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arxiv: 2604.18579 · v1 · submitted 2026-04-20 · 🌌 astro-ph.EP

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The T16 Planet Hunt: 10,000 New Planet Candidates from TESS Cycle 1 and the Confirmation of a Hot Jupiter Around TIC 183374187

Joshua T. Roth , Joel D. Hartman , G\'asp\'ar \'A. Bakos , Samuel W. Yee , Luke G. Bouma , Jhon Yana Galarza , Johanna K. Teske , R.P. Butler
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Jeffrey D. Crane Steve Shectman David Osip Shreyas Vissapragada Yuri Beletsky Shubham Kanodia Yadira Gaibor
Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:33 UTC · model grok-4.3

classification 🌌 astro-ph.EP
keywords TESSexoplanet candidatestransiting planetsfull-frame imageshot Jupiterradial velocity confirmationplanet detection pipeline
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The pith

A semi-automated search of TESS Cycle 1 full-frame images yields 10,091 new planet candidates and confirms one as a hot Jupiter.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that uniformly processed light curves for 83 million TESS stars down to 16th magnitude can be searched at scale for transits. The search returns 11,554 candidates with periods between 0.5 and 27 days, of which 10,091 had not been reported before. A single radial-velocity confirmation of a hot Jupiter around TIC 183374187 is presented as evidence that the pipeline recovers real planets. A sympathetic reader would care because this approach accesses the much larger population of faint host stars where most planets are expected to reside.

Core claim

Processing the T16 set of 83,717,159 detrended TESS Cycle 1 full-frame image light curves with a semi-automated transit search produces 11,554 planet candidates. Of these, 10,091 are new, 411 are single-transit events, and 1,052 match previously known TESS candidates. Radial-velocity measurements with Magellan/PFS confirm that the candidate around the metal-poor star TIC 183374187 is a genuine hot Jupiter, demonstrating the pipeline's ability to identify previously undiscovered transiting planets around faint stars.

What carries the argument

The T16 collection of uniformly detrended and systematics-corrected light curves for all TESS Cycle 1 targets to T=16 mag, searched by a semi-automated pipeline that flags transit-like signals.

If this is right

  • The known TESS exoplanet candidate count more than doubles.
  • A large pool of new targets around faint stars becomes available for validation and characterization.
  • Single-transit events are catalogued but lack full orbital solutions.
  • Machine learning-assisted searches on full-frame images can be scaled to later TESS cycles.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Repeating the same processing on TESS Cycles 2 and 3 would likely add thousands more candidates around faint stars.
  • The expanded sample could allow statistical tests of whether planet occurrence rates differ between bright and faint hosts.
  • If most candidates survive follow-up, the method could be adapted to other wide-field surveys such as PLATO.

Load-bearing premise

The pipeline returns mostly genuine transiting planets rather than false positives, with one radial-velocity confirmation taken as proof of overall reliability.

What would settle it

Detailed follow-up on a random sample of the new candidates that finds a false-positive rate above 50 percent, for example through radial-velocity measurements showing no planetary mass or through high-resolution imaging revealing blended eclipsing binaries.

Figures

Figures reproduced from arXiv: 2604.18579 by David Osip, G\'asp\'ar \'A. Bakos, Jeffrey D. Crane, Jhon Yana Galarza, Joel D. Hartman, Johanna K. Teske, Joshua T. Roth, Luke G. Bouma, R.P. Butler, Samuel W. Yee, Shreyas Vissapragada, Shubham Kanodia, Steve Shectman, Yadira Gaibor, Yuri Beletsky.

