# The occurrence of small, short-period planets younger than 200 Myr with TESS

Sydney Vach Centre for Astrophysics, University of Southern Queensland, West Street, Toowoomba, QLD 4350, Australia  George Zhou Centre for Astrophysics, University of Southern Queensland, West Street, Toowoomba, QLD 4350, Australia  Chelsea X. Huang Centre for Astrophysics, University of Southern Queensland, West Street, Toowoomba, QLD 4350, Australia  James G. Rogers Department of Earth, Planetary, and Space Sciences, The University of California, Los Angeles, 595 Charles E. Young Drive East, Los Angeles, CA 90095, USA  L. G. Bouma 51 Pegasi b Fellow  Cahill Center for Astrophysics, California Institute of Technology, Pasadena, CA 91125, USA  Stephanie T. Douglas Department of Physics, Lafayette College, 730 High St., Easton, PA 18042, USA  Michelle Kunimoto  Department of Physics and Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA  Andrew W. Mann Department of Physics and Astronomy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA  Madyson G. Barber NSF Graduate Research Fellow  Department of Physics and Astronomy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA  Samuel N. Quinn Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  David W. Latham Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Allyson Bieryla Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA  Centre for Astrophysics, University of Southern Queensland, West Street, Toowoomba, QLD 4350, Australia  Karen Collins Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA

###### Abstract

Within the first few hundreds of millions of years, many physical processes
sculpt the eventual properties of young planets. NASA’s TESS mission has
surveyed young stellar associations across the entire sky for transiting
planets providing glimpses into the various stages of planetary evolution.
Using our own detection pipeline, we search a magnitude-limited sample of 7219
young stars ($\lesssim 200$ Myr) observed in the first four years of TESS for
small ($2-8\,\mathrm{R_{\oplus}}$), short period ($1.6-20$ days) transiting
planets. The completeness of our survey is characterized by a series of
injection and recovery simulations. Our analysis of _TESS_ 2-minute cadence
and Full Frame Image (FFI) light curves recover all known TOIs, as well as
four new planet candidates not previously identified as TOIs. We derive an
occurrence rate of $35_{-10}^{+13}\%$ for mini-Neptunes and $27_{-8}^{+10}\%$
for super-Neptunes from the 2-minute cadence data, and $22^{+8.6}_{-6.8}$% for
mini-Neptunes and $13^{+3.9}_{-4.9}$% for super-Neptunes from FFI data. To
independently validate our results, we compare our survey yield with the
predicted planet yield assuming Kepler planet statistics. We consistently find
a mild increase in the occurrence of super-Neptunes and a significant increase
in the occurrence of Neptune-sized planets with orbital periods of $6.2-12$
days when compared to their mature counterparts. The young planet distribution
from our study is most consistent with evolution models describing the early
contraction of hydrogen-dominated atmospheres undergoing atmospheric escape
and inconsistent with heavier atmosphere models offering only mild radial
contraction early on.

exoplanets – young planets – planetary evolution – planet occurrence rates –
transiting exoplanets – transits – transit photometry

††facilities: TESS††software: astropy (Astropy Collaboration et al., 2013,
2018, 2022), batman (Kreidberg, 2015), cosmoabc (Ishida et al., 2015), emcee
(Foreman-Mackey et al., 2013), Matplotlib (Hunter, 2007), Numpy (van der Walt
et al., 2011), scipy (Virtanen et al., 2020), TRICERATOPS (Giacalone &
Dressing, 2020)

##  1 Introduction

During the first $\mathord{\sim}$100 Myr post-formation, many evolutionary
processes sculpt the eventual properties and architectures of young planetary
systems. Planet-disc (Lin & Papaloizou, 1986; Ida & Lin, 2004) ($\lesssim$ 10
Myr), planet-planet and planet-planetesimal ($\lesssim$ 100 Myr) interactions
(Chatterjee et al., 2008; Nagasawa et al., 2008; Nagasawa & Ida, 2011;
Schlichting et al., 2015) can cause perturbations to planetary orbits, shaping
the eventual system stability and architecture. Further, the evolution of the
host star and its interactions with evolving planets ($\lesssim 1$ Gyr) can
shape their physical properties (e.g., Lammer et al., 2003; Jackson et al.,
2012; Owen & Wu, 2013; Ginzburg et al., 2018).

Population studies of mature planetary systems have unveiled scarcities and
over-densities within the period-radius distribution of close-in small
planets, motivating investigations into the mechanisms sculpting this
distribution (e.g. Beaugé & Nesvorný, 2013; Mazeh et al., 2016; Fulton et al.,
2017; Hardegree-Ullman et al., 2020; Cloutier & Menou, 2020). Thermal
contraction, paired with atmospheric erosion and mass loss driven by
photoevaporation (Lopez & Fortney, 2013; Owen & Wu, 2013) and core-powered
mass-loss (Ginzburg et al., 2018; Gupta & Schlichting, 2019) are thought to be
the dominant processes. Atmospheric erosion driven by photoevaporation
resulting from high energy XUV flux is thought to be most active within the
first $\mathord{\sim}100$ Myr, whereas core-powered mass loss is thought to be
a more subtle process predicted to occur over the span of $\mathord{\sim}1$
Gyr. However, recent studies have illustrated that the exact timescales and
interplay between these two mechanisms are not yet well understood and require
further study (Owen & Schlichting, 2023). As a result of these processes, the
demographics of the close-in, small-planet population most heavily impacted by
these evolutionary processes should vary as a function of age. Kepler (Borucki
et al., 2010) and K2 (Howell et al., 2014) identified a number of young
planets (e.g., David et al., 2016; Mann et al., 2016, 2018; Rizzuto et al.,
2018; Vanderburg et al., 2018; David et al., 2019; Bouma et al., 2022; Barber
et al., 2022). These surveys provided the first glimpse into the temporal
evolution that small planets undergo early in their lives.

Since the launch of NASA’s Transiting Exoplanet Survey Satellite (TESS; Ricker
et al., 2016) in 2018, we have now been able to observe $\mathord{\sim}95\%$
of the sky. This has allowed us to gather precise space-based photometry for
stars located in well-aged moving groups and associations across the entire
sky, providing the first opportunity for detailed investigations into the
occurrence rates of planets as a function of age.

TESS has already detected and characterized tens of young planetary systems
(e.g., Newton et al., 2019; Battley et al., 2020; Plavchan et al., 2020; Zhou
et al., 2022; Wood et al., 2023), providing initial insights into the
differences between young planetary systems and their mature counterparts.
Initial investigations into the population of young planets observed by TESS
span a wider age range (Fernandes et al., 2023), targeting clusters which
already have confirmed planets (Fernandes et al., 2022), and explored methods
of light curve extraction that best preserves transit signals in the scenario
of heightened photometric variability from stellar activity (Nardiello et al.,
2020).

In this paper, we present the occurrence rates of close-in ($1.6-20$ days),
small ($2-8\,\mathrm{R_{\oplus}}$) planets around stars in nearby moving
groups and associations younger than 200 Myr. In Section 3, we describe our
parent stellar sample and our selection criteria. Utilizing the photometry
provided by TESS we present calculated rotation periods for our parent stellar
population in Section 3. Our planet detection pipeline, including our
detrending method, transit search, vetting process, and four newly identified
planet candidates, are presented in Section 4. In Section 5, we present our
injection and recovery test results in order to probe the completeness of our
planet search pipeline. We present our calculated occurrence rates in Section
6. We compare our results to the Kepler yield and complete a forward modeling
exercise to validate our derived occurrence rates. We present a discussion of
our findings in Section 7, and our conclusions in Section 8.

##  2 TESS Observations

TESS is an all-sky photometric transit planet survey. TESS observations are
conducted via four individual cameras covering a $24^{\circ}\times 96^{\circ}$
area of the sky, with each sector of observations lasting an average of 27
days. TESS samples the light curves of preselected target stars at 2-minute
and 20-second cadences via target pixel stamp observations. These data
products are processed by the Science Processing Operation Center (SPOC;
Jenkins et al., 2016) at the NASA Ames Research Center. We make use of the
SPOC Simple Aperture Photometry (SAP) for our SPOC planet search.

Additionally, TESS observes its entire field of view via Full-Frame Images
(FFIs). FFIs were taken every 30-minutes in the TESS primary mission and then
every 10-minutes in the first extended mission. The FFIs are downlinked and
processed by the MIT Quick-Look Pipeline (QLP; Huang et al., 2020a, b;
Kunimoto et al., 2022). We utilize the QLP default aperture raw flux light
curves for our QLP planet search program.

We make use of the light curves made available through the Mikulski Archive
for Space Telescopes
(MAST)111https://mast.stsci.edu/portal/Mashup/Clients/Mast/Portal.html as of
February 2023. This includes the first four years (sector 1-58) of TESS.222We
do not make use of newer 200 second FFI light curves in this analysis as they
were made available post-February 2023.

##  3 Stellar Population

Figure 1: Color-Magnitude diagram of the stellar population. The comoving
stars that have been kinematically aged to be $\lesssim 200$Myr that have been
observed by TESS in two-minute cadences (light blue) and in the TESS Full
Frame Images (purple) are plotted over the Kepler Input Catalog (Mathur et
al., 2017) (grey). Overlaid are the planet hosts identified in our SPOC
(circles) and the QLP (stars) surveys. The dark blue are published planetary
systems, the green are known TESS Objects of Interest (TOIs), and the pink are
new planet candidates identified in this search. Figure 2: Histograms of the
stellar properties of the parent stellar sample in our survey. To select our
parent population, we perform a radius cut at $<2\,\mathrm{R_{\odot}}$, a mass
cut at $<2\,\mathrm{M_{\odot}}$, a temperature cut at $<7000$ K, and an age
cut at $<200$ Myr. The stellar sample observed by SPOC (1927 stars) is shown
in blue, and the stellar sample observed by TESS in the FFIs (7154 stars) is
shown in purple. The left panel shows the histogram of the effective
temperatures of the parent sample, the middle panel shows the histogram of the
stellar masses, and the right panel shows the histogram of the stellar radii
(Stassun et al., 2019). We plot a random sample of 7154 stars of the Kepler
Input Catalog (Mathur et al., 2017) in grey for comparison to our parent
sample.

ESA’s Gaia (Gaia Collaboration et al., 2016, 2018, 2021) mission has provided
relative- and proper-motions and parallaxes for almost all nearby stars, which
has revealed previously unknown comoving populations.

We collate a sample of stars from literature analyses of Gaia kinematics to
assemble a population of young stars for our planet search. We make use of
associations and clusters assembled from Gagné et al. (2018), Kounkel & Covey
(2019), Ujjwal et al. (2020), and Moranta et al. (2022), as well as identified
members for well-characterized associations and clusters from Rebull et al.
(2016), Rebull et al. (2018), Rebull et al. (2022), and Meingast et al.
(2019). Gagné et al. (2018) and Ujjwal et al. (2020) provided updated
memberships for previously known young stellar associations. Ujjwal et al.
(2020) also updated the ages of the associations via _Gaia_ color-magnitudes
and MIST isochrone modeling (Dotter, 2016). Kounkel & Covey (2019) applied
unsupervised learning to _Gaia_ DR2 space motions to assemble groups of
associated stars, named as Theia groups, and aged via isochrone fits to their
color-magnitudes. Moranta et al. (2022) searched for nearby stellar streams
within $\mathord{\sim}200$ pc using $6D$ kinematics from _Gaia_ EDR3, named
Crius groups. For newly identified groups, Moranta et al. (2022) estimates age
ranges for identified associations via isochron fits to the Gaia color-
magnitude diagrams. However, we only adopt the members of Crius groups that
are affiliated with previously identified associations and clusters that have
well-characterized ages. Rebull et al. (2016) and Rebull et al. (2018) use
literature photometry and Gaia astrometry to classify previously identified
members of the Pleiades cluster and Upper Sco, we make use of stars identified
to be best/gold members (see Rebull et al. 2016 Table 1 and Rebull et al. 2018
Table 1). Rebull et al. (2022) selects members of Upper Centarus-Lupus (UCL)
and Lower Centaurus-Crux (LCC) from the literature, additionally requiring V
and K photometry and no source confusion in TESS; we only adopt members
identified as gold members (see Rebull et al. 2022 Table 2). Meingast et al.
(2019) used 6D Gaia kinematics to identify overdensities in velocity space,
indicative of nearby stellar streams. We make use of the members identified to
be part of Pisces-Eridanus stellar stream (Meingast et al., 2019; Curtis et
al., 2019). The assigned associations for each source star are listed in Table
1, where a star is listed from more than one source, all references and
literature association assignments are noted. Figure 3 shows the galactic
coordinates of our stellar population.

