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LEO-Vetter: Fully Automated Flux- and Pixel-Level Vetting of TESS Planet Candidates to Support Occurrence Rates

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read LEO-Vetter claims that a fully automated, publicly available pipeline can vet TESS planet candidates with 91% completeness and 97% reliability against false alarms, reducing about 20,000 detections to 172 candidates.

desk verdict A genuinely useful, publicly shipped TESS vetter whose headline completeness/reliability numbers are in-sample fitted values; the tool deserves peer review, and the numbers need a held-out check. read the letter →

arxiv 2509.10619 v1 pith:OKS4ELHA submitted 2025-09-12 astro-ph.EP astro-ph.IM

Michelle Kunimoto , Steve Bryson , Drayson Jaffee , Jason F. Rowe , Tansu Daylan , Steven Giacalone , Jack J. Lissauer , Michael R. B. Matesic
show 2 more authors
Susan E. Mullally Yoshi Nike Emilia Eschen
This is my paper · ORCID
classification astro-ph.EPastro-ph.IM
keywords exoplanetvettingTESStransitcandidatesplanetoccurrenceratesfalsealarmseclipsingbinariesautomatedpipelinespixel-levelanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to show that the bottleneck of manually vetting TESS transit candidates can be removed entirely. It presents LEO-Vetter, a public, fully automated pipeline that takes transit-like detections as input and labels each one as a planet candidate, an astrophysical false positive, or a noise/systematic false alarm. On simulated data the pipeline claims 91% completeness and 97% reliability against false alarms, and a demonstration search of about 200,000 M dwarfs reduces roughly 20,000 detections to 172 candidates. If these numbers hold, the result matters because a uniform, reproducible vetting step is precisely what TESS demographic calculations need to convert observed candidate counts into occurrence rates.

What carries the argument

The engine is a suite of decision metrics computed for each transit-like detection and compared against pass-fail thresholds: signal-to-noise ratio, transit model fits, a sine-wave variability test, transit duration and asymmetry checks, depth mean-to-median consistency, per-transit signal-to-noise consistency, uniqueness tests on the folded light curve, individual transit vetting, and a pixel-level difference-image centroid analysis. The thresholds were set by an optimization routine that varies false-alarm-test thresholds to minimize $\sqrt{(1-C)^2 + (1-R_{\rm FA})^2}$, using injected transits to measure completeness and orbit-scrambled light curves to estimate false-alarm reliability. This metric-plus-threshold design is what lets the pipeline be fully automated and uniform.

What would settle it

Apply LEO-Vetter with the published thresholds to an independent set of TESS light curves with known confirmed planets and known false positives, for example a different sector range or the 2-minute SPOC data, and compare the automated labels against the confirmed disposition. If the recovered fraction of confirmed planets is clearly below 91% or the false-alarm contamination of the candidate list clearly exceeds 3%, the reported completeness and reliability would be overstated.

Watch

Extended reading notes

Core claim

The paper claims that LEO-Vetter, a fully automated vetting pipeline for TESS transit signals, can replace the manual inspection currently used to build planet candidate catalogs without sacrificing the uniformity needed for demographic studies. On a test set of roughly 200,000 M dwarf light curves, it classifies 91.00% of injected transiting planets as planet candidates, rejects 99.91% of scrambled-light-curve false alarms, and reduces about 20,000 observed transit-like detections to 172 uniformly vetted candidates. The pipeline combines flux-level tests of transit shape, depth consistency, uniqueness, and signal-to-noise with a pixel-level centroid-offset test that flags signals coming from nearby stars. The authors conclude that this makes statistically grounded TESS occurrence-rate calculations possible because users can characterize both vetting completeness and false-alarm reliability from simulated data.

Load-bearing premise

The performance numbers rest on the assumption that transits injected into real light curves and false alarms made by scrambling orbit and sector order faithfully represent the real planets and noise/systematic signals a user will encounter.

Editorial extensions

If this is right

  • Users can turn their own TESS transit-like detections into a vetted planet candidate catalog without manual review, using the published thresholds as a starting point.
  • The injected and scrambled data products supplied with the pipeline let users measure vetting completeness and false-alarm reliability for their own sample, which is required input for occurrence-rate calculations.
  • The same thresholds applied to a 300,000-star FGK dwarf sample give comparable completeness (90.86%) and reliability (88.19% overall, 98.64% at signal-to-noise above 12), suggesting the M-dwarf tuning transfers to other dwarf populations.
  • Known TOIs that failed flux-level vetting recovered cleanly when 2-minute SPOC light curves were substituted for the longer-cadence FFI data, indicating performance improves with shorter cadence.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the pass-fail thresholds were optimized on the very injected and scrambled datasets used to measure performance, an independent test on a separate sector set or on 2-minute cadence data would be the natural check of whether 91% and 97% generalize to new observations.
  • The same architecture could be applied to other transit surveys with different bandpasses and cadence mixes; the limb-darkening assumptions and pixel-level code currently tied to TESS would need to be generalized first.
  • If the pipeline's completeness depends strongly on cadence, users analyzing only Prime Mission 30-minute data should re-derive thresholds rather than reuse the published ones, since the paper itself reports a Prime Mission completeness of 69.88%.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents LEO-Vetter, a fully automated Robovetter-inspired vetting pipeline for TESS transit candidates. The tool implements thirteen flux-level tests against noise/systematic false alarms, four flux-level tests against astrophysical false positives, and a pixel-level centroid offset test using difference images. As a demonstration, the authors apply LEO-Vetter to roughly 200,000 M-dwarf QLP light curves, obtain 18,424 observed TCEs plus 62,410 injected and 15,655 scrambled TCEs, and use a differential evolution optimizer to set pass-fail thresholds on the false-alarm tests. They report a completeness of 91.00% and a false-alarm reliability of 96.97% on these same data, and after pixel-level vetting they produce a uniformly vetted catalog of 172 planet candidates. They also apply the M-dwarf-tuned thresholds to 300,000 FGK dwarfs and report lower overall reliability (88.19%), which they interpret as demonstrating that the thresholds are applicable to FGK dwarfs. The paper claims that the vetter's characterized completeness and reliability make it suitable for TESS occurrence-rate calculations.