Figure 1
Figure 1. Figure 1: Comparison between the raw (top) and SEPD plus TFA detrended (bottom) light curves for TIC 273568903. The raw light curve exhibits strong sinusoidal variation as well as an overall rising trend. These signals are effectively removed by the detrending process. Instrumental artifacts like the one highlighted by the orange dashed vertical line can persist after detrending and pose potential challenges in the … view at source ↗
Figure 2
Figure 2. Figure 2: Confusion Matrices for the Base classifier (all stars, top) and the Faint star classifier (T ≥ 14.5, bottom). Both classifiers are highly effective at identifying transit-like signals from noise, but there are erroneous classifications, many of which are addressed in the subsequent vetting pro￾cedure. 3.2. Random Forest Classifier Training Machine learning performance is fundamentally lim￾ited by the quali… view at source ↗
Figure 3
Figure 3. Figure 3: Left: Example of automated off center source de￾tection. This example shows an off center residual indicating a blend as the source of the measured variability. The check￾ered pattern at the source position is consistent with Poisson noise or imperfect image subtraction due to the brightness of the source. Right: Localized, on center residual indicating that the variability is due to the target. examine th… view at source ↗
Figure 4
Figure 4. Figure 4: Example vetting “summary” plot for the now confirmed exoplanet TIC 183374187. Detailed description of each panel is provided in the main text. The complete figure set contains one image for each of the 11, 143 objects in [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Toomre diagram showing the Galactic space ve￾locities of TIC 183374187 relative to the local standard of rest (LSR). The star’s velocity components (ULSR,VLSR,WLSR) were computed using Gaia DR3 astrometry and radial veloc￾ity. The dashed curves indicate loci of constant total space velocity. Grey points indicate kinematics of the other tar￾gets in my sample with a Gaia DR3 radial velocity measure￾ments. Th… view at source ↗
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The phase folded T16 light curve, measured in TESS sector 2, camera 2 ccd 4 in magnitude space. The data has been binned into 200 phase bins. Overplotted in red is the best fit Mandel-Agol Model, fitted using the BATMAN package (Kreidberg 2015). 2011, 2013, 2015). Note that these models are com￾puted assuming a solar abundance pattern, however we do find evidence for α-element enhancement in the spectrum. … view at source ↗
Figure 8
Figure 8. Figure 8: Position of TIC 183374187 on the stellar den￾sity versus effective temperature plot, along with our 1σ uncertainty in violet. Plotted in various colors are stellar isochrones for the inferred metallicity of TIC 183374187, which were obtained via MIST. The ages are in Gyrs. It is apparent that TIC 183374187 is near the turnoff of the 12 and 13Gyr isochrones, hinting at an old, slightly evolved host star. ar… view at source ↗
Figure 9
Figure 9. Figure 9: Period distribution of our candidates. The promi￾nent peak around 3.5 days is expected from Hot Jupiter sur￾veys since Hot Jupiters constitute a majority of our candi￾dates, while the falloff towards longer periods is due to the decreasing transit probability and lower SNR of longer pe￾riod candidates. slight eccentricity in addition to the old age of the host star could indicate that this planet did not f… view at source ↗
Figure 10
Figure 10. Figure 10: Selected parameters distributions and correlations from our final sample. The orange magnitude histogram shows the TESS magnitude distribution of our candidates, as opposed to the blue histogram, which shows the magnitude distribution of all Cycle 1 samples analyzed. The black curve shows the fraction of all stars in a magnitude bin that we recovered as planet candidates. Black contours on the scatter plo… view at source ↗
Figure 11
Figure 11. Figure 11: Distribution of Galactic coordinates for our en￾tire candidate set (top panel, orange data points), our recov￾ered TOIs/CTOIs (top panel, black data points), and our T < 13.5 mag candidates (lower panel, blue data points). ultra-short period (USP) candidates with orbital periods less than a day. We do not attempt to constrain the orbital periods of the mono-transit candidates in our sample in any way, and… view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of our candidates on an HR di￾agram. The red cross marks the Sun’s position, the back￾ground colors provide approximate stellar types via the clas￾sifications in Pecaut & Mamajek (2013). Stellar parame￾ters are drawn from the TESS Input Catalog (Stassun et al. 2018), supplemented by Gaia DR3 where necessary (Gaia Collaboration et al. 2023) . reduces the recovery rate of known TOIs, it is esse… view at source ↗
read the original abstract

The T16 project has produced a uniformly detrended and systematics-corrected set of 83,717,159 TESS Cycle 1 full-frame image light curves for stars observed by TESS in its primary mission down to T=16 mag, enabling sensitive transit searches beyond the official TESS pipelines. While most existing TESS planet searches focus on relatively bright targets, planet occurrence rates suggest that a substantial number of planets should exist around fainter stars. We therefore use the T16 light curves to conduct a semi-automated search for transiting exoplanets across the full Cycle 1 FFI sample, resulting in 11,554 planet candidates orbiting stars down to 16th magnitude in the TESS band with orbital periods between 0.5 and 27 days. Of these, 10,091 are new planet candidates, and 411 are single-transit events, for which we do not attempt to determine orbital parameters. The remaining 1,052 candidates are previously known TESS candidates. We validate our pipeline through Magellan/PFS radial-velocity follow-up measurements on one of our candidate hosts, TIC 183374187, a metal poor thick-disk star, confirming the signal as newly identified hot Jupiter. This detection demonstrates our pipeline's ability to identify real, previously undiscovered, transiting planets. Overall, this work shows that large-scale, machine learning-assisted transit searches of TESS full-frame images can significantly expand the census of transiting planet candidates, particularly around faint stars, providing a rich target set for future validation and follow-up efforts. Our findings more than double the number of known TESS exoplanet candidates.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reports the T16 project's processing of 83,717,159 TESS Cycle 1 full-frame image light curves down to T=16 mag using a semi-automated, machine learning-assisted transit search pipeline. This yields 11,554 planet candidates (10,091 new, including 411 single-transit events) with periods 0.5-27 days. Pipeline validity is demonstrated via one Magellan/PFS radial-velocity confirmation of a hot Jupiter around TIC 183374187, and the work claims to more than double the known TESS exoplanet candidate sample, especially around faint stars.

