting and therefore, was performed for all TOIs.

For each TOI, the TESS Input Catalogue (version 8, TICv8; Stassun et al.,
2019) was queried using to fetch stellar properties (e.g., radius, etc.),
available photometry (i.e. Johnson $V$ and 2-MASS $JHK$) and the associated
Gaia Data Release 2 (DR2; Gaia Collaboration et al., 2016; Evans et al., 2018;
Gaia Collaboration et al., 2018) source ID. The Gaia ID was then used to
collect astrometric and photometric Gaia parameters. The TICv8 position was
used with tesspoint (Burke et al., 2020) to determine how many sectors a given
TESS target would be observed for. This was important for the TKS evolved
stars (SC4) and Doppler noise (TB) aspects, both which benefit from longer
baselines. The evolutionary state of the star was calculated using
evolstate777https://github.com/danxhuber/evolstate/, given a star’s effective
temperature and radius (Huber, 2017; Berger et al., 2018). For all steps that
involved calculations using stellar properties, information first came from
SpecMatch when a HIRES spectrum was available (see Section 3.2 for more
details), otherwise the TICv8 was used. In the event that neither were
available, stellar properties provided in the original TOI table were used.

Using the TOI planet radii, masses were computed using the following
relations:

$\mathrm{M_{p}}=\left\\{\begin{array}[]{ll}\rho_{\mathrm{p}}=2.43+3.39\Big{(}\frac{\mathrm{R_{p}}}{\mathrm{R_{\oplus}}}\Big{)}\,\mathrm{g\,cm}^{3}&\mathrm{R_{p}}<1.5\,\mathrm{R_{\oplus}}\\\
2.69\Big{(}\frac{\mathrm{R_{p}}}{\mathrm{R_{\oplus}}}\Big{)}&1.5\,\mathrm{R_{\oplus}}\leq\mathrm{R_{p}}<4\,\mathrm{R_{\oplus}}\\\
1.24\Big{(}\frac{\mathrm{R_{p}}}{\mathrm{R_{\oplus}}}\Big{)}^{1.7}&4\,\mathrm{R_{\oplus}}\leq\mathrm{R_{p}}<11.3\,\mathrm{R_{\oplus}}\\\
317.83&\mathrm{R_{p}}\geq 11.3\,\mathrm{R_{\oplus}}\\\ \end{array}\right.$

The relations are based on Weiss & Marcy (2014) for small planets, Chen &
Kipping (2017) for intermediate-mass planets, and assuming a Jupiter mass for
planets larger than Jupiter. Expected Doppler amplitudes were calculated using

$K_{\star,\mathrm{exp}}=\bigg{(}\frac{2\pi
G}{P}\bigg{)}^{1/3}\frac{\mathrm{M_{p}}}{(M_{p}+M_{\star})^{2/3}},$ (1)

where P is the orbital period, $G$ is the gravitational constant, and Mp and
M⋆ are the masses of the planet and star, assuming the orbit is aligned
($i=90^{o}$) and circular ($e=0$).

The CPS RV archive888https://jump.caltech.edu/ was queried for targets that
had existing spectra, which was important for estimating astrophysical
“jitter” (Section 4.2) and expected exposure times (Section 4.3). As a final
step, TOI dispositions were updated with the TESS Follow-up Observing Program
(TFOP) Working Group999https://tess.mit.edu/followup/ (WG) Sub Group 1
(hereafter referred to as SG1). TOIs with unfavorable or ambiguous
dispositions (e.g. APC, BEB, FA, FP, NEB, etc.)101010Ambiguous planet
candidate (APC), blended eclipsing binary (BEB), false alarm (FA), false
positive (FP), nearby eclipsing binary (NEB), etc. were removed.

###  4.2 Astrophysical Noise

To account for targets that would be challenging for PRV work, we estimated a
lower limit for a single RV uncertainty by considering the contributions from
various stellar sources. Specifically, we include effects from stellar
rotation and magnetic activity, as well as effects due to near-surface
processes like convection, granulation, and acoustic oscillations.

To estimate the Doppler noise contribution due to stellar activity
($\sigma_{\mathrm{act}}$), we used the empirical relation from Isaacson &
Fischer (2010) for stars that had the Ca ii S-index and color ($B-V$)
information available. For granulation and p-mode oscillations,
$\sigma_{\mathrm{gran}}$ was estimated using the effective temperature and
surface gravity as inputs to the RV jitter prediction
code111111https://github.com/Jieyu126/Jitter/ by Yu et al. (2018).