We concatenate these membership lists and cross-match them with TICv8 (Stassun
et al., 2019). TICv8 stellar parameter values (Stassun et al., 2019) are
adopted for all subsequent analyses. With the exception of planet-hosting
stars, we do not propagate the stellar parameter uncertainties for individual
stars forward in the occurrence rate analyses. We then select for stars with
age estimates $\leq 200$ Myr, stellar radii $\leq 2\,\mathrm{R_{\odot}}$,
stellar masses $\leq 2\mathrm{M_{\odot}}$, effective temperatures $\leq 7000$
K, and TESS band magnitudes $\leq 12$. This yields 9847 unique stars, with
8804 having been observed by TESS.

Following previous planet occurrence studies with the _Kepler_ sample (e.g.,
Berger et al., 2020; Kunimoto & Matthews, 2020), we filter for binaries by
removing stars with a GaiaDR3 renormalised unit weight error (RUWE) $>1.4$
(Kervella et al., 2022). However, a blind RUWE cut removes stars from our
sample that have a resolved companion. Therefore, for any star with RUWE ¿
1.4, we check for a resolved companion within 2 arcseconds and a delta
magnitude of 4, as the resolved companion explains the high RUWE and does not
suggest a possible false-positive (El-Badry et al., 2021). If this criterion
is matched, the high RUWE star is included in our parent population. This
yields 7219 stars with TESS observations. Of these, 7154 were observed in the
TESS FFIs and 1927 were preselected to have SPOC 2-minute cadence data. Figure
1 shows the color-magnitude diagram of our parent stellar population.

Figure 3: Galactic XY coordinates of the parent stellar sample from Gaia as a
function of age (darker colors indicate younger literature ages).

The population of stars within our SPOC sample is biased toward the proposed
science cases for their observations. As a result, the distributions of
stellar properties in our SPOC and QLP parent samples differ. Figure 2 shows
our QLP sample is skewed towards brighter, earlier-type stars leading to
intrinsic differences in our two surveys’ parent populations.

Table 1: Parent stellar population

TIC | R.A. | Dec. |  $T_{\mathrm{eff}}$ [K] | Parallax [mas] | $R_{\star}\,[\mathrm{R_{\odot}}]$ | $M_{\star}\,[\mathrm{M_{\odot}}]$ | TESSmag | Age [Myr] | Association |  $P_{\mathrm{rot}}$ [days]
---|---|---|---|---|---|---|---|---|---|---
289434416 | 315.36223 | 50.52834 | 6400 | 2.11 | 1.26 | 1.6 | 11.3 | 1.0 | Theia1 | $\cdots$
454364155 | 168.17869 | -76.61802 | 4700 | 5.23 | 0.75 | 1.45 | 11.2 | 1.0 | Theia2 | 6.1
454364158 | 168.10123 | -76.61838 | 5100 | 5.16 | 0.86 | 1.73 | 10.5 | 1.0 | Theia2 | 2.9
|  |  |  |  | . . . |  |  |  |  |

The full version of this table is made available in the supplementary online
material.

Our target list is a collation of multiple catalogs, some of which encompass
the traditional clusters and associations, while others are composed of new
associations identified via clustering studies. A fraction of our target list
may be contamination stars that are not part of their assigned associations,
while others may have ages that are older than our thresholds. For example,
Kounkel & Covey (2019) estimates a field star contamination rate of 5-10% for
the Theia group memberships. From the catalogs we used to collate our parent
stellar sample, only Kounkel & Covey (2019) estimates an age uncertainty,
$\mathord{\sim}0.15$ dex, which we adopt for our entire parent sample in our
forward modeling exercises (see Section 6.1).

To independently verify the contamination rate of our target sample, we
measure the rotation periods and variability signatures of our sample with the
available TESS photometry. We attempt to derive rotation periods for each star
with _TESS_ input catalog effective temperatures $T_{\mathrm{eff}}<6500$ K via
a Lomb-Scargle (Lomb, 1976; Scargle, 1982) periodogram over all available
_TESS_ long cadence photometry from the QLP. We only attempt to derive
rotation periods for later type stars in our sample, as they are more likely
to host convective envelops and exhibit strong stellar rotational variability
at ages up to 200 Myr. The membership contamination rates estimated from the
rotational variability of late-type stars are extrapolated and adopted for our
entire survey sample (see Section 6.2). We search for stellar rotation periods
between 0.5 and 12 days, as we do not attempt to search for rotational periods
longer than that of the TESS orbital period. We note that stars with ages
$<200$ Myr should have rotation periods $<10$ days (e.g. Messina, 2021), and
as such the upper limit cutoff should not bias our sample. The rotation
periods are then manually vetted to check if the signals resemble that of 1)
rotational variability, 2) binarity, 3) non-rotational variability, or 4) no
variability. Example light curve figures that were used for our manual vetting
process are shown in Appendix D. We analysed rotational signatures for a
sample of 6402 stars that satisfy our stellar properties thresholds, of which
4878 exhibit variability resembling rotational modulation from our manual
vetting. Our measured rotation periods are presented in Table 1.

Rotation-based age estimates are unreliable for the youngest stars we sample,
as pre-main sequence stars that are still contracting exhibit a significant
spread in rotation periods for a given age. As such, we do not directly make
use of gyrochronological relationships to directly compare against literature-
reported isochrone-derived ages. We do make use of the age rotation
relationships to quantify the membership contamination rate for our sample.

We make use of the Bouma et al. (2023) gyrochronology relationship to convert
rotation periods to age posteriors on a star-to-star basis. This is only
performed for the subsample of stars that exhibit rotational variability and
are not identified to be binaries based on their light curves and _Gaia_
stellar parameter flags. We find that 9% of stars have gyrochronology-based
posteriors with $1\sigma$ age upper limits $>200$ Myr, making them
inconsistent with the bounds of our sample selection. In addition, late-type
young stars should exhibit significant stellar activity due to spot-induced
modulations in their light curves. Our manual vetting flagged 30% of G and K
stars as not exhibiting significant photometric variations. As such, we
conservatively adopt a membership contamination rate ranging over 10-30% for
our parent sample in this study.

In addition, we also specifically check for consistency between the
literature-reported ages and our gyrochronology-derived ages for the
associations that host new planet candidates identified in this study. Figure
4 shows our measured rotation period distributions of LCC, Theia 116, and
Theia 214 against that of previously well-characterized populations. These
associations host new planet candidates. We find general consistency between
their literature ages to the rotation distributions of their members, and that
no planet candidates need to be excluded from our occurrence rate due to their
parent association being older than 200 Myr.

Figure 4: Stellar rotation period plotted as a function of stellar mass for
stars identified to host planet candidates (red star) and the members of their
respective stellar associations (light red)–UCL/LCC (upper panel), Theia 116
(middle panel) and Theia 214 (lower panel). We compare the rotation periods of
the stellar associations to rotation periods of well-characterized clusters at
a variety of stages: Upper Sco (left; Rebull et al., 2018), h Per (middle;
Moraux et al., 2013), and Pleiades (right; Rebull et al., 2016; Godoy-Rivera
et al., 2021). We compare our derived rotation periods from the TESS QLP light
curves to check for youth, and identify possible falsely identified non-
members.

##  4 Planet Detection Pipeline

We describe in the sections below our transit search routine, as well as a
series of checks to analyze the completeness and reliability of our transit
search pipeline. We present our recovered planet candidates in Table 2.

###  4.1 Detrending Stellar Activity

Young stars present additional challenges during the detrending processes of
their light curves as a result of heightened activity. Photometric variations
on the scale of 1-2% due to rapid stellar rotation, along with stellar flares,
require special attention.

We perform an iterative sigma clipping outlier rejection for major flare
rejection and then model and subtract the photometric signal induced by rapid
stellar rotation. Our detrending method is as follows.

  * •

Perform an initial spline fit of the stellar rotational variability via the
routine described in Vanderburg et al. (2019).

  * •

Identify flare rejection masks via 10 iterations of upward outlier rejection
at the $5\sigma$ level with standard deviation calculated by scaling the
measured median absolute deviation value.

  * •

Refit stellar variability via the spline model to the raw light curve with the
flare rejection masks applied and flatten the resulting light curve (e.g. see
Figure 10).

###  4.2 Box Least Squares Transit Detection

With the detrended light curves, we perform a Box Least Squares (BLS; Kovács
et al., 2002) search to identify planetary transits in the TESS light curves.
We sample 100,000 periods linearly spaced between 0.5 and 20 days and 500
transit durations linearly sampled between 0.008 times and 0.08 times of each
tested orbital period.

###  4.3 Vetting Threshold Crossing Events

We identify any signal with a calculated Signal-to-Pink Noise (Hartman &
Bakos, 2016) $\geq$ 8 to be a threshold crossing event (TCE). For each
individual TCE, we perform a visual inspection to vet for false positives
resulting from both astrophysical events and detrending artefacts. Each
candidate event is inspected to determine if the signal that triggered a TCE
is transit-shaped. Further, we require a minimum of three transit events in
order to determine a period.

We search for the presence of secondary eclipses and differences in the depths
between even and odd transits to determine if the transit is from an obvious
eclipsing binary (EB). For identified TCEs, we fit for the transit depth of
even and odd transits respectively to rule out EB scenarios. To measure the
transit depth, we fix all parameters except for transit depth, $\delta$. The
transits are modeled using batman (Kreidberg, 2015). We explore the best-fit
transit depth and posteriors with a Markov chain Monte Carlo run implemented
with emcee (Foreman-Mackey et al., 2013). We require the difference in our
calculated odd and even transit depths to be $<5\sigma$ to not be classified
as an EB, though no reported candidate has an odd-even difference $>3\sigma$
in our sample. We visually vet for secondary eclipses by phase-folding the
detrended TESS light curves at the derived TCE period and transit time. The
entire phase folded light curve is visually inspected for signs of secondary
eclipses. Targets exhibiting eclipsing binary-like secondary eclipses,
including those in eccentric orbits, are excluded as planet candidates.

We check for obvious nearby eclipsing binaries (NEBs) by comparing the pixel-
by-pixel light curves centered around the target star to determine if the
signal that triggered the TCE is consistent with being on target. We then
perform a centroid analysis with the TESS FFIs to create difference images
using the transit-diffImage codebase333https://github.com/stevepur/transit-
diffImage following the techniques described in Bryson et al. (2013). For each
candidate at each sector, we produce a difference image between the in-transit
and out-of-transit pixel stamps. We calculate the centroid of the target star
via a TESS Pixel Response Function (PRF) fit to the difference image. The
calculated centroid is compared against the expected centroid of the target
star based on a catalog projection. Some offset in the difference image is
expected if nearby stars are present, as the centroid naturally shifts away
from the dimming star during transit. We flag any centroid offset $>1$ pixel
for visual inspection to determine if the high centroid offset is due to an
astrophysical false-positive.

Remaining candidates were then checked for planetary multiplicity. We masked
the initial signal that triggered the TCE in the non-detrended light curves
and then repeated the detrending and transit search (see Section 4.1 and 4.2).
Any signals that pass our signal-to-noise criteria are visually vetted in the
same method as mentioned above. We additionally check for aliasing of the
original signal recovered. All remaining candidates are compared to the list
of TOIs and known planets.

Figure 5: Period-radius distribution of the recovered confirmed planets and
planet candidates in our SPOC and QLP surveys. Published planets are shown in
blue, planet candidates that are known TOIs are shown in green, and planet
candidates newly identified in this work are shown in pink. We plot the
contour distribution of the known transiting exoplanet population in grey.

Our stellar sample contained 26 TOIs (see Table 2), 22 of which were within
our search parameters (0.5 days $\leq\,P\leq$ 20 days and
$1\mathrm{R}_{\oplus}\leq\,R_{P}\leq 8\mathrm{R}_{\oplus}$), as well as 2
previously previously confirmed transiting planets from _K2_. Of these, 16
have both FFI light curves and 2-minute cadence light curves (see Table 2), as
well as the two _K2_ planets. Of the 22 known TOIs within our survey
parameters, 20 are either planet candidates (PC) or confirmed planets, the
remaining 2 have been ruled as either false-alarms or false-positives by
_TESS_ Follow-up Observing Program Working Groups (SG1 and SG2).

We recovered all known TOIs in our planet search using the SPOC light curves,
including 15 planet candidates within our search parameters and the known
planets V1298 Tau c and d. Our QLP planet search recovered all 20 TOIs within
our parameters but was unable to recover V1298 Tau c and d. Our pipeline
identified a signal corresponding to V1298 Tau c, with a signal-to-pink-noise
of 6.29, which did not trigger a TCE. Upon visual inspection of the FFI light
curves for V1298 Tau, the long cadence of the FFIs and the heightened stellar
activity subsequently diluted the transit signal in our detection pipeline.