Significance. The computational scale of uniformly detrending and searching 83 million light curves is a notable technical achievement that could, if the candidate purity is high, provide a substantial expansion of the TESS transit candidate catalog for faint hosts. This would be valuable for future statistical studies of planet occurrence and for prioritizing follow-up resources. However, the current validation does not yet support the headline doubling claim at the level required for a high-impact result.

major comments (2)
  1. [Abstract] Abstract and validation paragraph: The central claim that the search 'more than double[s] the number of known TESS exoplanet candidates' and 'significantly expand[s] the census' rests on the assumption that the semi-automated pipeline maintains high purity across 10,091 new candidates, especially at faint magnitudes where systematics dominate. The manuscript supports this solely with a single successful RV confirmation of TIC 183374187; no injection-recovery completeness estimates, false-positive probability calculations, multi-epoch vetting metrics, or additional follow-up results are reported to quantify the real-versus-false-positive fraction for the full sample.
  2. [Validation section] The absence of quantitative reliability metrics (e.g., recovery rates from injected transits or false-alarm probabilities) makes it impossible to assess whether the reported 11,554 candidates are dominated by genuine planets or by aliases, eclipsing binaries, and noise, particularly for the 411 single-transit events and the faint-star subset.
minor comments (2)
  1. [Methods] The orbital period bounds (0.5-27 days) and transit detection threshold are listed as free parameters but their specific values and sensitivity to the final candidate count are not tabulated or discussed in detail.
  2. [Figures/Tables] Figure captions and table headers should explicitly state the magnitude range and vetting criteria applied to the 10,091 new candidates to allow readers to evaluate selection biases.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive and detailed review. The comments highlight important considerations regarding the strength of our validation and the interpretation of our candidate catalog. We address each major comment below and indicate the revisions we will make to improve clarity and balance.

read point-by-point responses

  1. Referee: [Abstract] Abstract and validation paragraph: The central claim that the search 'more than double[s] the number of known TESS exoplanet candidates' and 'significantly expand[s] the census' rests on the assumption that the semi-automated pipeline maintains high purity across 10,091 new candidates, especially at faint magnitudes where systematics dominate. The manuscript supports this solely with a single successful RV confirmation of TIC 183374187; no injection-recovery completeness estimates, false-positive probability calculations, multi-epoch vetting metrics, or additional follow-up results are reported to quantify the real-versus-false-positive fraction for the full sample.

    Authors: We agree that the validation presented is limited and that the headline claim of more than doubling the known TESS candidate sample requires careful qualification. The manuscript's primary contribution is the uniform detrending and search of 83.7 million light curves, which yields a large set of transit-like signals as candidates. The single RV confirmation of TIC 183374187 serves only to show that the pipeline recovers at least some genuine planets rather than to statistically validate the full sample. We will revise the abstract and introduction to explicitly state that the reported objects are planet candidates, to remove or soften the doubling claim, and to note that purity has not been quantified across the catalog. We will also add a dedicated limitations paragraph in the validation section. revision: partial

  2. Referee: [Validation section] The absence of quantitative reliability metrics (e.g., recovery rates from injected transits or false-alarm probabilities) makes it impossible to assess whether the reported 11,554 candidates are dominated by genuine planets or by aliases, eclipsing binaries, and noise, particularly for the 411 single-transit events and the faint-star subset.

    Authors: We acknowledge that the lack of injection-recovery tests and false-alarm probability estimates limits the ability to assess overall reliability, especially for single-transit events and faint hosts. Performing a full injection campaign on 83.7 million light curves was beyond the scope and computational resources of the current project. We will expand the validation section to discuss expected false-positive rates drawn from the literature for similar TESS searches, to clarify that the 411 single-transit events are presented without periods, and to emphasize that the catalog is intended as a starting point for follow-up rather than a statistically validated planet sample. We cannot add new quantitative recovery metrics in this revision. revision: partial

standing simulated objections not resolved
  • Comprehensive injection-recovery tests and false-positive probability calculations for the full sample of 11,554 candidates