To investigate how rotation affected a typical RV observation, we calculated
the median RV uncertainty for all targets from the CPS RV archive. We kept
only stars with effective temperatures between $4000-6500\,\,\mathrm{K}$ with
at least 20 HIRES observations which are brighter than $V<10$ to to remove
targets with observations that could be dominated by photon noise. Figure 4
shows the median HIRES RV uncertainty as a function of $v\sin i$. For $v\sin
i$ $<2$ $\rm km\,s^{-1}$, the effects of rotational broadening are negligible
and therefore the RV precision is set by the typical noise floor of the
instrument and is 1.18 $\rm m\,s^{-1}$ for HIRES. For moderate and high $v\sin
i$ ($\geq 2$ $\rm km\,s^{-1}$), we fit a second order polynomial to estimate
$\sigma_{\mathrm{rot}}$,

$\sigma_{\mathrm{rot}}=0.867+0.140\,(v\sin i)+0.009\,(v\sin i)^{2},$ (2)

using a least-squares minimization, where $\sigma_{\mathrm{rot}}$ and $v\sin
i$ are in units of $\rm m\,s^{-1}$ and $\rm km\,s^{-1}$, respectively.

###  4.3 HIRES Observing Simulations

Exposure times were scaled based on the canonical exposure time of 100 seconds
for an iodine-in observation on a $V=8$ star in nominal conditions. For
iodine-out exposures, the HIRES throughput is $\sim 30\%$ higher and was
therefore factored in when relevant (e.g., recon spectra, templates). The
required counts, which determines the photon-limited RV precision
($\sigma_{\mathrm{RV}}$), varied between science cases. A maximum exposure
time of 30 minutes was implemented for all TKS observations.

The total single measurement uncertainty was then calculated using

$\sigma_{\mathrm{tot}}^{2}=\sigma_{\mathrm{RV}}^{2}+\sigma_{\mathrm{act}}^{2}+\sigma_{\mathrm{gran}}^{2}+\sigma_{\mathrm{rot}}^{2},$
(3)

which is the contribution from individual noise sources added in quadrature.
The measurement uncertainty scales as $\sigma_{\mathrm{tot}}$$\propto$
$\mathrm{N_{obs}}$-1/2, where the default number of observations for most TKS
science cases was $\rm N_{obs}=60$. Some exceptions that warranted more
observations included the multis (SC2C) and activity (TB) science cases, which
instead required 100 observations. On the other hand, the distant giants
(SC2A) science case preferred less precision and observations ($\rm
N_{obs}=15$) in exchange for more targets and longer baselines.

An average overhead of $120\,\mathrm{s}$ was charged per target per
observation that accounted for telescope slew time and CCD readout times. For
science cases that depended on a certain mass precision (e.g., SC3), total
nominal exposure times to achieve 5$\sigma$ ($t_{\mathrm{5\sigma,HIRES}}$) and
10$\sigma$ ($t_{\mathrm{10\sigma,HIRES}}$) masses were calculated for all
TOIs. Finally, to estimate realistic target costs, calculations also included
archival HIRES data and factored in if high-resolution templates were already
available.

Figure 5: Flow diagram for the survey target prioritization algorithm. The
Survey class contains all programmatic survey information as well as the
vetted sample. The selection process will continue until either all of the
time in the Survey is used or all science cases in the Survey are no longer
able to make selections.

###  4.4 Individual Science Case Selection Criteria

#### SC1A: Planet Radius Gap

The focus of SC1A is bulk properties of planets in the radius valley. However,
to increase overlap with other science cases, the radius limits were expanded
to include any planets in the range, 1 $R_{\oplus}$ $<$ $R_{\mathrm{p}}$ $<$
3.5 $R_{\oplus}$.

#### SC1B: Stellar Flux & Gaseous Envelopes

SC1B focuses on a narrow range of planet sizes that are exposed to very
different stellar environments. In particular, SC1B focused on smaller planets
with sizes, 1 $R_{\oplus}$ $<$ $R_{\mathrm{p}}$ $<$ 4 $R_{\oplus}$, with
orbital periods up to 100 days. To avoid sample biases, targets in this
parameter space were selected as uniformly and as randomly as possible. To
achieve this, 12 bins were created in roughly equal bin sizes in 2-dimensional
log-linear space, with planet radius bin edges of $R_{\mathrm{p}}$ $\sim$[1,
2, 3, 4] $R_{\oplus}$ (i.e. linear-space) and orbital period bin edges of P
$\sim$[1, 3, 10, 30, 100] days (i.e. log-space).

#### SC1C: Ultra-Short Period Planets

SC1C is primarily interested in differentiating between the smaller ($<$ 2
$R_{\oplus}$) USP population that appear to have $\sim$Earth-like compositions
and the recently discovered sub-day hot Neptunes (e.g., TOI$-$849 b; Armstrong
et al., 2020). Therefore, the selection criteria for SC1C includes both
classes of sub-Jovian planets, with $R_{\mathrm{p}}$ $<8$ $R_{\oplus}$ and
$\rm S_{inc}$ $>650$ $\rm S_{\oplus}$.

#### SC1D: Habitable Zone Planets

We selected potential habitable zone planets based on their incident flux. We
adopted a conservative limit of $<10$ $\rm S_{\oplus}$, and required all SC1D
targets to orbit main-sequence stars.