We identified new planet candidates TIC 88785435.01, TIC 150070085.01, TIC
434398831.01, and TIC 434398831.02 (Vach et al. in prep) in our QLP search.
Out of the new planet candidates, only TIC 150070085.01 is also observed by
SPOC data and is independently identified by our SPOC search. These candidates
are described in detail in Appendix C.

Table 2: Planets and candidates identified in our search

TIC | Name | RP [$R_{\oplus}$] |  $P$ [days] | Pipeline∗ |  $T_{\mathrm{eff}}$ [K] | Age [Myr] | Association | False Positive Rate
---|---|---|---|---|---|---|---|---
Confirmed and verified planets
15756231 | V1298Tau ca | 5.16$\pm$0.38 | 8.25 | SPOC | 5160 | 23 | Taurus-Auriga Complex | 0
15756231 | V1298Tau da | 6.43$\pm$0.46 | 12.41 | SPOC | 5160 | 23 | Taurus-Auriga Complex | 0
299798795 | TOI-1224.01b | 1.90$\pm$0.050 | 4.18 | SPOC & QLP | 3390 | 178 | MELANGE-5 | 0
299798795 | TOI-1224.02b | 3.00$\pm$0.14 | 17.95 | SPOC & QLP | 3390 | 178 | MELANGE-5 | 0
360630575 | HD 109833 bc | 2.89$\pm$0.15 | 9.19 | QLP | 5880 | 26 | LCC | 0
360630575 | HD 109833 cc | 2.59$\pm$0.2 | 13.90 | QLP | 5880 | 26 | LCC | 0
384984325 | TOI-6109.01d | 4.51$\pm$0.46 | 8.54 | SPOC & QLP | 5760 | 55 | Theia133 | 0
384984325 | TOI-6109.02d | 2.78$\pm$0.32 | 5.70 | SPOC & QLP | 5760 | 55 | Theia133 | 0
410214986 | DS Tuc Abe | 5.70$\pm$0.17 | 8.14 | SPOC & QLP | 5410 | 40 | Tuc-Hor | 0
441420236 | AU Mic bf | 4.20$\pm$0.20 | 8.46 | SPOC & QLP | 3590 | 25 |  $\beta$ Pic | 0
441420236 | AU Mic cf | 3.24$\pm$0.16 | 18.86 | SPOC & QLP | 3590 | 25 |  $\beta$ Pic | 0
460205581 | TOI-837 bg | 8.60$\pm$1.0 | 8.32 | SPOC & QLP | 6100 | 35 | IC2602 | 0
464646604 | HIP 94235 bh | 3.00$\pm$0.21 | 7.71 | SPOC & QLP | 5990 | 120 | AB Dor | 0
434398831 | TIC 434398831.01i,j | 3.65$\pm$0.28 | 3.69 | QLP | 5550 | 48 | Theia116 | 0
434398831 | TIC 434398831.02i,j | 5.41$\pm$0.32 | 6.21 | QLP | 5550 | 48 | Theia116 | 0
257605131 | TOI-451 bk | $1.94\pm 0.15$ | 1.86 | SPOC & QLP | 5480 | 120 | Pisces-Eridanus | 0
257605131 | TOI-451 ck | $3.07\pm 0.14$ | 9.19 | SPOC & QLP | 5480 | 120 | Pisces-Eridanus | 0
257605131 | TOI-451 dk | $4.03\pm 0.15$ | 16.36 | SPOC & QLP | 5480 | 120 | Pisces-Eridanus | 0
Planet candidates |
46631742 | TOI-5358.01i | 2.61$\pm$0.14 | 2.66 | SPOC & QLP | 4640 | 134 | Theia369 | 0.01126$\pm$0.00088
157081737 | TOI-6095.01i | 1.87$\pm$0.16 | 2.16 | SPOC & QLP | 5260 | 106 | Theia301 | 0.1204$\pm$0.0020
460950389 | TOI-6715.01i | 4.52$\pm$0.19 | 2.86 | QLP | 4740 | 35 | Theia92 | 0.00538$\pm$0.00047
88785435 | TIC 88785435.01i | 4.88$\pm$0.28 | 10.51 | QLP | 4000 | 16 | UCL | $0.0947\pm 0.0037$
150070085 | TIC 150070085.01i | 3.64$\pm$0.41 | 10.47 | SPOC & QLP | 6070 | 96 | Theia214 | 0.0312$\pm$0.0067
Planets and planet candidates not included in occurrence rate calculation |
36332984 | TIC 36332984.01i | 4.40 | 6.33 | QLP | 5400 | 84 | Theia 217 |  $\cdots$
47720259 | TOI-2519.01α | 2.29 | 6.96 | QLP | 4740 | 94 | Theia 204 |  $\cdots$
64837857 | TOI-6550.01 n | 14.1 | 20.3 | SPOC & QLP | 6740 | 43 | Theia 117 |  $\cdots$
86951294 | TOI-4596.01α | 2.72 | 4.12 | SPOC & QLP | 5600 | 55 | Theia 133 |  $\cdots$
166527623 | HIP 67522 b l,β | 10.1 | 6.96 | SPOC & QLP | 5770 | 17 | Sco-Cen |  $\cdots$
238395674 | TIC 238395674.01i | – | – | QLP | 5700 | 22 | Theia 12 |  $\cdots$
238395674 | TIC 238395674.02i | – | – | QLP | 5700 | 22 | Theia 12 |  $\cdots$
259606227 | TOI-6555.01i,γ | 3.77 | 10.1 | QLP | 6800 | 172 | Theia 436 |  $\cdots$
441546821 | HD 114082 bm,β | 11.2 | – | SPOC & QLP | 6610 | 16 | LCC |  $\cdots$
TOIs not included in occurrence rate calculation |
14091633 | TOI-447.01δ | 23.5 | 5.52 | SPOC & QLP | 6430 | 35 | Crius 164 |  $\cdots$
151284882 | TOI-2595.01δ | 3.32 | 3.81 | QLP | 6030 | 48 | Theia 116 |  $\cdots$
117689799 | TOI-3504.01δ | 14.2 | 2.16 | SPOC & QLP | 5860 | 99 | Theia 254 |  $\cdots$
133505138 | TOI-4359.01ϵ | 2.47 | 2.436 | SPOC & QLP | 6530 | 91 | Theia 215 |  $\cdots$
164150539 | TOI-1372.01ζ | 8.22 | 6.16 | SPOC & QLP | 5770 | 100 | Theia 312 |  $\cdots$
294500964 | TOI-2550.01ζ | 10.5 | 9.07 | SPOC & QLP | 5740 | 83 | Theia 211 |  $\cdots$
457939414 | TOI-1990.01η | 11.8 | 3.78 | SPOC & QLP | 6080 | 35 | Theia 92 |  $\cdots$

$(a)$ David et al. (2019); $(b)$ Thia et al. in prep; $(c)$ Wood et al.
(2023); $(d)$ in prep; $(e)$ Newton et al. (2019); $(f)$ Plavchan et al.
(2020); $(g)$ Bouma et al. (2020); $(h)$ Zhou et al. (2022); $(i)$ This work;
$(j)$ Vach et al. in prep; $(k)$ Newton et al. (2021); ($l$) Rizzuto et al.
(2020); ($m$) Zakhozhay et al. (2022); $(\alpha)$ No signatures of youth in
spectroscopic follow-up; ($\beta$) Not in our survey parameters; $(\gamma)$
Low SNR, ambiguous signal– not a TOI as of February 2023; $(\delta)$ Ambiguous
Planet Candidate; $(\epsilon)$ Nearby Eclipsing Binary SG2; $(\zeta)$ Nearby
Planet Candidate; $(\eta)$ Eclipsing Binary SG2; .
∗The pipeline column identifies which of our surveys recovered the signal.

###  4.4 False Positive Rate

Our planet detection pipeline recovered 14 confirmed planets within our SPOC
parent sample, and 16 confirmed planets in the QLP parent sample. For planets
with Confirmed Planet dispositions by the TESS Follow-up Observing Program
Working Group, we adopt a false positive rate of zero, as these planets have
undergone extensive spectroscopic and photometric follow-up observations to
rule out false positive scenarios.

Five remaining planet candidates have not received sufficient follow-up to be
categorized as verified or validated planets. As such, there exists a
possibility that they are the result of astrophysical false positive
scenarios, instrumental systematics, stellar variability, detrending
artefacts, etc. There are not sufficient planet candidates around young stars
from which we can derive a secure false positive rate estimate.

To attain false-positive estimates, we make use of the publically available
TRICERATOPS (Giacalone et al., 2021) code to estimate the probability a
transit signal is planetary in nature or the result of an astrophysical false
positive. We explore the impact on the false positive rates when derived using
blend analyses codes, including TRICERATOPS and MOLUSC (Wood et al., 2021).
The false-positive rates from TRICERATOPS are adopted in our occurrence rate
calculations hereafter and are typically 1-5% for a given candidate. When we
adopt TRICERATOPS and MOLUSC together, the false-positive rate approaches 0%
for all candidates, and its impact on the calculated occurrence rates is the
same as if we assume all candidates are true planets. This upper-bound case is
further explored in Appendix B. We do not attempt to differentiate the false
positive rates between multi- and single-planet systems. We present our
calculated false-positive rates in Table 2.

We adopt a false positive rate of 0% for TIC 434398831.01 and .02, which are
presented in this work. These are characterized as confirmed planets as space-
and ground-based follow-up have ruled out any false-positive scenarios (see
Appendix C).

We perform a series of checks to illustrate our false-positive rate
assumptions do not significantly influence our derived occurrence rates (see
Appendix B).

###  4.5 Candidates identified but not included in occurrence rate
calculations

We identify 9 planets or planet candidates that are not included in our
occurrence rate calculations. These are presented in Table 2. HIP 67522 b
($10.1\mathrm{R_{\oplus}}$, Rizzuto et al., 2020), HD 114082 (single transit,
$11.2\mathrm{R_{\oplus}}$, Zakhozhay et al., 2022), and TOI-6550.01
($14.1\mathrm{R_{\oplus}}$, $P=20.3$ days) were recovered in both our SPOC and
QLP pipelines, but are not included in our occurrence rate calculations as
they fall outside the bounds our survey investigates. We note that TOI-2519.01
(QLP only) and TOI-4596.01 (SPOC and QLP), which we recover in our survey,
both show no Li features in ground-based follow-up observations. Upon visual
inspection, their respective light curves are also not consistent with youth.
We therefore exclude them from our occurrence rate calculations as they are
likely not members of their respective clusters. TOI-4596 has an additional
single transit event, but we are unable to constrain the period.

TOI-6555.01 is excluded as our vetting processes did not find it to be
consistent with being on target. Additionally, TOI-6555.01 was not alerted
until sector 65, which was not made available until after February 2023.

We identify two new additional candidates, TIC 36332984.01 and TIC
238395674.01, which both pass all of our initial vetting procedures, but we
are unable to uniquely determine the orbital periods. TIC 238395674 is likely
a multi-planet system, where we observe two transits of TIC 238395674.01 and
an additional single transit of TIC 238395674.02. Therefore we do not include
these candidates in our occurrence rate calculations.

Additionally, TOI-6715, previously identified as PATHOS 31 (Nardiello et al.,
2020), has both QLP and SPOC data. However, as of February 2023 only the QLP
data was available, therefore we only include TOI-6715 in our QLP occurrence
rate calculations, but not in our SPOC occurrence rate calculations.

###  4.6 False positive TOIs not included in occurrence rate

We identified seven TOIs that failed our own vetting process but were
identified as TOIs by QLP and/or SPOC TPS. These TOIs are presented in Table
2.

Follow-up observations by the _TESS_ Follow-up Observing Program Working
Groups (TFOP WG) rule five as nearby planet candidates (NPC), meaning the
signal triggering the TCE is from a planet transiting a nearby star, or
ambiguous planet candidates (APC). TFOP WG SG2 identified the remaining two as
a nearby eclipsing binary (NEB) and an eclipsing binary (EB) respectively.

##  5 Injection Recovery Test

We perform a set of transit injection and recovery tests to test the
completeness of the transit detection pipeline. We perform 1000 iterations of
transit injections into each of the non-detrended 7154 QLP and 1927 SPOC light
curves. The injected transits are modeled via batman (Kreidberg, 2015), with
periods, $P$, and radii, $R_{p}$, sampled from log-uniform distributions
ranging from 0.5 days to 20 days, and 1$\,\,\mathrm{R}_{\rm\earth}$ to
$10\,\,\mathrm{R}_{\rm\earth}$, impact parameters, $b$, selected from a
uniform distribution, between 0 and 0.8, and a transit time, $T_{0}$,
uniformly selected from a random phase. The models are resampled to 2-, 10-,
and 30-minute cadences, and multiplied into the non-detrended QLP and/or SPOC
light curves for each target. The injected light curve is then run through our
detection pipeline as described in Section 4.