Circularity Check

0 steps flagged

No circularity in observational data-processing pipeline

full rationale

The paper's central results consist of direct processing of 83 million TESS Cycle 1 FFI light curves via a semi-automated ML-assisted transit search, producing a catalog of 11,554 candidates of which 10,091 are reported as new. Validation rests on one external Magellan/PFS radial-velocity measurement for a single target (TIC 183374187). No equations, parameter fits, self-citations, or ansatzes are invoked that reduce any claimed prediction or uniqueness result to the inputs by construction. The derivation chain is therefore self-contained as empirical catalog generation with independent observational grounding.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that the T16 detrending and transit-search pipeline preserves real signals while removing systematics, plus standard photometric assumptions about transit shapes and noise properties.

free parameters (2)
  • transit detection threshold
    Semi-automated search requires a signal-to-noise or similar cutoff whose exact value is not stated in the abstract.
  • orbital period bounds
    Search limited to 0.5–27 days; this range choice affects which signals are recovered.
axioms (2)
  • domain assumption Periodic dips in stellar brightness are produced by transiting planets
    Standard assumption invoked throughout the transit search description.
  • domain assumption The T16 systematics correction does not remove genuine transit signals
    Central to claiming that the 10,091 new candidates are real.

pith-pipeline@v0.9.0 · 5698 in / 1360 out tokens · 32321 ms · 2026-05-10T03:33:15.624962+00:00 · methodology

discussion (0)

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Works this paper leans on

107 extracted references · 95 canonical work pages · 4 internal anchors

  1. [1]

    Alard, C., & Lupton, R. H. 1998, ApJ, 503, 325, doi: 10.1086/305984 Ardeshir Petigura, E. 2015, arXiv e-prints, arXiv:1510.03902, doi: 10.48550/arXiv.1510.03902 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068

    work page doi:10.1086/305984 1998

  2. [2]

    2019, AJ, 157, 113, doi: 10.3847/1538-3881/aaf477

    Ballard, S. 2019, AJ, 157, 113, doi: 10.3847/1538-3881/aaf477

    work page doi:10.3847/1538-3881/aaf477 2019

  3. [3]

    Barclay, T., Pepper, J., & Quintana, E. V. 2018, ApJS, 239, 2, doi: 10.3847/1538-4365/aae3e9

    work page doi:10.3847/1538-4365/aae3e9 2018

  4. [4]

    , keywords =

    Batygin, K., Bodenheimer, P. H., & Laughlin, G. P. 2016, ApJ, 829, 114, doi: 10.3847/0004-637X/829/2/114

    work page doi:10.3847/0004-637x/829/2/114 2016

  5. [5]

    2010, arXiv e-prints, arXiv:1005.0208, doi: 10.48550/arXiv.1005.0208

    Biau, G. 2010, arXiv e-prints, arXiv:1005.0208, doi: 10.48550/arXiv.1005.0208

    work page doi:10.48550/arxiv.1005.0208 2010

  6. [6]

    Bakos, G. ´A. 2019, ApJS, 245, 13, doi: 10.3847/1538-4365/ab4a7e

    work page doi:10.3847/1538-4365/ab4a7e 2019

  7. [7]

    G., Winn, J

    Bouma, L. G., Winn, J. N., Kosiarek, J., & McCullough, P. R. 2017, arXiv e-prints, arXiv:1705.08891, doi: 10.48550/arXiv.1705.08891

    work page doi:10.48550/arxiv.1705.08891 2017

  8. [8]

    Finkbeiner, D. P. 2016, ApJ, 818, 130, doi: 10.3847/0004-637X/818/2/130

    work page doi:10.3847/0004-637x/818/2/130 2016

  9. [9]

    2024, astropy/photutils: 2.0.2, 2.0.2, Zenodo, doi: 10.5281/zenodo.13989456

    Bradley, L., Sip˝ ocz, B., Robitaille, T., et al. 2024, astropy/photutils: 2.0.2, 2.0.2, Zenodo, doi: 10.5281/zenodo.13989456

    work page doi:10.5281/zenodo.13989456 2024

  10. [10]

    Machine Learning , author =

    Breiman, L. 2001, Machine Learning, 45, 5, doi: 10.1023/A:1010933404324

    work page doi:10.1023/a:1010933404324 2001

  11. [11]

    P., Marcy, G

    Butler, R. P., Marcy, G. W., Williams, E., et al. 1996, Publications of the Astronomical Society of the Pacific, 108, 500, doi: 10.1086/133755

    work page doi:10.1086/133755 1996

  12. [12]

    Research Notes of the American Astronomical Society , keywords =

    Caldwell, D. A., Tenenbaum, P., Twicken, J. D., et al. 2020, Research Notes of the American Astronomical Society, 4, 201, doi: 10.3847/2515-5172/abc9b3

    work page doi:10.3847/2515-5172/abc9b3 2020

  13. [13]