#### SC1E: Planet-Star Correlations

To enable statistical investigations of possible correlations between stellar
and planetary properties, we selected planets orbiting stars with a wide range
of masses and metallicities. In order to ensure that low-mass stars were
observed despite their faintness, TOI-1467 and TOI-1801 were added manually as
high-priority targets (see Section 6). Due to the broad parameter space
covered by this science case, almost all selected targets had overlap with
multiple science cases.

#### SC2A: Distant Giants

We selected all main-sequence stars brighter than $V=12$ that have an
approximately solar-like mass ($0.5$$M_{\odot}$ $\leq$ $M_{\star}$ $\leq
1.5$$M_{\odot}$). We avoided rapidly rotating and/or active stars by also
applying cuts in $\rm T_{eff}$ ($\leq 6200$ K $\approx$ Kraft Break) and $\log
R_{HK}^{\prime}$ ($\leq-4.7$). For stars that had recon spectra available and
thus a measured projected velocity, we excluded anything with a $v\sin i$
$\geq 5$ $\rm km\,s^{-1}$. We also excluded stars with Gaia RUWE $>1.3$.

Figure 6: All TESS Objects of Interest above a declination of
$\delta>=-30^{o}$ with a low Gaia RUWE $<$ 2 (gray sample). The black sample
comprises all TOIs that passed every vetting step and therefore available for
target selection. The final TKS sample that was selected by the target
prioritization algorithm is shown by the orange line.

We only selected targets with a multiple event statistics (MES) of at least
12, which indicated high-quality transit detections based on a visual
inspection of SPOC DV reports. Only systems that contained at least one
transiting planet smaller than 10 $R_{\oplus}$ were kept (Van Zandt et al., in
prep).

#### SC2B: Obliquities and Eccentricities

Obliquity targets were selected on a case-by-case basis. To select high-
probability eccentric planet candidates, we performed transit fitting to
measure the photo-eccentric effect, which compares the observed transit
duration with the expected duration for a circular orbit (Dawson & Johnson,
2012; Kipping et al., 2012). Transit duration is related to the eccentricity
(e) and the argument of periastron ($\omega$) by

$T_{14}=\left(\frac{R_{*}P}{\pi
a}\sqrt{1-b^{2}}\right)\frac{\sqrt{1-e^{2}}}{1+e\sin{\omega}},$ (4)

where $T_{14}$ is the total transit duration (i.e. from first to final
contact), $R_{\star}$ is the stellar radius, $P$ is the orbital period, $b$ is
the impact parameter, and $a$ is the planet’s semi-major axis (Winn, 2010).

Instead of using the standard transit parameter $a/R_{*}$, we reparametrized
our model in terms of stellar density ($\rho_{*}$), which maps to $a/R_{*}$
through Kepler’s 3rd Law and can be measured independently. We combined this
with Equation 4, which yielded a new parameter ($\rho_{*,circ}$) that was
directly sampled in our transit modeling using exoplanet (Foreman-Mackey,
2019). We derived an eccentricity estimate by re-sampling from the posterior
distribution of $\rho_{*,circ}$ and comparing these values to an independent
stellar density measurement derived by combining HIRES spectroscopy, Gaia
parallaxes, and isochrone models. We selected all targets for which zero
eccentricity is ruled out at the 2-$\sigma$ level by this method (MacDougall
et al., in prep).

#### SC2C: Multis

The only selection requirement for SC2C was the presence of more than one
transiting planet. Systems with the highest planet multiplicity were ranked
the highest. For systems with the same number of planet candidates, priority
was given to lower cost targets (i.e. brighter and/or shared targets).

#### SC3: Atmospheres

The Transmission Spectroscopy Metric (TSM) from Kempton et al. (2018) is an
SNR proxy for atmospheric observations and was computed for all planet
candidates. The TSM is defined as:

$\centering\mathrm{TSM}_{\mathrm{p}}=S\times\frac{R_{p}^{3}T_{eq}}{M_{p}R_{*}^{2}}\times
10^{-0.2m_{J}},\@add@centering$ (5)

where $S$ is a dimensionless normalization constant,

$S=\left\\{\begin{array}[]{ll}0.19,&R_{p}<1.5\,R_{\oplus}\\\
1.26,&1.5<R_{p}<2.75\,R_{\oplus}\\\ 1.28,&2.75<R_{p}<4.0\,R_{\oplus}\\\
1.15,&4.0<R_{p}<10\,R_{\oplus}.\end{array}\right.$

Quantitatively, the TSM is the expected (or simulated) SNR from a 10-hour
observation with _JWST_ -NIRISS, assuming a cloud-free, solar-metallicity,
H2-dominated atmosphere. For reference, a “good” TSM for a sub-Neptune
($1.5<R_{p}<4.0$ R⊕) is roughly between 80 and 150 (see Table 1 in Kempton et
al. 2018).

Figure 7: TESS Objects of Interest plotted in an HR-diagram (left) and in
stellar mass-metallicity space (right). Gray symbols represent all TOIs with
$\delta>=-30^{o}$ and Gaia RUWE metric $<$ 2\. Black symbols represent TOI
hosts that passed all vetting steps and hence available during the target
selection process, while the final TKS sample selected by the algorithm is
shown in orange.