An injected planet is recovered if the BLS signal-to-pink-noise threshold is
met and if the output ephemeris matches that of the injection. As with our
planet detection pipeline, we require a TCE to have a signal-to-pink-noise
$\geq 8$. The detected TCE is also required to have its detected transit time
and period from the BLS to match the injected time and period with a
significance of $\Delta\sigma_{P}\geq 2.5$ and $\Delta\sigma_{T}\geq 2$ (see
Coughlin et al. (2014) for definitions of $\Delta\sigma_{P}$ and
$\Delta\sigma_{T}$).

Figure 6: Recoverability maps from series of injection and recovery tests
performed on each star in our SPOC (left) and QLP (right) survey. We inject
1000 simulated planet signals into the light curves of every star within our
survey sampling orbital periods between 0.5 - 20 days, and radii between
$1-10\,\mathrm{R_{\oplus}}$. The darker shades indicate a higher
recoverability rate.

We present our derived injection and recovery maps in Figure 6. These maps
present our full series of injection and recovery tests summed across the
entire parent stellar population of QLP and SPOC respectively. Our
recoverability of larger planets ($\gtrsim$5 R⊕) is consistent across the two
surveys, but we see a sharper decline in recoverability in the region between
2-4 R⊕ for QLP compared to SPOC. This is consistent with our expectations due
to the difference in stellar populations between the two samples. The on-
average larger stellar radii in the QLP sample lead to more difficulties in
recovering smaller planets.

We also investigate our susceptibility to false alarms with an inverted light
curve test, as described in Appendix A, finding our manual vetting to be
sufficient in removing such cases.

##  6 Occurrence Rate Calculations

###  6.1 Forward Modeling

We perform a forward modeling test to compare the demographic of planets
around young stars against that of the _Kepler_ distribution. We synthesize a
population of planets using the _Kepler_ statistics as presented in Kunimoto &
Matthews (2020) and compute the expected number of recovered planets as per
our survey efficiency. The forward modeling process allows us to compare the
expected characteristics of the planet population against those that we
identify from the young stellar sample.

Figure 7: Number of combined confirmed planets (dark) and planet candidates
(light) detected in our SPOC (left) and QLP (right) planet surveys compared to
the expected yield (light yellow) from our forward model calculations using
the Kepler occurrence rates (Kunimoto & Matthews, 2020) and the expected yield
from evolution models for hydrogen-dominated (dark yellow) planets (Rogers &
Owen, 2021) as a function of orbital period (top) and planet radius (bottom).
We find a significant excess in planets with orbital periods between $6.2-12$
days around young stars. We also find a mild excess of young super-Neptunes
compared to the predicted _Kepler_ yield. Note, the forward modeled population
as a function of orbital period does not include planets in the super-Earth
bin to allow for a more direct comparison to our estimated occurrence rates,
as we do not attempt to derive an occurrence rate for the $1-2$ R⊕ bin. We
find that the yield in both our QLP and SPOC survey is consistent with
evolutionary models for the contraction of hydrogen-dominated atmospheres
undergoing photoevaporative escape.

We adopt the Kunimoto & Matthews (2020) _Kepler_ occurrence rates for F, G,
and K stars. We draw the occurrence rates assuming half Gaussians based on the
asymmetric uncertainties in Kunimoto & Matthews (2020) for each of the
simulations. For each star in our parent sample, we simulate a planetary
system based on its _Kepler_ period-radius planet frequency grid for its
specific stellar type.444The definition of the stellar type boundaries are
adopted to be the same as Kunimoto & Matthews (2020) For the M-dwarfs in our
sample, we use the occurrence rates for K stars to approximate their planet
occurrence rate.. The planets are first randomly assigned into a period-radius
cell based on the 2-dimensional cumulative probability. Then, their period and
radius are drawn within the cell assuming log-uniform distributions in both
axes. We then account for transit probability, and detection probability as
per the injection and recovery exercise described in Section 5. For each
planet, the transit probability is calculated as $0.8\times R_{\star}/a$, as
we reject near-grazing candidates with impact parameters of $b>0.8$ due to
their difficulty for confirmation. Detection probability is computed as the
percentage of recovered planets within a single period-radius bin as shown in
Figure 6 from our injection and recovery exercise. This forward modeling
process is performed 100 times each for the QLP and SPOC samples to quantify
its associated uncertainties. The resulting comparison between the expected
population and that identified from the _TESS_ sample is presented in Figure
7.

We compare the predicted planet yield from the forward modeling to the
detected sample further in Section 6.3. The forward modeling comparison
accounts for intrinsic differences in occurrence rates due to the differing
host star populations sampled by the QLP and SPOC samples from _TESS_ and the
_Kepler_ sample. Such differences, such as that of SPOC preferentially
sampling later-type stars, may contribute to an intrinsic shift in the planet
occurrence rates compared to that from the _Kepler_ sample, as well as a
difference in the measured occurrence rates between the two _TESS_ subsamples.

###  6.2 Approximate Bayesian Computation (ABC)

We use a Population Monte Carlo Approximate Bayesian Computation (ABC-PMC;
Beaumont et al., 2009) method to estimate the occurrence rates of young
planets. We use the ABC-PMC algorithm implemented in the python sampler
cosmoabc (Ishida et al., 2015), described in Kunimoto & Matthews (2020), which
requires three elements:

  1. 1.

A prior probability distribution, we adopt the per-bin polynomial distribution
as implemented in (Kunimoto & Matthews, 2020)

  2. 2.

A forward modeled population, used to generate a simulated set of data to
compare to observations

  3. 3.

A distance function, used to determine the consensus between the simulated
data and the observations

We use the prior probability distribution described in Kunimoto & Matthews
(2020) and modify their forward modeling method and distance function to
accurately represent the characteristics of the TESS data, rather than Kepler
data. Typically, in Kepler occurrence studies, the recoverability of transit
signals is computed by a function dependent on their estimated signal-to-noise
(SNR) based on the injection and recovery experiments (e.g. Christiansen et
al., 2015). In our study, we also require recoverability to be a function of
the host star spectral type, such that the differences in stellar variability
and transit duration are accounted for. This approach is also computationally
more efficient compared to the per-star recovery map method detailed in our
section 6.1. We use one realization of ABC-PMC run to test that the SNR
recovery curve approach yields similar occurrence rates compared to the per
star recovery map approach and use the recovery curves for the final
occurrence rate results presented in section 7.

We follow Kovács et al. (2002) to derive the expected signal-to-noise of an
injected transit signal for the SPOC observations. We adopt a similar signal-
to-noise calculation for the QLP light curves, modified to account for its
varying cadences (30- and 10-minutes),55530- and 10-minute observations, at
the time we downloaded the light curves there were no 200-second FFI light
curves made available on MAST we modify the calculation as follows:

For each injected planet with transit depth $\delta$, we split the light
curves of the star into two segments with the same cadence, and estimate the
number of transits, $N_{tr,i}$, and the average number of points in transit,
$N_{in,i}$ for each segment. The expected SNR is then expressed as:

$\mathrm{SNR_{\star}}=\delta\sqrt{\sum_{i}\frac{N_{tr,i}N_{in,i}}{\sigma_{i}^{2}}},$
(1)

where $\sigma_{i}$ is the per point noise level, and is calculated as
$1.48\times$ the median average deviation of the light curve.

Figure 8 shows our computed recoverability as a function of SNR by stellar
type in the QLP and SPOC survey. Our recoverability for both surveys plateau
at $\mathord{\sim}80\%$.

Figure 8: Recovery rate as a function of expected transit signal-to-noise
derived for QLP (top panel) and SPOC (bottom panel) sample. We compute the
recovery rate for F, G, K, and M type stars individually to illustrate
potential differences that might incur from differing levels of stellar
variability between the spectral classes.

In addition, we adopt a distance function similar to Kunimoto & Matthews
(2020) Equation 21 but require the computation to be done over the radius,
period, and stellar type bins, rather than just radius and period. This allows
us to account for the differences in stellar types in our parent stellar
samples.

Following Hsu et al. (2019) and Kunimoto & Matthews (2020), we first fit for
the occurrence rates over a fine period-radius grid. We break the period-
radius space into bins between periods of 0.78, 1.56, 3.125, 6.25, 12.5, and
20 days and radii of 0.5, 0.71, 1.41, 2.0, 2.83, 4.0, 5.66, 8.0, 11.31, and 16
R⊕. For each period-radius cell, the occurrence rate is obtained from fitting
five radius bins (two on each side in radius dimension) simultaneously. The
edge radius bins ($0.5-1.41$ $\,\mathrm{R}_{\rm\earth}$, $8-16$
$\,\mathrm{R}_{\rm\earth}$) are then excluded from the reported occurrence
rates. These finer resolution occurrence rate values are then combined into
the final occurrence rates we report in larger cells in section 7.

To account for the radius uncertainty of each planet and false positive
probability of the unconfirmed planet candidates in our sample, we perform
multiple realizations of the occurrence rate calculations. At each
realization, the radius of each planet is drawn from a Gaussian distribution
about their fitted mean and standard deviation values. Each unconfirmed planet
candidate is included, or excluded, based on a random draw as per their false
positive probability (see Section 4.4). Ten realizations of the SPOC and QLP
samples are performed, with their resulting posteriors concatenated to attain
the final occurrence rate posterior distribution, such that the uncertainties
associated with planet radius and false positive rate are incorporated into
our reported occurrence rates.

Additionally, we account for the possibility of stars falsely identified as
members of kinematically young comoving groups in our parent stellar
population. Our survey uses catalogs collated from Gaia kinematic associations
described in Gagné et al. (2018), Kounkel & Covey (2019), Ujjwal et al.
(2020), and Moranta et al. (2022). Our analysis of the rotational variability
of the stars in our parent sample indicates $10-30\%$ of the stellar
population does not exhibit rotational variations consistent with the
literature ages. To incorporate this uncertainty in our parent stellar
population, we randomly sample $70-90\%$ of the parent stellar population in
each of our ten realizations to estimate our occurrence rates. Each of the
identified planet candidates and confirmed planets show signs of variability
consistent with literature ages, as such we do not resample the planet
population.

Due to our visual vetting process, we induce non-reproducible biases,
especially near the detection limit, as signals induced by smaller planets are
more challenging to identify by eye. This would manifest as an under-
representation of the derived occurrence rates for small planets. In this
work, we do not correct our completeness maps to account for the possibility
of planets being mislabeled as false-positives or false-alarms in our vetting
process. We therefore do not attempt to derive an occurrence rate for the
$1-2\,\mathrm{R_{\oplus}}$ bin, which would be most heavily impacted by this
bias.

###  6.3 Comparing occurrence rates and forward modeling expectation

We find general agreement between the forward modeling results and the
occurrence rate calculations. The results from our forward model exercises are
presented in Figure 7.

Comparing bulk planet occurrence rates between different host star populations
can be made difficult by the numerous factors on which planet frequencies
depend. A number of works have demonstrated that the occurrence rate of small
planets depends strongly on spectral type (e.g. Kunimoto & Matthews, 2020).
The forward modeling approach accounts for spectral type dependencies of the
host population.

The forward modeled planet yield agrees well with that recovered from the
young planet population for mini-Neptunes, and for short period ($1.6-6.2$
day) planets. This agreement is also reflected in the occurrence rate results,
where the differences between our survey results and that from the Kepler
sample differ by $<2\sigma$. We find an excess of super-Neptunes, and planets
at longer period orbits compared to that expected from the forward model,
resulting in a noteably higher occurrence rate for these sub-populations as
discussed in Section 7.1.

###  6.4 Comparing QLP and SPOC results

The derived occurrence rates for our SPOC and QLP samples are presented in
Figure 9. There exist differences between the QLP and SPOC samples at the
$1-1.5\,\sigma$ level. We find a generally lower planet occurrence rate in the
QLP sample compared to SPOC. The small planet and parent stellar population
sample size makes it difficult to interpret the validity of this difference.

Figure 2 shows the QLP parent sample is skewed towards earlier type stars.
Kunimoto & Matthews (2020) finds K dwarfs to host $2-3\times$ more planets in
the $2-8\,\mathrm{R}_{\oplus}$ range compared to F dwarfs. The potential
differences in the QLP and SPOC occurrence rates highlight the difficulty in a
direct interpretation of planet frequency results without also accounting for
the bulk parent stellar type. If such stellar type dependencies are validated,
it points to the paucity of small planets around F stars being developed early
in their evolutionary history, as expected due to the differing levels of
irradiation received by these planets around early-type stars.