    , keywords =

    Cassan, A., Kubas, D., Beaulieu, J. P., et al. 2012, Nature, 481, 167, doi: 10.1038/nature10684

    work page doi:10.1038/nature10684 2012

  14. [14]

    Castelli, F., & Kurucz, R. L. 2003, in IAU Symposium, Vol. 210, Modelling of Stellar Atmospheres, ed. N. Piskunov, W. W. Weiss, & D. F. Gray, A20 Castro-Gonz´ alez, A., Bourrier, V., Lillo-Box, J., et al. 2024, A&A, 689, A250, doi: 10.1051/0004-6361/202450957

    work page doi:10.1051/0004-6361/202450957 2003

  15. [15]

    , keywords =

    Charbonneau, D., Brown, T. M., Latham, D. W., & Mayor, M. 2000, ApJL, 529, L45, doi: 10.1086/312457

    work page doi:10.1086/312457 2000

  16. [16]

    , keywords =

    Chatterjee, S., Ford, E. B., Matsumura, S., & Rasio, F. A. 2008, ApJ, 686, 580, doi: 10.1086/590227

    work page doi:10.1086/590227 2008

  17. [17]

    2025, arXiv e-prints, arXiv:2510.26084, doi: 10.48550/arXiv.2510.26084

    Chen, D.-C., Xie, J.-W., Zhou, J.-L., et al. 2025, arXiv e-prints, arXiv:2510.26084, doi: 10.48550/arXiv.2510.26084

    work page doi:10.48550/arxiv.2510.26084 2025

  18. [18]

    , keywords =

    Chen, J., & Kipping, D. 2017, ApJ, 834, 17, doi: 10.3847/1538-4357/834/1/17

    work page doi:10.3847/1538-4357/834/1/17 2017

  19. [19]

    2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102

    Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102, doi: 10.3847/0004-637X/823/2/102

    work page internal anchor Pith review doi:10.3847/0004-637x/823/2/102 2016

  20. [20]

    S., & Swastik, C

    Choudhary, A., Bandari, S., Kushvah, B. S., & Swastik, C. 2025, AJ, 170, 120, doi: 10.3847/1538-3881/ade99c

    work page doi:10.3847/1538-3881/ade99c 2025

  21. [21]

    , keywords =

    Christiansen, J. L., McElroy, D. L., Harbut, M., et al. 2025, PSJ, 6, 186, doi: 10.3847/PSJ/ade3c2

    work page doi:10.3847/psj/ade3c2 2025

  22. [22]

    H., & Witte, S

    Claret, A., Hauschildt, P. H., & Witte, S. 2012, A&A, 546, A14, doi: 10.1051/0004-6361/201219849 —. 2013, A&A, 552, A16, doi: 10.1051/0004-6361/201220942

    work page doi:10.1051/0004-6361/201219849 2012

  23. [23]

    2018, A&A, 618, A20, doi: 10.1051/0004-6361/201833060

    Claret, Antonio. 2018, A&A, 618, A20, doi: 10.1051/0004-6361/201833060

    work page doi:10.1051/0004-6361/201833060 2018

  24. [24]

    Castilho, B. V. 2005, A&A, 443, 735, doi: 10.1051/0004-6361:20053511

    work page doi:10.1051/0004-6361:20053511 2005

  25. [25]

    D., Shectman, S

    Crane, J. D., Shectman, S. A., & Butler, R. P. 2006, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 6269, Ground-based and Airborne Instrumentation for Astronomy, ed. I. S. McLean & M. Iye, 626931, doi: 10.1117/12.672339

    work page doi:10.1117/12.672339 2006

  26. [26]

    D., Shectman, S

    Crane, J. D., Shectman, S. A., Butler, R. P., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S

    2010

  27. [27]

    McLean, S. K. Ramsay, & H. Takami, 773553, doi: 10.1117/12.857792

    work page doi:10.1117/12.857792

  28. [28]

    D., Shectman, S

    Crane, J. D., Shectman, S. A., Butler, R. P., Thompson, I. B., & Burley, G. S. 2008, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7014, Ground-based and Airborne Instrumentation for Astronomy II, ed. I. S. McLean & M. M. Casali, 701479, doi: 10.1117/12.789637

    work page doi:10.1117/12.789637 2008

  29. [29]

    P., Marcy, G

    Cumming, A., Butler, R. P., Marcy, G. W., et al. 2008, PASP, 120, 531, doi: 10.1086/588487

    work page doi:10.1086/588487 2008

  30. [30]

    P., et al

    Czesla, S., Schr¨ oter, S., Schneider, C. P., et al. 2019, PyA: Python astronomy-related packages. http://ascl.net/1906.010

    2019

  31. [31]