To rank targets, TOIs identified by the SPOC pipeline (Jenkins et al., 2016)
were divided into log-uniform bins in planet radius ($R_{\mathrm{p}}$),
stellar effective temperature ($\rm T_{eff}$), and incident flux ($\rm
S_{inc}$). Bin edges were located at $R_{\mathrm{p}}$ = 1.0, 1.6, 2.5, 4.0,
6.3, and 11.2 $R_{\oplus}$, $\rm T_{eff}$ = 2500, 3900, 5200, and 6500 K, and
$\rm S_{inc}$ = 0.1, 1, 10, 100, 1e3, and 1e4 S⊕. Confirmed planets were added
alongside the TESS candidates in order to identify TOIs which had potential to
fill in sparsely-sampled regions of parameter space.

Ultimately, we divided the TSM by the expected Keck/HIRES exposure time
required to obtain a 5$\sigma$ mass ($t_{\mathrm{5\sigma,HIRES}}$). The
combined metric was used as a simple way to compromise between planets with
the best atmospheric prospects and those orbiting bright hosts with
reasonably-detectable (expected) Doppler amplitudes ($K_{\mathrm{exp}}\gtrsim
2$ m s-1).

#### SC4: Evolved

To identify higher priority targets that were likely to exhibit solar-like
oscillations, asteroseismic detection probabilities were computed for all TOIs
(Chaplin et al., 2011; Schofield et al., 2019). Broadly speaking,
asteroseismic detection probabilities increase with luminosity, and longer
time series. Schofield et al. (2019) calculated probabilities for the TIC
prior to the TESS launch, which required an assumption about the number of
observed TESS sectors. Consequently, we recalculated detection probabilities
using the actual number of observed TESS sectors. The selection criteria for
SC4 required either an evolved star status (subgiant or red giant) for new
planet candidate hosts or a predicted asteroseismic detection probability
$\geq 0.5$.

#### TB: Astrophysical Doppler Noise

For stars that had HIRES spectra available, an activity metric was computed
for the Doppler noise aspect using traditional activity indicators for
chromospheric emission like $\log R_{HK}^{\prime}$ and the estimated jitter.
Targets with known rotation periods and longer baselines were typically given
higher priorities. Gaussian weights centered on $\log R_{HK}^{\prime}$$=-4.8$
ensured a moderate level of activity while downweighting both inactive stars,
which would not be as scientifically interesting, as well as very active stars
that would be challenging to characterize. Using the $\sigma_{\mathrm{tot}}$
metric calculated in Equation 3 (Section 4.2), Gaussian weights were centered
on a “jitter” value of 4 $\rm m\,s^{-1}$ using a similar logic as that for the
chromospheric emission. Finally, brighter stars were ranked in ascending
order, which was helpful for narrowing the targets down by an order of
magnitude. However, more active targets require multiple considerations and
therefore, the final high-priority high-cadence targets were hand-selected for
this case.

Figure 8: Planet sizes of TOIs plotted versus period (left) and incident flux
(right). Gray symbols represent all TOIs with declination $\delta>=-30^{o}$
and Gaia RUWE metric $<$ 2\. Black symbols include all TOIs that passed every
vetting step, as discussed in Section 3. The final selected TESS-Keck Survey
sample is highlighted in orange, which includes $86$ unique systems with a
total of $103$ planets.

##  5 Automated Target Selection

###  5.1 Motivation & Initial Conditions

Using the master target list described in Section 4, the goal was to develop
an automated and reproducible procedure to select targets given a set of
science cases and time allocation. Specifically, a set of programs, $\rm
P\supset\,${$\rm P_{1},P_{2},\dots,P_{n}$} receive time allocations $\rm
T\sim${$\rm T_{1},T_{2},\dots,T_{n}$} of the total observing time allocation,
$\rm T_{tot}$. A set of stars, S$\,\supset\,${$\rm S_{1},S_{2},\dots,S_{m}$}
have a set of observing times per star, t$\sim${$\rm
t_{1},t_{2},\dots,t_{m}$}, which can vary for each program. Each program has a
set of ordinal rankings, R$\sim${$\rm R_{1},R_{2},\dots,R_{m}$} for the set of
stars S. In addition, some programs may want to update their rankings after
each selection step and therefore, individual program rankings have the
flexibility to adapt and update with each iteration. This encourages
cooperation because a program would rank a shared star more highly, which
would ultimately cost that program (and other programs) less.

A key component of the target selection algorithm is the Survey class, which
includes general survey information, science-case-specific requirements, and
the vetted planet sample. Individual programs are able to specify an observing
strategy (i.e. the total number of observations and required photon counts),
any high priority or dropped targets, as well as the selection criteria to
filter a program by. Upon the initialization of the Survey class, an initial
accounting of the vetted sample occurs by determining the number of relevant
science targets (or “picks”) available for each program in the Survey. This is
a required check to make sure that, at any point during the target selection
process, the number of program picks does not exceed the available number of
targets for a given science case.