Target stars selected as a part of the initial TESS mission and TESS Guest
Investigator Programs to have their light curves sampled at 2-minute cadences
may bias the derived SPOC occurrence rate. These young stars may have been
known to host planets (i.e. V1298 Tau) or have been identified as planet-
candidate hosts in the FFIs subsequently receiving SPOC observations during
the extended TESS mission, resulting in an overall higher occurrence rate
compared to the QLP sample. Further, the SPOC sample favors later-type stars,
which are known to have higher occurrence rates (e.g. Kunimoto & Matthews,
2020).

It is also likely the contamination rate for cluster membership of the parent
population is different for QLP and SPOC. The low occurrence rate of planets
in the QLP sample is more consistent with that of the mature planet
population. To investigate the impact of our false positive rate for cluster
membership on the derived occurrence rates, we repeat our occurrence rate
calculations as described in section 6 without resampling the parent stellar
population for the $10-30\%$ assumed contamination rate. Using the full
stellar population observed by SPOC, we derive an occurrence rate of
28${}_{-8.5}^{+11}$% for mini-Neptunes and 25${}_{-7}^{+8.6}$% for super-
Neptunes. For the full QLP population, we calculate an occurrence rate of
$17^{+6.4}_{-5.0}\%$ for mini-Neptunes and $8^{+2.9}_{-2.3}\%$ for super-
Neptunes. The excess of super-Neptunes is still prevalent in these
calculations indicating that the uncertainties on our occurrence rates (see
section 7.1) are derived from the small planet number statistics rather than
the membership contamination rate of our parent sample. Similarly, we find the
excess of Neptune-sized planets with periods of $6.2-12$ day as well in the
full stellar sample. We derive an occurrence rate of 17${}_{-5.6}^{+7.3}$% for
the $6.2-12$ day bin with the SPOC sample and $8^{+3.5}_{-2.6}\%$ with QLP.
Secure young star membership lists may help future studies resolve the
discrepancies between the derived SPOC and QLP occurrence rates.

##  7 Occurrence rate results

Figure 9: Occurrence rates for short-period, small planets around stars
younger than 200 Myr in known clusters from the TESS 2-minute cadence
observations (blue) and FFIs (purple) as a function of planet radius (left)
and orbital period (right). We compare our derived occurrence rates to that of
the mature population (gold) as derived from the Kepler yield (Kunimoto &
Matthews, 2020).

The computed occurrence rates are shown in Figure 9. We breakdown the
occurrence rates as a function of planet radius over the mini-Neptune
($2-4\,\mathrm{R}_{\oplus}$) and super-Neptune ($4-8\,\mathrm{R}_{\oplus}$)
ranges. Due to only detecting two super-Earth in our SPOC and QLP surveys, we
do not attempt to derive an occurrence rate for the $1-2\,\mathrm{R_{\oplus}}$
bin. We also break down the occurrence rates as a function of orbital period,
over the bins of $1.6-3.1$, $3.1-6.2$, $6.2-12$, and $12-20$ days.

For short-period ($1.6-20$ days), small planets ($2-8$ R⊕) younger than 200
Myr, we derive an occurrence rate of $61^{+14}_{-13}\%$ from our SPOC survey
and $33^{+10}_{-7.3}\%$ from our QLP survey. For comparison, the Kepler
occurrence rate for similar planets in this period range is
$15.0^{+1.1}_{-1.3}\%$. This result is qualitatively consistent with previous
works that show an increase in the planet occurrence rate about young stars
(e.g. Rizzuto et al., 2017; Fernandes et al., 2023; Christiansen et al.,
2023).

###  7.1 The planet distribution about young stars is different to that of the
mature population

In both the QLP and SPOC samples, we find (1) a mild excess of super-Neptunes
in the young planet population, and (2) a significant excess of Neptune-sized
planets at $\mathord{\sim}10$ day orbital periods in the young planet
population.

####  7.1.1 We find a mild excess of super-Neptunes orbiting young stars

Previous young planet censuses have found a higher occurrence rate of super-
Neptunes (e.g. Rizzuto et al., 2017; Fernandes et al., 2023; Christiansen et
al., 2023) at ages up to that of the Hyades cluster. David et al. (2021) and
Berger et al. (2023) found the distribution of small planets is continuously
modified over the billion-year timescale, pointing to the role core-powered
mechanisms play in shaping the mature planet population.

Our work presents the radius distribuiton of planets amongst one of the
youngest stellar distributions studied so far. We find a mild excess of super-
Neptune-sized planets around the youngest surveyed population in literature.
The _Kepler_ sample yielded an occurrence rate of $0.9\pm 0.2\%$ for super-
Neptunes in the mature population (Kunimoto & Matthews, 2020). We derive
higher occurrence rates for super-Neptunes (SPOC $27_{-8}^{+10}\%$ differing
to the equivalent _Kepler_ occurrence rate at $2.9\sigma$ significance; QLP
$13^{+3.9}_{-4.9}$% differing from _Kepler_ at $2.8\sigma$) in our $<200$ Myr
sample (see Figure 9). A similar significance exists in the forward modeling
comparison, at $2.4\sigma$ for SPOC and $1.4\sigma$ for QLP. These
qualitatively agree with previous works showing an excess of inflated planets
around young stars.

####  7.1.2 We find a significant excess of $\mathord{\sim}10$ day period
planets around young stars

The period distribution of young planets appears distinctively different from
that of the mature population. Within our SPOC sample, we predict
0.94${}^{+1.1}_{-0.94}$ planets between $6.2-12$ days and find 7. The same
excess of $\mathord{\sim}10$ day period planets is also apparent in our QLP
sample. The _Kepler_ statistic predicts 1.6$\pm 1.1$ planets between $6.2-12$
days, while we recover 9 such planets.

This apparent period evolution between our $<200$ million year sample and that
of the Kepler distribution helps us understand the mechanisms that shape the
sub-Saturn desert. Hallatt & Lee (2022) and Thorngren et al. (2023) find that
XUV-driven mass-loss can lead to runaway envelope stripping of the least dense
hot Saturns. In this scenario, close-in planets with initial radii
$>22\,\mathrm{R}_{\oplus}$ undergo runaway mass loss within 100 Myr. In
addition, close-in planets that have undergone runaway mass-loss remain puffy
at the $\mathord{\sim}8\,\mathrm{R_{\oplus}}$ radius due to residual heat,
before cooling and contracting to $\mathord{\sim}4\,\mathrm{R_{\oplus}}$ at
time scales of over $\mathord{\sim}500$ Myr.

Hansen & Murray (2013) finds that an initial over-density of planets in the
$5-10$ day orbital period range is a natural result of in-situ formation of
rocky cores with subsequent accretion of gaseous envelopes. This distance
corresponds to the dust sublimation radius for Sun-like stars (Swift et al.,
2013). Tidal evolution models from Hansen & Murray (2015) show that this
initial buildup of planets at $\mathord{\sim}8$ days can migrate inward via
tidal dissipation and eccentricity damping at the billion-year timescale if
they exist in compact multiplanet systems that mutually undergo eccentricity
excitation. Such tidally driven effects may be responsible for smoothing the
over-density of young planets into the mature planet period distribution.

###  7.2 Comparison to Hydrogen-helium dominated planet evolution models

Mapping the distribution of young planets has the potential to disentangle
otherwise degenerate modes of planet formation. Some super-Earths and Neptune-
sized planets may have formed in close-in volatile- and gas-depleted regions
of the protoplanet disk (e.g. Lee et al., 2014), others may be composed of
water-rich envelopes and were formed further out (e.g. Zeng et al., 2019;
Luque & Pallé, 2022). These different formation modes produce planets of
similar bulk physical properties in the mature planet distribution. In the
water-rich scenario, the planets may have formed in the outer ice-rich
protoplanet disk, migrating inward to their current locations (e.g. Mordasini
et al., 2009). These planets may have silicate cores, icy mantles, and steam
atmospheres (e.g. Rogers & Seager, 2010; Zeng et al., 2019).

In the alternate scenario, the planets may be born with a thick hydrogen-
helium envelope surrounding an iron-silicate core in the volatile-depleted
inner regions of the disk (e.g. Lee et al., 2014). Some of these hydrogen-
helium dominated planets may undergo runaway mass loss early in their
evolution driven by boil-off (Owen & Wu, 2016; Ginzburg et al., 2016),
photoevaporation (Lopez & Fortney, 2013; Owen & Wu, 2013) and core-powered
(Ginzburg et al., 2018; Gupta & Schlichting, 2019) mechanisms. Owen &
Schlichting (2023) suggested that a combination of these mechanisms is likely
acting with timescales from $\mathord{\sim}100$ Myrs to Gyrs, although the
details of this timeline require further study.

These different formation pathways differ most drastically early in their
evolution. In particular, hydrogen-helium dominated atmospheres are expected
to experience rapid contraction within the first few hundred million to
billion years of evolution (e.g. Lopez et al., 2012; Chen & Rogers, 2016),
while steam-dominated atmospheres experience very mild radius contraction as
they cool (Lopez et al., 2012; Lopez, 2017), and our young planet population
should resemble that of the Kepler distribution. Furthermore, atmospheric
escape mechanisms should be evident with an increased frequency of planets
$\gtrsim 1.8\,\,\mathrm{R}_{\rm\earth}$ around younger stars. As some of the
planets have their atmospheres stripped and transition to below the radius
valley, this frequency should decrease with time.

To test these predictions, we perform a forward modeling exercise using the
models presented in Rogers & Owen (2021) and Rogers et al. (2023). These
models consider the thermal contraction and photoevaporative mass loss for
small, close-in exoplanets hosting hydrogen-helium atmospheres. We apply our
forward modeling framework (Section 6.1) to a population of synthesized
planets about our stellar population, to test for the consistency between the
observed distribution and a synthetic distribution of planets evolving through
these physical processes.

For each star in the underlying SPOC and QLP stellar samples presented in
Table 1, we forward-model 100 planets, assuming the underlying core mass,
initial atmospheric mass fraction, core composition and orbital period
distributions inferred in Rogers & Owen (2021). The synthesized planets have
ages corresponding to that estimated for each respective association as listed
in Table 1. We then randomly draw planets from this sample to match the Kepler
occurrence rates, as done in Rogers & Owen (2021). This population, therefore
represents a sample of young planets about our observed stellar sample, under
the photoevaporation model, that would eventually evolve to reproduce the
Kepler sample of $\mathord{\sim}5$ Gyr old super-Earths and mini-Neptunes.

We compare the predicted yield to our QLP and SPOC planet yields in Figure 7.
The models are in agreement at the $0.5-1.5\,\sigma$ level, showing the
derived occurrence rates in this work are consistent with the contraction of
hydrogen-dominated atmospheres undergoing photoevaporative escape (Rogers &
Owen, 2021). Of note, one can see that the occurrence rate of super-Neptunes
in the Rogers & Owen (2021) models is increased when compared to the Kepler
sample in Figure 9. This is because the young mini-Neputunes in this model
host significant hydrogen-helium atmospheres that are inflated to super-
Neptune sizes for ages $<200$ Myrs. While the results are generally
inconsistent with non-contracting steam-based atmospheres (see Figure 7), we
leave model comparisons of evolving hydrogen vs. high mean-molecular weight
(e.g. steam) atmospheres for future work.

##  8 Conclusion

Understanding how the occurrence rates of small, short-period planets evolve
as a function of age can unveil the universal mechanisms driving planetary
evolution. This paper presents the occurrence rates for young ($\leq 200$
Myr), short-period $1.6-20$ days), small ($2-8\,\mathrm{R_{\oplus}}$) planets
from both the short-cadence and FFI light curves from TESS.

  * •

We conduct two independent planet surveys with the SPOC data (1927 stars) and
the FFIs (7154 stars) from TESS targeting known moving groups and cluster
members with literature ages $\leq$200 Myr (Gagné et al., 2018; Kounkel &
Covey, 2019; Ujjwal et al., 2020; Moranta et al., 2022).

  * •

We identify a total of 23 planets, including 4 new planet candidates in the
SPOC and QLP data previously not identified as TOIs. Initial ground-based
follow-up rule out any obvious astrophysical false positives.

  * •

We derive occurrence rates for young, short-period, small planets using an
Approximate Bayesian Computation approach, finding an increase in the
occurrence of super-Neptunes within the $\leq 200$ Myr population, consistent
with models of planetary evolution, and a significant excess of planets
residing at $\mathord{\sim}10$ day orbital periods when compared to the mature
population of exoplanets.

## Acknowledgements

We respectfully acknowledge the traditional custodians of the lands on which
we conducted this research and throughout Australia. We recognize their
continued cultural and spiritual connection to the land, waterways, cosmos and
community. We pay our deepest respects to all Elders, present and emerging,
and the people of the Giabal, Jarowair, and Kambuwal nations, upon whose lands
this research was conducted.