    2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8 Ekstr¨ om, S., Georgy, C., Eggenberger, P., et al

    Dotter, A. 2016, ApJS, 222, 8, doi: 10.3847/0067-0049/222/1/8

    work page doi:10.3847/0067-0049/222/1/8 2016

  32. [32]

    M., Vallely, P

    Fausnaugh, M. M., Vallely, P. J., Kochanek, C. S., et al. 2021, ApJ, 908, 51, doi: 10.3847/1538-4357/abcd42

    work page doi:10.3847/1538-4357/abcd42 2021

  33. [33]

    New alpha-Enhanced Exoplanet Hosts

    Ferreira, T., Yana Galarza, J., Reggiani, H., et al. 2025, arXiv e-prints, arXiv:2512.17072, doi: 10.48550/arXiv.2512.17072

    work page doi:10.48550/arxiv.2512.17072 2025

  34. [34]

    and Lang, Dustin and Goodman, Jonathan , title =

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067 27

    work page doi:10.1086/670067 2013

  35. [35]

    J., Petigura, E

    Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109, doi: 10.3847/1538-3881/aa80eb Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1, doi: 10.1051/0004-6361/201629272 Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1, doi: 10.1051/0004-6361/201833051 Gaia Collaboration, Vallena...

    work page doi:10.3847/1538-3881/aa80eb 2017

  36. [36]

    D., Jensen, E

    Giacalone, S., Dressing, C. D., Jensen, E. L. N., et al. 2021, AJ, 161, 24, doi: 10.3847/1538-3881/abc6af

    work page doi:10.3847/1538-3881/abc6af 2021

  37. [37]

    M., Brasseur, C

    Ginsburg, A., Sip˝ ocz, B. M., Brasseur, C. E., et al. 2019, AJ, 157, 98, doi: 10.3847/1538-3881/aafc33

    work page doi:10.3847/1538-3881/aafc33 2019

  38. [38]

    2010, CAMCS, 5, 65, doi: 10.2140/camcos.2010.5.65

    Goodman, J., & Weare, J. 2010, Communications in Applied Mathematics and Computational Science, 5, 65, doi: 10.2140/camcos.2010.5.65

    work page doi:10.2140/camcos.2010.5.65 2010

  39. [39]

    , keywords =

    Guerrero, N. M., Seager, S., Huang, C. X., et al. 2021, ApJS, 254, 39, doi: 10.3847/1538-4365/abefe1

    work page doi:10.3847/1538-4365/abefe1 2021

  40. [40]

    A., Schult, D

    Hagberg, A. A., Schult, D. A., & Swart, P. J. 2008, in Proceedings of the 7th Python in Science Conference, ed. G. Varoquaux, T. Vaught, & J. Millman, Pasadena, CA USA, 11 – 15

    2008

  41. [41]

    Han, T., & Brandt, T. D. 2023, AJ, 165, 71, doi: 10.3847/1538-3881/acaaa7

    work page doi:10.3847/1538-3881/acaaa7 2023

  42. [42]

    C., Bou´ e, G., Laskar, J., Delisle, J.-B., & Unger, N

    Hara, N. C., Bou´ e, G., Laskar, J., Delisle, J.-B., & Unger, N. 2019, MNRAS, 489, 738, doi: 10.1093/mnras/stz1849

    work page doi:10.1093/mnras/stz1849 2019

  43. [43]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    work page doi:10.1038/s41586-020-2649-2 2020

  44. [44]

    D., & Bakos, G

    Hartman, J. D., & Bakos, G. ´A. 2016, Astronomy and Computing, 17, 1, doi: 10.1016/j.ascom.2016.05.006

    work page doi:10.1016/j.ascom.2016.05.006 2016

  45. [45]

    D., Bakos, G

    Hartman, J. D., Bakos, G. ´A., Bouma, L. G., & Csubry, Z. 2025, PASP, 137, 024501, doi: 10.1088/1538-3873/adad42

    work page doi:10.1088/1538-3873/adad42 2025

  46. [46]

    D., Bakos, G

    Hartman, J. D., Bakos, G. ´A., Bayliss, D., et al. 2019, AJ, 157, 55, doi: 10.3847/1538-3881/aaf8b6

    work page doi:10.3847/1538-3881/aaf8b6 2019

  47. [47]

    2019, Astronomy & Astrophysics, 623, A39, doi: 10.1051/0004-6361/201834672

    Hippke, M., & Heller, R. 2019, A&A, 623, A39, doi: 10.1051/0004-6361/201834672

    work page doi:10.1051/0004-6361/201834672 2019

  48. [48]