Each program within the Survey can specify how to prioritize and select their
targets. Additionally, programs are not limited to a single criterion but
instead can rank targets by providing a list of prioritization metrics. An
example is SC2C, whose targets were first ranked by those with the highest
planet multiplicity and then by target cost. For atmospheric targets, TOIs
were prioritized by the TSM metric discussed in Section 4.4 and then sorted by
cost. However, for situations that involved more complicated vetting (e.g.,
SC2A) or meticulous observing plans (e.g., SC2Bii) whose prioritization could
not be automated, a list of rankings could be provided that did not change
under any circumstances. This was particularly important for a majority of SC2
that focused on dynamical planets, which provided an outlet for target lists
to “override” any automated process.

###  5.2 Algorithm

Figure 9: Venn diagram of the selected planet sample divided into the four TKS
science cases: 1) bulk compositions, 2) system architectures & dynamics, 3)
exoplanet atmospheres and 4) evolved stars. Numbers indicate the number of
planets belonging to each unique set of science cases, where total numbers
include 51 planets for SC1, 64 planets for SC2, 28 planets for SC3, and 16
planets for SC4. The entire sample will be used for investigating any star-
planet correlations (SC1E), characterizing properties of TKS host stars (TA),
as well as exploring Doppler noise contributions and/or dependencies (TB).

A data-flow diagram of the target prioritization algorithm is shown in Figure
5. In summary, the following steps occur while the total remaining time,
$\mathrm{T_{tot}}\equiv\sum_{n}\mathrm{T}_{n}$ for $n$ science cases in a
Survey is greater than zero:

  1. 1.

A program is selected at random, with a probability of selection that is
proportional to the fractional amount of time remaining for each science case
in the Survey. Specifically, the program calculates the cumulative
distribution function by normalizing the most current list of remaining times
for $n$ science cases by $\mathrm{T_{tot}}$. Therefore, a uniform random
number on the unit interval $\sim[0,1]$ will accurately map back to the $n$
discrete science cases and select a program from the Survey.

  2. 2.

The vetted Survey sample (see Section 3) is filtered based on the selected
program’s selection criteria (see Section 4.4) to include only the relevant
science targets. This is done by initializing a Sample class object, which
updates the target Sample costs based on past algorithm selections. After cost
updates are calculated, targets are ranked based on the prioritization
metric(s) of the selected program. If the program provided any high priority
targets, all priority targets would be ranked first, followed by the ordinal
rankings.

  3. 3.

The selected program chooses the highest ranked star that has not yet been
selected by that program.

    1. (a)

If the selected program _can_ afford the target (i.e. has the available time),
the program is charged the required observing time for the selected star and
the target is appended to the final Survey target list. If the target has
already been selected by other programs in previous iterations, relevant
programs are credited back any differences in costs.

    2. (b)

If the selected program _cannot_ afford the target (i.e. the required time for
the highest ranked target exceeds the remaining time available for the
program), the program is temporarily stuck. The “stuck” feature enabled the
continuation of the algorithm by allowing a program to pass without removing
it from the selection process altogether. The main reason for this is that as
the algorithm progresses, including more target selections, a given target
might suddenly become affordable if it is shared by enough programs.

The above steps repeat until either 1) all resources have been exhausted
(i.e.$\rm\,\,T_{tot}=0$) or 2) all programs in the Survey are “stuck”. Once
one of these conditions is satisfied, the selection process is complete and
the program will save a number of data products. The data products include a
csv file with the target list and the Track, which records the detailed
history (i.e. every iteration) of the selection process. Finally, an
additional Monte Carlo-like simulation option is available to test how robust
(or sensitive) the target sample is due to the inherent randomness of the
selection process.