SV thanks the support of the Fulbright U.S. Student Program, which is
sponsored by the U.S. Department of State and Australian-American Fulbright
Commission. The contents of this paper are solely the responsibility of the
author and do not necessarily represent the official views of the Fulbright
Program, the Government of the United States, or the Australian-American
Fulbright Commission. GZ thanks the support of the Australian Research Council
projects DE210101893 and FT230100517. CH thanks the support of the ARC DECRA
project DE200101840.

This research has made use of the NASA Exoplanet Archive, which is operated by
the California Institute of Technology, under contract with the National
Aeronautics and Space Administration under the Exoplanet Exploration Program.
Funding for the TESS mission is provided by NASA’s Science Mission
directorate. We acknowledge the use of public TESS Alert data from pipelines
at the TESS Science Office and at the TESS Science Processing Operations
Center. This research has made use of the Exoplanet Follow-up Observation
Program (EXOFOP) website, which is operated by the California Institute of
Technology, under contract with the National Aeronautics and Space
Administration under the Exoplanet Exploration Program. This paper includes
data collected by the TESS mission, which are publicly available from the
Mikulski Archive for Space Telescopes (MAST). Resources supporting this work
were provided by the NASA High-End Computing (HEC) Program through the NASA
Advanced Supercomputing (NAS) Division at Ames Research Center for the
production of the SPOC data products.

##  Appendix A Inverted Light Curve Test

False alarms are particularly prevalent at lower signal-to-noises. Kunimoto et
al. (2023) illustrates the presence of an excess of false alarms from BLS
searches of TESS light curves at periods related to both astrophysical and
instrumental signatures. Specifically, Kunimoto et al. (2023) found build-ups
of BLS false-alarm detections at aliases of a TESS orbit, $\mathord{\sim}13.7$
days. As this work performs a BLS search for planets with periods on the order
of a TESS orbit and 0.5$\times$ a TESS orbit, we perform a blind planet search
on both inverted and non-inverted light curves to test the reliability of our
detections.

We randomly sample 1000 stars in both our QLP and SPOC parent population to
quantify our false alarm rate. We ensure that both subsamples include the
light curves of all planet hosts detected in this work (see Table 2). For each
individual light curve, we perform a random draw to determine if the light
curve is to be inverted. For stars selected to have their light curves
inverted, we follow Thompson et al. (2018), multiplying the zero-mean flux by
negative one to invert the light curve. Each light curve is run through our
planet detection pipeline (see Section 4).

From our test, no signals from our inverted light curve sample were wrongly
classified as planet candidates. Of the positive light curves, all planet
candidates were successfully recovered. In the QLP test sample, all the planet
hosts were randomly selected to have their light curves inverted, none of
which triggered a TCE. In the SPOC test sample, we identified TOI-2550.01 and
DS Tuc Ab as planet candidates, neither of which had their light curves
inverted. Additionally, HD 114082 b triggered a TCE, however, the period and
radius are not within our search parameters and therefore was not identified
as a planet candidate. The remaining planet hosts all had their light curves
inverted. The inverted light curve of AU Mic triggered a TCE, but our vetting
procedure rejected the event due to asymmetries in the detected possible
transit events.

##  Appendix B Robustness of occurrence rate on sample assumptions

To verify our derived occurrence rate calculations, we perform a series of
checks to test the occurrence rates’ robustness to astrophysical false
positives and false positives induced by human vetting.

Table 3: Calculated Occurrence Rates

Occurrence Rate Bin | This work∗ |  | Without Filtering
---
for Binarity
| Confirmed
---
planets only
| All planets and
---
planet candidates
SPOC |  |  |  |
Mini-Neputnes ($2-4\,\mathrm{R_{\oplus}}$) | 35${}_{-10}^{+13}$% | 31${}_{-9.4}^{+12}$% (0.25$\sigma$) | 29${}_{-9.3}^{+12}$% (0.38$\sigma$) | 36${}_{-9.9}^{+12}$% (0.063$\sigma$)
Super-Neputnes ($4-8\,\mathrm{R_{\oplus}}$) | 27${}_{-8}^{+10}$% | 21${}_{-6.3}^{+8.5}$% (0.51$\sigma$) | 27${}_{-8.5}^{+10}$% (0$\sigma$) | 31${}_{-8.9}^{+12}$% (0.29$\sigma$)
$1.6-3.1$ days | 4.6${}_{-1.7}^{+2.3}$% | 3.6${}_{-1.3}^{+1.9}$% (0.39 $\sigma$) |  $\cdots$ | 4.4${}_{-1.6}^{+2.2}$% (0.072$\sigma$)
$3.1-6.2$ days | 6.6${}_{-2.4}^{+3.4}$% | 6.5${}_{-2.5}^{+5}$% (0.021$\sigma$) | 5.7${}_{-2}^{+2.9}$% (0.24$\sigma$) | 10${}_{-3.3}^{+3.6}$% (0.75$\sigma$)
$6.2-12$ days | 18${}_{-6.2}^{+7.8}$% | 18${}_{-5.9}^{+6.7}$% (0$\sigma$) | 19${}_{-6.4}^{+8.3}$% (0.098$\sigma$) | 21${}_{-6.4}^{+8.4}$% (0.29$\sigma$)
$12-20$ days | 31${}_{-11}^{+14}$% | 23${}_{-8.8}^{+12}$% (0.49$\sigma$) | 29${}_{-10}^{+14}$% (0.12$\sigma$) | 31${}_{-11}^{+14}$% (0$\sigma$)
QLP |  |  |  |
Mini-Neputnes ($2-4\,\mathrm{R_{\oplus}}$) | 22${}_{-6.8}^{+8.6}$% | 18${}_{-5.5}^{+7.9}$% ($0.39\sigma$) | 17${}_{-5.1}^{+7.2}$% (0.51$\sigma$) | 19${}_{-6.2}^{+8.1}$% (0.28$\sigma$)
Super-Neputnes ($4-8\,\mathrm{R_{\oplus}}$) | 13${}_{-3.9}^{+4.9}$% | 9.5${}_{-2.9}^{+4}$% (0.63$\sigma$) | 8.0${}_{-2.3}^{+3.0}$% (0.98$\sigma$) | 10${}_{-3.1}^{+4.1}$% (0.53$\sigma$)
$1.6-3.1$ days | 2.6${}_{-1.1}^{+1.7}$% | 1.6${}_{-0.61}^{+0.84}$% ($0.63\sigma$) |  $\cdots$ | 2.0${}_{-0.7}^{+1.1}$% (0.36$\sigma$)
$3.1-6.2$ days | 3.7${}_{-1.3}^{+1.8}$% | 3.9${}_{-1.3}^{+1.8}$% ($0.090\sigma$) | 2.8${}_{-1}^{+1.3}$% (0.47$\sigma$) | 3.5${}_{-1.3}^{+2}$% (0.088$\sigma$)
$6.2-12$ days | 9.9${}_{-3.3}^{+4.3}$% | 7.8${}_{-2.7}^{+3.8}$% ($0.42\sigma$) | 7.3${}_{-2.6}^{+3.4}$% (0.53$\sigma$) | 8.6${}_{-2.8}^{+3.7}$% (0.26$\sigma$)
$12-20$ days | 19${}_{-6.8}^{+9}$% | 14${}_{-5.5}^{+7.6}$% ($0.49\sigma$) | 13${}_{-4.8}^{+6.9}$% (0.61$\sigma$) | 15${}_{-5.6}^{+7.7}$% (0.39$\sigma$)

∗ Sigma differences are computed against this column. These values are the
same as presented in Figure 9.

###  B.1 Impact of RUWE threshold on occurrence rates

We investigate the impact of binarity on our derived occurrence rates by
repeating our occurrence rate calculations without removing high RUWE stars.
We collate our stellar parent population following Section 3. We find 8852
stars with TESS observations. Of which, 8505 were observed in the FFIs and
2243 were observed in the target pixel stamps.

The occurrence rates derived from the full sample, without attempting to
filter for binarity, agree with our presented occurrence rates at the
$<1\sigma$ level. Similarly to the occurrence rates presented in Figure 9, we
find a mild excess of super-Neptunes and a significant excess of Neptune-sized
planets at orbital periods of $\mathord{\sim}10$ days in the population
without filtering for binarity. We search for transiting planets following
Section 4, and characterize our pipeline completeness following Section 5. The
planet sample recovered is identical to the sample presented in Table 2. We
follow Section 6, and perform 10 realizations of both the QLP and SPOC
occurrence rate calculations, with each realization randomly sampling
$70-90\%$ of the stellar population to account for the membership
contamination rate. See Table 3 full results.

###  B.2 Impact of false-positive rate assumptions on occurrence rates

To investigate the degree to which our adopted false-positive rate assumptions
via TRICERATOPS impact our derived occurrence rates, we explore the bounding
cases where (a) all planet candidates are counted as confirmed planets, and
(b) only verified/published planets are included in our occurrence rate
calculation and all planet candidates are treated as false-alarms. We
calculate the occurrence rates following Section 6.

In both bounding cases, we find that the main conclusions presented in Section
7 are also present $--$ a mild excess of super-Neptunes and a significant
excess of planet with orbital periods of $\mathord{\sim}10$ days when compared
to the Kepler statistics. Our full results are presented in Table 3.

##  Appendix C New Planetary Candidates

We report from our planet search the following new planet candidates
previously unidentified in the public _TESS_ candidate list: TIC 150070085.01
(SPOC and QLP), TIC 88785435, and TIC 434398831.01 and TIC 434398831.02 (QLP).

All new candidates have been submitted to the TESS Follow-Up Observing Program
Working Group (TFOP WG) for ground-based follow-up observations for further
confirmation and characterization. We also conducted photometric and
spectroscopic ground-based follow-up observations for TIC 434398831 to allow
us to rule out any false positive scenarios. Further, TIC 434398831 has been
observed by the CHaracterising ExOPlanet Satellite (CHEOPS; Benz et al., 2021)
as a part of AO-4 program PR230002 (PI Vach).

Precise planet radii and periods are key for calculating planetary occurrence
rates. To properly characterize the newly identified planet candidates, we
perform a global model to constrain the planetary parameters for the
individual systems. We follow Zhou et al. (2021) and model the stellar
properties and transit light curve parameters simultaneously. The free
parameters for our global model are stellar mass, $M_{\star}$, stellar radius,
$R_{\star}$, the orbital period, $P$, the time of transit center, $T_{0}$, the
radius ratio, $R_{P}/R_{\star}$, and transit inclination $i$, and eccentricity
parameters $\sqrt{e}\cos\omega$ and $\sqrt{e}\sin\omega$ that influence the
transit duration. The limb-darkening parameters are fixed to that interpolated
from Claret (2017). The transits were calculated using the BATMAN (Kreidberg,
2015) implementation of the Mandel & Agol (2002) models. At each iteration, we
interpolate the stellar parameters onto the MIST isochrone (Dotter, 2016) to
estimate their magnitudes. These isochrone magnitudes are compared against
that observed in the Gaia, (Gaia Collaboration et al., 2016), 2MASS, and WISE
bands, corrected for the distance modulus via the _Gaia_ parallax. We
calculate a likelihood function incorporating the magnitudes and light curves.
The best-fit parameters and posteriors are explored via a Markov chain Monte
Carlo run via emcee (Foreman-Mackey et al., 2013). The derived planet radii
and associated uncertainties are incorporated into our occurrence rate
calculations (See Section 6). We present our best-fit values for the new
planet candidates identified in this work below.

#### TIC 150070085

TIC 150070085 was observed by TESS in Sectors 20 and 47, receiving both two-
minute target pixel stamps and FFI observations. TIC 150070085.01 was
identified in both our SPOC and QLP planet search with a signal-to-pink noise
of 8.2 and 9.5 respectively. Our vetting diagnostic figure for TIC
150070085.01 is shown in Figure 10. We utilize the two-minute cadence
observations for our global model.

TIC 150070085 ($T_{\mathrm{eff}}=6070\pm 146$ K) is located in the Theia 214
moving group. We use our derived age to inform our model by constraining the
stellar age. Our global best-fit model for the TIC 150070085 system finds
$R_{P}=3.64\pm 0.41\,\mathrm{R}_{\oplus}$, $t_{0}=2458843.8908\pm 0.0041$ BJD,
$P=10.4745\pm 0.0036$ days, and $t_{\mathrm{dur}}=0.1466\pm 0.0031$ days. The
stellar rotation period, $P_{rot}=3.0$ days, calculated from the QLP light
curves shows the rotational spread for Theia 214 is consistent with the
literature age of $\mathord{\sim}100$ Myr.