    Science , keywords =

    Howard, A. W., Marcy, G. W., Johnson, J. A., et al. 2010, Science, 330, 653, doi: 10.1126/science.1194854

    work page doi:10.1126/science.1194854 2010

  49. [49]

    X., Shporer, A., Dragomir, D., et al

    Huang, C. X., Shporer, A., Dragomir, D., et al. 2018, arXiv e-prints, arXiv:1807.11129, doi: 10.48550/arXiv.1807.11129

    work page doi:10.48550/arxiv.1807.11129 2018

  50. [50]

    X., Vanderburg, A., P´ al, A., et al

    Huang, C. X., Vanderburg, A., P´ al, A., et al. 2020a, Research Notes of the American Astronomical Society, 4, 204, doi: 10.3847/2515-5172/abca2e —. 2020b, Research Notes of the American Astronomical Society, 4, 206, doi: 10.3847/2515-5172/abca2d

    work page doi:10.3847/2515-5172/abca2e

  51. [51]

    Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

    work page doi:10.1109/mcse.2007.55 2007

  52. [52]

    2008, ApJ, 678, 1396, doi: 10.1086/529187

    Jackson, B., Greenberg, R., & Barnes, R. 2008, ApJ, 678, 1396, doi: 10.1086/529187

    work page doi:10.1086/529187 2008

  53. [53]

    Kipping, D. M. 2013, Monthly Notices of the Royal Astronomical Society, 435, 2152, doi: 10.1093/mnras/stt1435

    work page doi:10.1093/mnras/stt1435 2013

  54. [54]

    1950, Harvard College Observatory Circular, 454, 1

    Kopal, Z. 1950, Harvard College Observatory Circular, 454, 1

    1950

  55. [55]

    , keywords =

    Kostov, V. B., Powell, B. P., Fornear, A. U., et al. 2025, ApJS, 279, 50, doi: 10.3847/1538-4365/ade2d8 Kov´ acs, G., Bakos, G., & Noyes, R. W. 2005, MNRAS, 356, 557, doi: 10.1111/j.1365-2966.2004.08479.x Kov´ acs, G., Zucker, S., & Mazeh, T. 2002, A&A, 391, 369, doi: 10.1051/0004-6361:20020802

    work page doi:10.3847/1538-4365/ade2d8 2025

  56. [56]

    2015, Publications of the Astronomical Society of the Pacific, 127, 1161, doi: 10.1086/683602

    Kreidberg, L. 2015, PASP, 127, 1161, doi: 10.1086/683602

    work page doi:10.1086/683602 2015

  57. [57]

    2022, ApJS, 259, 33, doi: 10.3847/1538-4365/ac5688

    Kunimoto, M., Daylan, T., Guerrero, N., et al. 2022, ApJS, 259, 33, doi: 10.3847/1538-4365/ac5688

    work page doi:10.3847/1538-4365/ac5688 2022

  58. [58]

    Lin, D. N. C., Bodenheimer, P., & Richardson, D. C. 1996, Nature, 380, 606, doi: 10.1038/380606a0

    work page doi:10.1038/380606a0 1996

  59. [59]

    1971AJ.....76..544L Mann, A.W., Feiden, G.A., Gaidos, E., Boyajian, T., and von Braun, K.: 2015,The Astrophysical Journal804,

    Lucy, L. B., & Sweeney, M. A. 1971, AJ, 76, 544, doi: 10.1086/111159

    work page doi:10.1086/111159 1971

  60. [60]

    2002, ApJL, 580, L171, doi: 10.1086/345520

    Mandel, K., & Agol, E. 2002, ApJL, 580, L171, doi: 10.1086/345520

    work page doi:10.1086/345520 2002

  61. [61]

    W., & Butler, R

    Marcy, G. W., & Butler, R. P. 1992, PASP, 104, 270, doi: 10.1086/132989

    work page doi:10.1086/132989 1992

  62. [62]

    2016, A&A, 589, A75, doi: 10.1051/0004-6361/201528065

    Mazeh, T., Holczer, T., & Faigler, S. 2016, A&A, 589, A75, doi: 10.1051/0004-6361/201528065

    work page doi:10.1051/0004-6361/201528065 2016

  63. [63]

    D., Jenkins, J

    McCauliff, S. D., Jenkins, J. M., Catanzarite, J., et al. 2015, ApJ, 806, 6, doi: 10.1088/0004-637X/806/1/6

    work page doi:10.1088/0004-637x/806/1/6 2015

  64. [64]

    2010 , keywords =

    McKinney, W. 2010, SciPy 2010, doi: 10.25080/Majora-92bf1922-00a

    work page doi:10.25080/majora-92bf1922-00a 2010

  65. [65]