Table 3: TKS Target Sample

|  | RA | $\delta\,\,\,\,\,$ | V | $\rm T_{eff}$ | $R_{\mathrm{p}}$ | Period | $\rm S_{inc}$ |
---|---|---|---|---|---|---|---|---|---
TOI | TIC | (deg) | (deg) | (mag) | (K) | ($R_{\oplus}$) | (days) | ($\rm S_{\oplus}$) | Science Cases∗
260.01 | 37749396 | 4.7732 |  $-$9.9648 | 9.90 | 4049 | 1.64 | 13.47 | 1.1e+01 | 1A, 1B, TA, TB†
266.01 | 164767175 | 26.2096 |  $-$18.4009 | 10.07 | 5784 | 2.41 | 10.77 | 1.0e+02 | 1A, 1B, 3†, TA, TB
266.02 | – | – | – | – | – | 1.76 | 6.19 | 2.2e+02 | 1A, 1B, 3, TA, TB
329.01 | 169765334 | 351.3209 |  $-$15.6347 | 11.26 | 5560 | 10.84 | 5.70 | 3.6e+03 | 4, TA, TB
465.01 | 270380593 | 32.7818 |  $+$2.4180 | 11.56 | 4936 | 5.64 | 3.84 | 1.5e+02 | 2A, TA, TB
469.01 | 33692729 | 93.0582 |  $-$14.6500 | 9.49 | 5283 | 3.69 | 13.63 | 8.9e+01 | 1B, 3†, TA, TB
480.01 | 317548889 | 88.3787 |  $-$16.2650 | 7.28 | 6212 | 3.08 | 6.87 | 3.7e+02 | 1A, 1B, 4†, TA, TB
509.01 | 453211454 | 117.9250 |  $+$9.3861 | 8.58 | 5560 | 3.07 | 18.12 | 4.6e+01 | 1A, 1B, 2A, 3†, TA, TB
554.01 | 407966340 | 60.7479 |  $+$9.2085 | 6.91 | 6337 | 3.41 | 7.05 | 4.2e+02 | 1A, 1B, 3†, TA, TB
561.01 | 377064495 | 148.1856 |  $+$6.2164 | 10.25 | 5440 | 3.77 | 10.78 | 6.6e+01 | 1A, 1B, 1C, 2C, 3, TA, TB
561.02 | – | – | – | – | – | 1.55 | 0.45 | 4.6e+03 | 1A, 1B, 1C, 2C, 3†, TA, TB
561.03 | – | – | – | – | – | 2.84 | 16.37 | 3.8e+01 | 1A, 1B, 1C, 2C, 3, TA, TB
669.01 | 124573851 | 158.9006 |  $-$5.1817 | 10.61 | 5624 | 3.84 | 3.95 | 6.4e+02 | 1B, 3†, TA, TB
1136.01 | 142276270 | 192.1849 |  $+$64.8553 | 9.53 | 5767 | 4.72 | 12.52 | 7.7e+01 | 1A, 1B, 2C, 3, TA, TB†
1136.02 | – | – | – | – | – | 3.00 | 6.26 | 2.1e+02 | 1A, 1B, 2C, 3, TA, TB†
1136.03 | – | – | – | – | – | 4.04 | 26.32 | 3.0e+01 | 1A, 1B, 2C, 3†, TA, TB†
1136.04 | – | – | – | – | – | 2.54 | 18.80 | 4.8e+01 | 1A, 1B, 2C, 3, TA, TB†
1173.01 | 232967440 | 197.6823 |  $+$70.7684 | 11.04 | 5322 | 9.22 | 7.06 | 8.1e+01 | 2A, TA, TB
1174.01 | 154089169 | 209.2181 |  $+$68.6180 | 10.96 | 5077 | 2.31 | 8.95 | 4.4e+01 | 2A, TA, TB
1180.01 | 158002130 | 214.5531 |  $+$82.1940 | 11.02 | 4700 | 2.85 | 9.69 | 3.5e+01 | 2A, TA, TB
1181.01 | 229510866 | 297.2159 |  $+$64.3544 | 10.58 | 6122 | 16.66 | 2.10 | 4.0e+03 | 4, TA, TB
1184.01 | 233087860 | 272.2039 |  $+$60.6782 | 10.99 | 4534 | 2.40 | 5.75 | 5.3e+01 | 2Bi†, TA, TB
1194.01 | 147950620 | 167.8205 |  $+$69.9647 | 11.30 | 5323 | 8.86 | 2.31 | 6.4e+02 | 2A, TA, TB
1244.01 | 219850915 | 256.2803 |  $+$69.5194 | 11.93 | 4599 | 2.39 | 6.40 | 5.6e+01 | 2A, TA, TB
1246.01 | 230127302 | 251.1165 |  $+$70.4296 | 11.63 | 5141 | 3.33 | 18.65 | 2.7e+01 | 2A, 2C, TA, TB
1246.02 | – | – | – | – | – | 3.00 | 4.31 | 2.0e+02 | 2A, 2C, TA, TB
1246.03 | – | – | – | – | – | 2.63 | 5.90 | 1.3e+02 | 2A, 2C, TA, TB
1246.04 | – | – | – | – | – | 3.26 | 37.92 | 1.0e+01 | 2A, 2C, TA, TB
1247.01 | 232540264 | 227.8705 |  $+$71.8410 | 9.08 | 5711 | 2.80 | 15.92 | 7.0e+01 | 1A, 1B, 2A, 3†, TA, TB†
1248.01 | 232612416 | 259.0236 |  $+$63.1060 | 11.81 | 5227 | 6.62 | 4.36 | 2.0e+02 | 2A, TA, TB
1249.01 | 232976128 | 200.5617 |  $+$66.3087 | 11.09 | 5453 | 3.15 | 13.08 | 3.1e+01 | 2A, TA, TB
1255.01 | 237222864 | 296.2443 |  $+$74.0627 | 9.92 | 5126 | 2.70 | 10.29 | 5.9e+01 | 1A, 1B, 2A, 2Bi†, 3, TA, TB
1269.01 | 198241702 | 249.6971 |  $+$64.5587 | 11.62 | 5517 | 2.38 | 4.25 | 2.3e+02 | 2A, TA, TB
1269.02 | – | – | – | – | – | 2.32 | 9.24 | 8.2e+01 | 2A, TA, TB

Note. —

Values in this table represent what was used during the target selection and
therefore, do not necessarily reflect the most up-to-date values. Duplicated
target information for TOIs with more than one transiting planet candidate
have been omitted.