Figure 10: Vetting plot for TIC 150070085.01. The upper panel shows the TESS
SPOC light curve (grey) from sector 47. We model the stellar variability with
a spline (blue) and mark the transits in the full light curves (red
triangles). The middle panels show the phase-folded binned light curve (red)
on the left using the BLS period and transit times overlaid with the full
phase-folded FFI light curve (grey) and the per-pixel FFI light curves on the
right. The bottom panel shows the CMD of the candidate (red star) and its
comoving group (light red), Theia 214 (96 Myr). The Kepler Input Catalog
(Mathur et al., 2017) CMD is over-plotted for reference (grey-scale). On the
right, we plot our derived stellar rotation periods as a function of effective
temperature of TIC 150070085 (red star) and the members of Theia 214 (light
red). The rotation periods of the 120 Myr old Pleiades association are
overlaid in grey for reference (Rebull et al., 2016)

#### TIC 88785435

TIC 88785435 was observed by TESS in Sectors 11 and 38 in the FFIs. TIC
88785435.01 was identified in our QLP planet search with a signal-to-pink
noise of 9.47. Figure 11 shows our vetting diagnostic figure for TIC
88785435.01.

TIC 88785435 ($T_{\mathrm{eff}}=4000\pm 150$ K) is located in the UCL/LCC
moving group. We use our derived age to inform our model by constraining the
stellar age. Our global best-fit model for the TIC 88785435 system finds
$R_{P}=4.88\pm 0.28\,\mathrm{R}_{\oplus}$, $t_{0}=2458609.2308\pm 0.0036$ BJD,
$P=10.508907\pm 0.000035$ days, and $t_{\mathrm{dur}}=0.1466\pm 0.0031$ days.
The stellar rotation period, $P_{rot}=8.6$ days, is calculated from the QLP
light curves, consistent with the age of literature age of UCL/LCC.

Figure 11: Vetting plot for TIC 88785435.01 following the style of Figure 10.

#### TIC 434398831

TIC 434398831 (Vach et al. in prep) received FFI observations over _TESS_
sectors 6, 33, 43, 44, and 45. Our planet search identifies two planet
candidates, TIC 434398831.01 (signal-to-pink noise of 11.8) and TIC
434398831.02 (signal-to-pink noise of 15.7). Our best fit parameters for TIC
434398831.01 are $R_{P}=3.65\pm 0.28\,\mathrm{R_{\oplus}}$, $P=3.68551266\pm
0.000019$ days, $t_{0}=2458468.6318\pm 0.0052$ BJD, and
$t_{\mathrm{dur}}=0.1061\pm 0.0014$ days, and for TIC 434398831.02 are
$R_{P}=5.41\pm 0.32\,\mathrm{R_{\oplus}}$ $P=6.210377\pm 0.000017$ days,
$t_{0}=2458470.6110\pm 0.0027$ BJD, and $t_{\mathrm{dur}}=0.1289\pm 0.010$
days.

The host star TIC 434398831 is on the pre-main sequence, with an effective
temperature of $T_{\mathrm{eff}}=5554\pm 144$ K. Previous works place TIC
434398831 in the Theia 116 moving group. We measure the stellar rotation
period, $P_{rot}=2.0$ days, using the FFI light curves. After analyzing the
members of Theia 116, we find the rotational spread to be consistent with
$\mathord{\sim}50$ Myr, agreeing with the isochrone-derived age.

Three transits of TIC 434398831.01 and two transits of TIC 434398831.02 have
been observed by ESA’s CHEOPS mission (AO-4 program PR230002). Paired with
extensive LCO ground-based follow-up of TIC 434398831.01 and .02, we were able
to clear the field of nearby eclipsing binaries (NEBs) and rule out any false-
positive scenarios. Therefore, we classify TIC 434398831.01 and .02 as
confirmed planets in our occurrence rate calculations and adopt false-positive
rates of 0 for both (see Table 2).

Figure 12: Vetting plot for TIC 434398831.01 (left) and TIC 434398831.02
(right) following the style of Figure 10.

##  Appendix D Rotation Analysis Plots

Our parent stellar population is a collation of multiple catalogs (see section
3) from various clustering studies (Gagné et al., 2018; Kounkel & Covey, 2019;
Ujjwal et al., 2020; Moranta et al., 2022). To independently measure the field
star contamination rate of our collated stellar population, we make use of the
TESS FFI light curves to measure the rotation periods and variability
signatures of stars in our survey with $T_{\mathrm{eff}}<6500$ K. As we do not
attempt to measure rotation periods longer than the duration of a single TESS
orbit, we sample periods between 0.5-12 days using a Lomb-Scargle periodogram.
Figure 13 shows examples of vetting figures that were used to classify
rotational modulation light curves. We visually examine each light curve for
signatures of rotational variability, and report measured rotation periods
where possible in Table 1.

Example: Rotational variability
---
Example: Non-rotational variability
Example: No variability
Example: Binarity
Figure 13: Sample rotation vetting plots. Each row shows the nondetrended FFI
light curve (left) from a single sector of TESS observations, the phase-folded
light curve based on the measured rotation period (middle), and the Lomb-
Scargle (Lomb, 1976; Scargle, 1982) periodogram (right) sampling rotation
periods between 0.5 and 12 days. The first panel displays an example
rotational vetting plot in which a measured rotational period was identified.
The second panel is an example of a variable star, but an incorrect/no
rotation period is measured. The third panel illustrates an example where
there is no apparent activity or rotation measured. The fourth panel is an
example of a binary system.