    , keywords =

    Montalto, M. 2023, MNRAS, 518, L31, doi: 10.1093/mnrasl/slac131

    work page doi:10.1093/mnrasl/slac131 2023

  66. [66]

    , keywords =

    Montalto, M., Borsato, L., Granata, V., et al. 2020, MNRAS, 498, 1726, doi: 10.1093/mnras/staa2438

    work page doi:10.1093/mnras/staa2438 2020

  67. [67]

    S., Dressing, C

    Muirhead, P. S., Dressing, C. D., Mann, A. W., et al. 2018, AJ, 155, 180, doi: 10.3847/1538-3881/aab710

    work page doi:10.3847/1538-3881/aab710 2018

  68. [68]

    D., Pascucci, I., Apai, D., & Ciesla, F

    Mulders, G. D., Pascucci, I., Apai, D., & Ciesla, F. J. 2018, AJ, 156, 24, doi: 10.3847/1538-3881/aac5ea

    work page doi:10.3847/1538-3881/aac5ea 2018

  69. [69]

    2020, MNRAS, 498, 5972, doi: 10.1093/mnras/staa2745

    Nardiello, D. 2020, MNRAS, 498, 5972, doi: 10.1093/mnras/staa2745

    work page doi:10.1093/mnras/staa2745 2020

  70. [70]

    2019, MNRAS, 490, 3806, doi: 10.1093/mnras/stz2878

    Nardiello, D., Borsato, L., Piotto, G., et al. 2019, MNRAS, 490, 3806, doi: 10.1093/mnras/stz2878

    work page doi:10.1093/mnras/stz2878 2019

  71. [71]

    2020, MNRAS, 495, 4924, doi: 10.1093/mnras/staa1465

    Nardiello, D., Piotto, G., Deleuil, M., et al. 2020, MNRAS, 495, 4924, doi: 10.1093/mnras/staa1465

    work page doi:10.1093/mnras/staa1465 2020

  72. [72]

    2021, MNRAS, 505, 3767, doi: 10.1093/mnras/stab1497

    Nardiello, D., Deleuil, M., Mantovan, G., et al. 2021, MNRAS, 505, 3767, doi: 10.1093/mnras/stab1497

    work page doi:10.1093/mnras/stab1497 2021

  73. [73]

    A., & Mead, R

    Nelder, J. A., & Mead, R. 1965, The Computer Journal, 7, 308

    1965

  74. [74]

    The VizieR database of astronomical catalogues , publisher =

    Ochsenbein, F. 1996, The VizieR database of astronomical catalogues, CDS, Centre de Donn ˜A©es astronomiques de Strasbourg, doi: 10.26093/CDS/VIZIER 28

    work page doi:10.26093/cds/vizier 1996

  75. [75]

    Astronomy and Astrophysics Supplement Series , author =

    Ochsenbein, F., Bauer, P., & Marcout, J. 2000, A&AS, 143, 23, doi: 10.1051/aas:2000169

    work page doi:10.1051/aas:2000169 2000

  76. [76]

    J., & Stassun, K

    Oelkers, R. J., & Stassun, K. G. 2018, AJ, 156, 132, doi: 10.3847/1538-3881/aad68e

    work page doi:10.3847/1538-3881/aad68e 2018

  77. [77]

    B., Zaguri, Y., Perdelwitz, V., et al

    Ogunwale, B. B., Zaguri, Y., Perdelwitz, V., et al. 2025, arXiv e-prints, arXiv:2511.19313, doi: 10.48550/arXiv.2511.19313 P´ al, A. 2012, MNRAS, 421, 1825, doi: 10.1111/j.1365-2966.2011.19813.x

    work page doi:10.48550/arxiv.2511.19313 2025

  78. [78]

    2011, ApJS, 192, 3, doi: 10.1088/0067-0049/192/1/3

    Paxton, B., Bildsten, L., Dotter, A., et al. 2011, ApJS, 192, 3, doi: 10.1088/0067-0049/192/1/3

    work page doi:10.1088/0067-0049/192/1/3 2011

  79. [79]

    2013, ApJS, 208, 4, doi: 10.1088/0067-0049/208/1/4 10

    Paxton, B., Cantiello, M., Arras, P., et al. 2013, ApJS, 208, 4, doi: 10.1088/0067-0049/208/1/4

    work page internal anchor Pith review doi:10.1088/0067-0049/208/1/4 2013

  80. [80]

    2015, ApJS, 220, 15, doi: 10.1088/0067-0049/220/1/15

    Paxton, B., Marchant, P., Schwab, J., et al. 2015, ApJS, 220, 15, doi: 10.1088/0067-0049/220/1/15

    work page internal anchor Pith review doi:10.1088/0067-0049/220/1/15 2015

Showing first 80 references.