∗ Please refer to Table 1 for the definition of science case keys.

† High-priority targets provided during the target selection process, as
indicated by the individual program.

Table 4: TKS Target Sample (continued)

Note. —

Values in this table represent what was used during the target selection and
therefore, do not necessarily reflect the most up-to-date values. Duplicated
target information for TOIs with more than one transiting planet candidate
have been omitted.

∗ Please refer to Table 1 for the definition of science case keys.

† High-priority targets provided during the target selection process, as
indicated by the individual program.

Table 5: TKS Target Sample (continued)

Note. —

Values in this table represent what was used during the target selection and
therefore, do not necessarily reflect the most up-to-date values. Duplicated
target information for TOIs with more than one transiting planet candidate
have been omitted.

∗ Please refer to Table 1 for the definition of science case keys.

† High-priority targets provided during the target selection process, as
indicated by the individual program.

##  6 TKS Target Sample

For TKS allocations, most science cases started with an equal amount of time
(10% of $\rm T_{tot}$), with the exception of SC1D that started with half of
that allocation (5% of $\rm T_{tot}$). In addition, 10% of $\rm T_{tot}$ was
designated to TB for high-cadence high-priority activity targets, while TA did
not require any allocation. Figure 6 shows the complete TKS sample (orange)
selected by the prioritization algorithm using an allocation of 50 nights and
assuming 10 hours per night. Almost all of the brighter targets available for
selection were almost always selected (Figure 6a), which was an inherent
product set by a characteristic of the algorithm (i.e. brighter targets are
cheaper and therefore, typically more highly ranked). Moreover, most TKS
targets are brighter than $V=12$, with the exception of one or two hand-
selected targets.

Figure 10: Same as Figures 7 and 8 but now only showing the selected TKS
sample. Markers are colored by the four TKS science cases, where concentric
rings represent higher-priority targets that address multiple science cases.

Figure 7 shows various host star properties for the TKS sample. Of the
selected $86$ targets, 51 are solar-type stars, which is defined here as any
star with an effective temperature within $\pm\,500$ K of the Sun. There are
very few hot stars, most likely due to the rapid rotation of stars past the
Kraft break (occurring near $\sim 6250\,$ K). Stellar masses and metallicites
appear clustered around solar-like values (Figure 7, right), as expected from
the effective temperature distribution. Fortuitously, the diverse range in
metallicity was a natural outcome of the selection process. On the other hand,
cool low-mass stars were selected less since they are fainter and therefore
more expensive. Therefore in order to partially avoid this bias, TOI 1467 and
TOI 1801 were added as high priority targets for SC1E and ended up being the
two coolest stars in the TKS sample (with $\rm T_{eff}$ $\leq 4000\,$K).

Figure 8 shows the sample of $103$ planets in $86$ systems selected for the
TESS-Keck Survey. Planet properties are slightly biased towards sub-Jovian
planets, which was expected since all but TKS Evolved (SC4) preferentially
selected smaller planets. In fact, the sample includes 71 planets smaller than
$\leq 4$ $R_{\oplus}$ that span nearly 5 orders of magnitude in incident flux,
including a few that experience a similar amount to that received on Earth.
Targets for the habitable zone science case are cooler than the majority of
small planets with measured densities and therefore useful for exploring how
planet properties change with decreasing incident flux. The TKS sample also
includes a handful of planets at the opposite extreme, on sub-day orbital
periods presumably with little to no remaining atmosphere.

While not unexpected, the strong overlap between the broad TKS science themes
is remarkable. Figure 9 shows a Venn diagram of the planet sample by science
case, and Figure 10 shows the samples in host star and planet parameter
spaces. The full TKS sample demonstrates the proof of concept for the unique
target selection process. Most notably, the large number of interdisciplinary
targets that were automatically identified through the algorithm emphasizes
the value for applying such target selection technique. This demonstrates the
utility for other large surveys in the future that face similar challenges
related to target selection processes, especially for collaborations with sub-
teams and/or overlapping science cases.

The complete TKS sample selected by the algorithm is provided in Tables
5.2-5.2, including the science case(s) for each target. Most notably, the
highest priority targets within the TKS sample (i.e. targets prioritized by
the most science cases) have been or will be highlighted by early TKS single-
object papers. An example is the galactic thick disc multi-planet system with
an ultra-short-period planet, TOI 561 (Weiss et al., 2021). Other single-
system TKS papers nearing publication include the high-eccentricity candidate
TOI 1255 (MacDougall et al., in prep.), four transiting sub-Neptunes with
diverse masses around TOI 1246 (Turtelboom et al., in prep.), a pair of prime
atmospheric, sub-Neptune twins orbiting HD 63935 (Scarsdale et al., in prep.),
as well as the five-planet system HD 191939 (Lubin et al., in prep). The
entire TKS sample provided in this paper has not been modified since August
21st, 2020, but is still subject to change as the survey continues.