## References

  * Astropy Collaboration et al. (2013) Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068
  * Astropy Collaboration et al. (2018) Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f
  * Astropy Collaboration et al. (2022) Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167, doi: 10.3847/1538-4357/ac7c74
  * Barber et al. (2022) Barber, M. G., Mann, A. W., Bush, J. L., et al. 2022, AJ, 164, 88, doi: 10.3847/1538-3881/ac7b28
  * Battley et al. (2020) Battley, M. P., Pollacco, D., & Armstrong, D. J. 2020, MNRAS, 496, 1197, doi: 10.1093/mnras/staa1626
  * Beaugé & Nesvorný (2013) Beaugé, C., & Nesvorný, D. 2013, ApJ, 763, 12, doi: 10.1088/0004-637X/763/1/12
  * Beaumont et al. (2009) Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., & Robert, C. P. 2009, Biometrika, 96, 983, doi: 10.1093/biomet/asp052
  * Benz et al. (2021) Benz, W., Broeg, C., Fortier, A., et al. 2021, Experimental Astronomy, 51, 109, doi: 10.1007/s10686-020-09679-4
  * Berger et al. (2020) Berger, T. A., Huber, D., Gaidos, E., van Saders, J. L., & Weiss, L. M. 2020, AJ, 160, 108, doi: 10.3847/1538-3881/aba18a
  * Berger et al. (2023) Berger, T. A., Schlieder, J. E., Huber, D., & Barclay, T. 2023, arXiv e-prints, arXiv:2302.00009, doi: 10.48550/arXiv.2302.00009
  * Borucki et al. (2010) Borucki, W. J., Koch, D., Basri, G., et al. 2010, Science, 327, 977, doi: 10.1126/science.1185402
  * Bouma et al. (2023) Bouma, L. G., Palumbo, E. K., & Hillenbrand, L. A. 2023, ApJ, 947, L3, doi: 10.3847/2041-8213/acc589
  * Bouma et al. (2020) Bouma, L. G., Hartman, J. D., Brahm, R., et al. 2020, AJ, 160, 239, doi: 10.3847/1538-3881/abb9ab
  * Bouma et al. (2022) Bouma, L. G., Kerr, R., Curtis, J. L., et al. 2022, AJ, 164, 215, doi: 10.3847/1538-3881/ac93ff
  * Bryson et al. (2013) Bryson, S. T., Jenkins, J. M., Gilliland, R. L., et al. 2013, PASP, 125, 889, doi: 10.1086/671767
  * Chatterjee et al. (2008) Chatterjee, S., Ford, E. B., Matsumura, S., & Rasio, F. A. 2008, ApJ, 686, 580, doi: 10.1086/590227
  * Chen & Rogers (2016) Chen, H., & Rogers, L. A. 2016, ApJ, 831, 180, doi: 10.3847/0004-637X/831/2/180
  * Christiansen et al. (2015) Christiansen, J. L., Clarke, B. D., Burke, C. J., et al. 2015, ApJ, 810, 95, doi: 10.1088/0004-637X/810/2/95
  * Christiansen et al. (2023) Christiansen, J. L., Zink, J. K., Hardegree-Ullman, K. K., et al. 2023, AJ, 166, 248, doi: 10.3847/1538-3881/acf9f9
  * Claret (2017) Claret, A. 2017, A&A, 600, A30, doi: 10.1051/0004-6361/201629705
  * Cloutier & Menou (2020) Cloutier, R., & Menou, K. 2020, AJ, 159, 211, doi: 10.3847/1538-3881/ab8237
  * Coughlin et al. (2014) Coughlin, J. L., Thompson, S. E., Bryson, S. T., et al. 2014, AJ, 147, 119, doi: 10.1088/0004-6256/147/5/119
  * Curtis et al. (2019) Curtis, J. L., Agüeros, M. A., Mamajek, E. E., Wright, J. T., & Cummings, J. D. 2019, AJ, 158, 77, doi: 10.3847/1538-3881/ab2899
  * David et al. (2019) David, T. J., Petigura, E. A., Luger, R., et al. 2019, ApJ, 885, L12, doi: 10.3847/2041-8213/ab4c99
  * David et al. (2016) David, T. J., Hillenbrand, L. A., Petigura, E. A., et al. 2016, Nature, 534, 658, doi: 10.1038/nature18293
  * David et al. (2021) David, T. J., Contardo, G., Sandoval, A., et al. 2021, AJ, 161, 265, doi: 10.3847/1538-3881/abf439
  * Dotter (2016) Dotter, A. 2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8
  * El-Badry et al. (2021) El-Badry, K., Rix, H.-W., & Heintz, T. M. 2021, MNRAS, 506, 2269, doi: 10.1093/mnras/stab323
  * Fernandes et al. (2022) Fernandes, R. B., Mulders, G. D., Pascucci, I., et al. 2022, AJ, 164, 78, doi: 10.3847/1538-3881/ac7b29
  * Fernandes et al. (2023) Fernandes, R. B., Hardegree-Ullman, K. K., Pascucci, I., et al. 2023, AJ, 166, 175, doi: 10.3847/1538-3881/acf4f0
  * Foreman-Mackey et al. (2013) Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067
  * Fulton et al. (2017) Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109, doi: 10.3847/1538-3881/aa80eb
  * Gagné et al. (2018) Gagné, J., Mamajek, E. E., Malo, L., et al. 2018, ApJ, 856, 23, doi: 10.3847/1538-4357/aaae09
  * Gaia Collaboration et al. (2016) Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1, doi: 10.1051/0004-6361/201629272
  * Gaia Collaboration et al. (2018) Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1, doi: 10.1051/0004-6361/201833051
  * Gaia Collaboration et al. (2021) —. 2021, A&A, 649, A1, doi: 10.1051/0004-6361/202039657
  * Giacalone & Dressing (2020) Giacalone, S., & Dressing, C. D. 2020, triceratops: Candidate exoplanet rating tool.  http://ascl.net/2002.004
  * Giacalone et al. (2021) Giacalone, S., Dressing, C. D., Jensen, E. L. N., et al. 2021, AJ, 161, 24, doi: 10.3847/1538-3881/abc6af
  * Ginzburg et al. (2016) Ginzburg, S., Schlichting, H. E., & Sari, R. 2016, ApJ, 825, 29, doi: 10.3847/0004-637X/825/1/29
  * Ginzburg et al. (2018) —. 2018, MNRAS, 476, 759, doi: 10.1093/mnras/sty290
  * Godoy-Rivera et al. (2021) Godoy-Rivera, D., Pinsonneault, M. H., & Rebull, L. M. 2021, ApJS, 257, 46, doi: 10.3847/1538-4365/ac2058
  * Gupta & Schlichting (2019) Gupta, A., & Schlichting, H. E. 2019, MNRAS, 487, 24, doi: 10.1093/mnras/stz1230
  * Hallatt & Lee (2022) Hallatt, T., & Lee, E. J. 2022, ApJ, 924, 9, doi: 10.3847/1538-4357/ac32c9
  * Hansen & Murray (2013) Hansen, B. M. S., & Murray, N. 2013, ApJ, 775, 53, doi: 10.1088/0004-637X/775/1/53
  * Hansen & Murray (2015) —. 2015, MNRAS, 448, 1044, doi: 10.1093/mnras/stv049
  * Hardegree-Ullman et al. (2020) Hardegree-Ullman, K. K., Zink, J. K., Christiansen, J. L., et al. 2020, ApJS, 247, 28, doi: 10.3847/1538-4365/ab7230
  * Hartman & Bakos (2016) Hartman, J. D., & Bakos, G. Á. 2016, Astronomy and Computing, 17, 1, doi: 10.1016/j.ascom.2016.05.006
  * Howell et al. (2014) Howell, S. B., Sobeck, C., Haas, M., et al. 2014, PASP, 126, 398, doi: 10.1086/676406
  * Hsu et al. (2019) Hsu, D. C., Ford, E. B., Ragozzine, D., & Ashby, K. 2019, AJ, 158, 109, doi: 10.3847/1538-3881/ab31ab
  * Huang et al. (2020a) Huang, C. X., Vanderburg, A., Pál, A., et al. 2020a, Research Notes of the American Astronomical Society, 4, 204, doi: 10.3847/2515-5172/abca2e
  * Huang et al. (2020b) —. 2020b, Research Notes of the American Astronomical Society, 4, 206, doi: 10.3847/2515-5172/abca2d
  * Hunter (2007) Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55
  * Ida & Lin (2004) Ida, S., & Lin, D. N. C. 2004, ApJ, 604, 388, doi: 10.1086/381724
  * Ishida et al. (2015) Ishida, E. E. O., Vitenti, S. D. P., Penna-Lima, M., et al. 2015, Astronomy and Computing, 13, 1, doi: 10.1016/j.ascom.2015.09.001
  * Jackson et al. (2012) Jackson, A. P., Davis, T. A., & Wheatley, P. J. 2012, MNRAS, 422, 2024, doi: 10.1111/j.1365-2966.2012.20657.x
  * Jenkins et al. (2016) Jenkins, J. M., Twicken, J. D., McCauliff, S., et al. 2016, in Proc. SPIE, Vol. 9913, Software and Cyberinfrastructure for Astronomy IV, 99133E, doi: 10.1117/12.2233418
  * Kervella et al. (2022) Kervella, P., Arenou, F., & Thévenin, F. 2022, A&A, 657, A7, doi: 10.1051/0004-6361/202142146
  * Kounkel & Covey (2019) Kounkel, M., & Covey, K. 2019, AJ, 158, 122, doi: 10.3847/1538-3881/ab339a
  * Kovács et al. (2002) Kovács, G., Zucker, S., & Mazeh, T. 2002, A&A, 391, 369, doi: 10.1051/0004-6361:20020802
  * Kreidberg (2015) Kreidberg, L. 2015, PASP, 127, 1161, doi: 10.1086/683602
  * Kunimoto et al. (2023) Kunimoto, M., Bryson, S., Daylan, T., et al. 2023, Research Notes of the American Astronomical Society, 7, 7, doi: 10.3847/2515-5172/acb149
  * Kunimoto & Matthews (2020) Kunimoto, M., & Matthews, J. M. 2020, AJ, 159, 248, doi: 10.3847/1538-3881/ab88b0
  * Kunimoto et al. (2022) Kunimoto, M., Daylan, T., Guerrero, N., et al. 2022, ApJS, 259, 33, doi: 10.3847/1538-4365/ac5688
  * Lammer et al. (2003) Lammer, H., Selsis, F., Ribas, I., et al. 2003, ApJ, 598, L121, doi: 10.1086/380815
  * Lee et al. (2014) Lee, E. J., Chiang, E., & Ormel, C. W. 2014, ApJ, 797, 95, doi: 10.1088/0004-637X/797/2/95
  * Lin & Papaloizou (1986) Lin, D. N. C., & Papaloizou, J. 1986, ApJ, 309, 846, doi: 10.1086/164653
  * Lomb (1976) Lomb, N. R. 1976, Ap&SS, 39, 447, doi: 10.1007/BF00648343
  * Lopez (2017) Lopez, E. D. 2017, MNRAS, 472, 245, doi: 10.1093/mnras/stx1558
  * Lopez & Fortney (2013) Lopez, E. D., & Fortney, J. J. 2013, ApJ, 776, 2, doi: 10.1088/0004-637X/776/1/2
  * Lopez et al. (2012) Lopez, E. D., Fortney, J. J., & Miller, N. 2012, ApJ, 761, 59, doi: 10.1088/0004-637X/761/1/59
  * Luque & Pallé (2022) Luque, R., & Pallé, E. 2022, Science, 377, 1211, doi: 10.1126/science.abl7164
  * Mandel & Agol (2002) Mandel, K., & Agol, E. 2002, ApJ, 580, L171, doi: 10.1086/345520
  * Mann et al. (2016) Mann, A. W., Gaidos, E., Mace, G. N., et al. 2016, ApJ, 818, 46, doi: 10.3847/0004-637X/818/1/46
  * Mann et al. (2018) Mann, A. W., Vanderburg, A., Rizzuto, A. C., et al. 2018, AJ, 155, 4, doi: 10.3847/1538-3881/aa9791
  * Mathur et al. (2017) Mathur, S., Huber, D., Batalha, N. M., et al. 2017, ApJS, 229, 30, doi: 10.3847/1538-4365/229/2/30
  * Mazeh et al. (2016) Mazeh, T., Holczer, T., & Faigler, S. 2016, A&A, 589, A75, doi: 10.1051/0004-6361/201528065
  * Meingast et al. (2019) Meingast, S., Alves, J., & Fürnkranz, V. 2019, A&A, 622, L13, doi: 10.1051/0004-6361/201834950
  * Messina (2021) Messina, S. 2021, A&A, 645, A144, doi: 10.1051/0004-6361/202038739
  * Moranta et al. (2022) Moranta, L., Gagné, J., Couture, D., & Faherty, J. K. 2022, ApJ, 939, 94, doi: 10.3847/1538-4357/ac8c25
  * Moraux et al. (2013) Moraux, E., Artemenko, S., Bouvier, J., et al. 2013, A&A, 560, A13, doi: 10.1051/0004-6361/201321508
  * Mordasini et al. (2009) Mordasini, C., Alibert, Y., Benz, W., & Naef, D. 2009, A&A, 501, 1161, doi: 10.1051/0004-6361/200810697
  * Nagasawa & Ida (2011) Nagasawa, M., & Ida, S. 2011, ApJ, 742, 72, doi: 10.1088/0004-637X/742/2/72
  * Nagasawa et al. (2008) Nagasawa, M., Ida, S., & Bessho, T. 2008, ApJ, 678, 498, doi: 10.1086/529369
  * Nardiello et al. (2020) Nardiello, D., Piotto, G., Deleuil, M., et al. 2020, MNRAS, 495, 4924, doi: 10.1093/mnras/staa1465
  * Newton et al. (2019) Newton, E. R., Mann, A. W., Tofflemire, B. M., et al. 2019, ApJ, 880, L17, doi: 10.3847/2041-8213/ab2988
  * Newton et al. (2021) Newton, E. R., Mann, A. W., Kraus, A. L., et al. 2021, AJ, 161, 65, doi: 10.3847/1538-3881/abccc6
  * Owen & Schlichting (2023) Owen, J. E., & Schlichting, H. E. 2023, arXiv e-prints, arXiv:2308.00020, doi: 10.48550/arXiv.2308.00020
  * Owen & Wu (2013) Owen, J. E., & Wu, Y. 2013, ApJ, 775, 105, doi: 10.1088/0004-637X/775/2/105
  * Owen & Wu (2016) —. 2016, ApJ, 817, 107, doi: 10.3847/0004-637X/817/2/107
  * Plavchan et al. (2020) Plavchan, P., Barclay, T., Gagné, J., et al. 2020, Nature, 582, 497, doi: 10.1038/s41586-020-2400-z
  * Rebull et al. (2018) Rebull, L. M., Stauffer, J. R., Cody, A. M., et al. 2018, AJ, 155, 196, doi: 10.3847/1538-3881/aab605
  * Rebull et al. (2022) Rebull, L. M., Stauffer, J. R., Hillenbrand, L. A., et al. 2022, AJ, 164, 80, doi: 10.3847/1538-3881/ac75f1
  * Rebull et al. (2016) Rebull, L. M., Stauffer, J. R., Bouvier, J., et al. 2016, AJ, 152, 113, doi: 10.3847/0004-6256/152/5/113
  * Ricker et al. (2016) Ricker, G. R., Vanderspek, R., Winn, J., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9904, Space Telescopes and Instrumentation 2016: Optical, Infrared, and Millimeter Wave, ed. H. A. MacEwen, G. G. Fazio, M. Lystrup, N. Batalha, N. Siegler, & E. C. Tong, 99042B, doi: 10.1117/12.2232071
  * Rizzuto et al. (2017) Rizzuto, A. C., Mann, A. W., Vanderburg, A., Kraus, A. L., & Covey, K. R. 2017, AJ, 154, 224, doi: 10.3847/1538-3881/aa9070
  * Rizzuto et al. (2018) Rizzuto, A. C., Vanderburg, A., Mann, A. W., et al. 2018, AJ, 156, 195, doi: 10.3847/1538-3881/aadf37
  * Rizzuto et al. (2020) Rizzuto, A. C., Newton, E. R., Mann, A. W., et al. 2020, AJ, 160, 33, doi: 10.3847/1538-3881/ab94b7
  * Rogers & Owen (2021) Rogers, J. G., & Owen, J. E. 2021, MNRAS, 503, 1526, doi: 10.1093/mnras/stab529
  * Rogers et al. (2023) Rogers, J. G., Schlichting, H. E., & Owen, J. E. 2023, ApJ, 947, L19, doi: 10.3847/2041-8213/acc86f
  * Rogers & Seager (2010) Rogers, L. A., & Seager, S. 2010, ApJ, 712, 974, doi: 10.1088/0004-637X/712/2/974
  * Scargle (1982) Scargle, J. D. 1982, ApJ, 263, 835, doi: 10.1086/160554
  * Schlichting et al. (2015) Schlichting, H. E., Sari, R., & Yalinewich, A. 2015, Icarus, 247, 81, doi: 10.1016/j.icarus.2014.09.053
  * Stassun et al. (2019) Stassun, K. G., Oelkers, R. J., Paegert, M., et al. 2019, AJ, 158, 138, doi: 10.3847/1538-3881/ab3467
  * Swift et al. (2013) Swift, J. J., Johnson, J. A., Morton, T. D., et al. 2013, ApJ, 764, 105, doi: 10.1088/0004-637X/764/1/105
  * Thompson et al. (2018) Thompson, S. E., Coughlin, J. L., Hoffman, K., et al. 2018, ApJS, 235, 38, doi: 10.3847/1538-4365/aab4f9
  * Thorngren et al. (2023) Thorngren, D. P., Lee, E. J., & Lopez, E. D. 2023, ApJ, 945, L36, doi: 10.3847/2041-8213/acbd35
  * Ujjwal et al. (2020) Ujjwal, K., Kartha, S. S., Mathew, B., Manoj, P., & Narang, M. 2020, AJ, 159, 166, doi: 10.3847/1538-3881/ab76d6
  * van der Walt et al. (2011) van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Computing in Science and Engineering, 13, 22, doi: 10.1109/MCSE.2011.37
  * Vanderburg et al. (2018) Vanderburg, A., Mann, A. W., Rizzuto, A., et al. 2018, AJ, 156, 46, doi: 10.3847/1538-3881/aac894
  * Vanderburg et al. (2019) Vanderburg, A., Huang, C. X., Rodriguez, J. E., et al. 2019, ApJ, 881, L19, doi: 10.3847/2041-8213/ab322d
  * Virtanen et al. (2020) Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2
  * Wood et al. (2021) Wood, M. L., Mann, A. W., & Kraus, A. L. 2021, AJ, 162, 128, doi: 10.3847/1538-3881/ac0ae9
  * Wood et al. (2023) Wood, M. L., Mann, A. W., Barber, M. G., et al. 2023, AJ, 165, 85, doi: 10.3847/1538-3881/aca8fc
  * Zakhozhay et al. (2022) Zakhozhay, O. V., Launhardt, R., Trifonov, T., et al. 2022, A&A, 667, L14, doi: 10.1051/0004-6361/202244747
  * Zeng et al. (2019) Zeng, L., Jacobsen, S. B., Sasselov, D. D., et al. 2019, Proceedings of the National Academy of Science, 116, 9723, doi: 10.1073/pnas.1812905116
  * Zhou et al. (2021) Zhou, G., Quinn, S. N., Irwin, J., et al. 2021, AJ, 161, 2, doi: 10.3847/1538-3881/abba22
  * Zhou et al. (2022) Zhou, G., Wirth, C. P., Huang, C. X., et al. 2022, AJ, 163, 289, doi: 10.3847/1538-3881/ac69e3