##  7 Conclusions

In this paper, we have presented the science cases and target selection
process for the TESS-Keck Survey, a large program using Keck/HIRES to confirm
and characterize planets discovered with TESS.

Our main conclusions are as follows:

  * •

The TESS-Keck survey will measure precise ($>5\sigma$) masses for $\sim 100$
planets using an allocation of $\sim$100 nights over four semesters. TKS will
leverage this new population of transiting exoplanets orbiting bright, nearby
stars to address four main science themes, including 1) the bulk compositions
of small planets, 2) dynamical temperatures and system architectures, 3) a
larger, more refined sample for future atmospheric studies and 4) planets
orbiting evolved stars (Section 2.1).

  * •

We have developed open-source software for a fully-automated and reproducible
target selection procedure, which can be adapted for and used by other surveys
(Section 5.2). By providing a set of science programs with unique selection
criteria and prioritization metrics, the ranking algorithm will randomly
select programs and program targets until the allocated resources are
exhausted.

  * •

A total of $86$ targets were selected by the prioritization algorithm for the
final TKS sample (Section 6). The majority of TKS hosts are brighter ($V<12$),
solar-like main-sequence stars with effective temperatures, $\rm
4500\,\,K\leq$ $\rm T_{eff}$ $\rm<6000\,\,K$, at a wide range of metallicities
($-0.5<$ $\rm{[Fe/H]}$ $<0.5$). With the exceptions of hand-selected, high-
priority targets for the obliquity and stellar activity programs, most stars
have modest rotation and activity levels (Figures 6, 7, and 10).

  * •

The final TKS planet population comprises $103$ transiting planets in a rich
diversity of system configurations (Section 6, Tables 5.2-5.2). The selected
sample has $71$ small planets ($R_{\mathrm{p}}$ $\leq 4$ $R_{\oplus}$) with
incident fluxes that span nearly 5 orders of magnitude, including a few that
are in or near the habitable zone of their host star (Figures 6, 8 and 10).

The target selection presented here is the first in a series of papers
presenting ensemble results from the TESS-Keck Survey. Future planned catalogs
include homogeneous stellar properties, exoplanet masses and dynamical
architectures of planetary systems observed by the TESS Mission. The target
prioritization algorithm presented in this paper is publicly
available121212https://github.com/ashleychontos/sort-a-survey/, which is
currently designed to reproduce the enclosed TKS target sample. However, the
selection algorithm can be generalized and applicable to other large
collaborations or surveys that require a balance of multiple science interests
within a given allocation.

## Acknowledgements

We thank all the observers who spent time collecting data over the many years
at Keck/HIRES. We are grateful to the time assignment committees of the
University of California, University of Hawai’i, the California Institute of
Technology, and NASA for supporting the TESS-Keck Survey with observing time
at Keck Observatory and on the Automated Planet Finder. We thank NASA for
funding associated with our Key Strategic Mission Support project. We
gratefully acknowledge the efforts and dedication of the Keck Observatory
staff for support of HIRES and remote observing. We recognize and acknowledge
the cultural role and reverence that the summit of Maunakea has within the
indigenous Hawaiian community. We are deeply grateful to have the opportunity
to conduct observations from this mountain. We thank Ken and Gloria Levy, who
supported the construction of the Levy Spectrometer on the Automated Planet
Finder. We thank the University of California and Google for supporting Lick
Observatory and the UCO staff for their dedicated work scheduling and
operating the telescopes of Lick Observatory. This paper is based on data
collected by the TESS mission. Funding for the TESS mission is provided by the
NASA Explorer Program.

A.C., J.M.A.M., R.A.R., A.B., and A.M. acknowledge support from the National
Science Foundation through the Graduate Research Fellowship Program (DGE
1842402, DGE 1842400, DGE 1745301, DGE 1745301, DGE 1752814). J.M.A.M. also
acknowledges the LSSTC Data Science Fellowship Program, which is funded by
LSSTC, NSF Cybertraining Grant No. 1829740, the Brinson Foundation, and the
Moore Foundation; his participation in the program has benefited this work.
D.H. acknowledges support from the Alfred P. Sloan Foundation, the National
Aeronautics and Space Administration (80NSSC18K1585, 80NSSC19K0379), and the
National Science Foundation (AST-1717000). I.J.M.C. acknowledges support from
the NSF through grant AST-1824644. C.D.D. acknowledges the support of the
Hellman Family Faculty Fund, the Alfred P. Sloan Foundation, the David &
Lucile Packard Foundation, and the National Aeronautics and Space
Administration via the TESS Guest Investigator Program (80NSSC18K1583). E.A.P.
acknowledges the support of the Alfred P. Sloan Foundation. L.M.W. is
supported by the Beatrice Watson Parrent Fellowship and NASA ADAP Grant
80NSSC19K0597. P.D. acknowledges support from a National Science Foundation
Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1903811.
S.G. acknowledges support from NASA FINESST Grant 80NSSC20K1549.

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