Significance. If the performance claims hold, LEO-Vetter would be a valuable public resource: it is the first fully automated TESS vetter that combines flux-level tests with pixel-level difference-image analysis, and it is designed specifically for uniform catalog production in support of demographic studies. The paper's strengths include the public release of the code on GitHub and Zenodo, the use of injection and scrambling experiments following the Kepler DR25 methodology, the large-scale M-dwarf demonstration producing a 172-candidate catalog, and the per-candidate false-positive probabilities, positional probabilities, and disposition scores. The central weakness is that the headline completeness and reliability numbers are in-sample estimates: thresholds were optimized on the same injected and scrambled TCE sets used to quote performance, and the FGK re-run in §8.2 shows that reliability is sensitive to the stellar sample. This load-bearing issue must be addressed before the occurrence-rate support claim is credible.

major comments (3)
  1. [§5.6 and §6.1] The headline performance figures are in-sample fitted values, not independent predictions. In §5.6 the differential evolution optimizer minimizes sqrt((1-C)^2 + (1-R_FA)^2) over the injected and scrambled TCE sets, and §6.1 then quotes C = 91.00%, E = 99.91%, and R_FA = 96.97% computed on those same sets. The paper reports no held-out split, cross-validation, or alternative test set for the M-dwarf pipeline. Because the optimizer can exploit noise in the specific simulated TCEs, the quoted completeness and reliability may be optimistic. The FGK re-run in §8.2 provides the natural out-of-sample test: applying the same thresholds to FGK dwarfs gives C = 90.86% but R_FA = 88.19% overall. This demonstrates that the 97% reliability is not robust across stellar populations and that the claim of applicability to FGKM dwarfs is only weakly supported. An unbiased evaluation (e.g., tuning on one half of the M-dwarf data and evaluating on the other half, or presenting the FGK result as the headline transferable reliability) is required before the paper can claim that LEO-Vetter produces catalogs with characterized completeness and reliability for occurrence rates.
  2. [§6.3 and §7] The reliability of the final 172-candidate catalog is not characterized. The 96.97% false-alarm reliability in §6.1 is computed after flux-level vetting only; the pixel-level centroid offset test then removes 153 of 325 flux-level candidates, yet no completeness or reliability estimate is provided for the pixel-level stage. The centroid threshold of Δθ < 15″ appears to be hand-set based on performance on known TOIs rather than validated with injected or simulated pixel-level tests, and the paper does not quantify how many true planets might be lost at this stage. Since the stated purpose of the tool is to support occurrence-rate calculations that require end-to-end reliability of the final catalog, the absence of a validated pixel-level performance estimate is a load-bearing gap.
  3. [§5.5] The cleaning of simulated TCEs introduces manual judgment into the benchmark sets used for the headline numbers. The authors removed 389 injected and 479 scrambled TCEs from light curves containing known TOIs, eclipsing binaries, or 'signals that we determined to be previously unknown planet candidates or eclipsing binaries manually identified while testing the vetter.' This manual pruning can bias both completeness and effectiveness in unpredictable directions and weakens the claim that C and R_FA are purely simulation-based quantities. The paper should justify that the removed TCEs are not systematically different from the retained ones, or present the performance metrics with and without this cleaning step.
minor comments (6)
  1. [Abstract and §6.1] The point estimates 91.00% and 96.97% are quoted without any uncertainty. Statistical uncertainties from the finite TCE samples are small, but systematic uncertainties from simulation fidelity and threshold tuning are not; a brief statement about the dominant uncertainty would help readers.
  2. [§3.8] There is a typo: 'A value if C_i ≈ 0' should read 'A value of C_i ≈ 0'.
  3. [§5.2] The definition of P_max as 'half the length of the longest continuous stretch of consecutive sectors' is confusing; it would be clearer to say 'half the length of the longest continuous observing stretch within a sector or across consecutive sectors.'
  4. [§6.1] The sentence 'LEO-Vetter successfully classified 91.00% of injected planets as PCs' should say 'of injected TCEs recovered by the BLS search,' to match the definition of completeness in Eq. (14).
  5. [§8.2] The conclusion that 'the thresholds optimized for M dwarfs are also applicable to FGK dwarfs' is too strong given that R_FA drops from 96.97% to 88.19%; the SNR > 12 reliability of 98.64% should be reported more prominently if the authors intend this as the applicable regime.
  6. [§7.3] The disposition scores are derived from metric standard deviations measured on the same injected dataset used for threshold optimization; the text should note that these scores are therefore not an independent validation of the vetter's reliability.

Circularity Check

1 steps flagged · score 6.0 of 10

Headline completeness/reliability numbers are computed on the same simulated TCE sets used to optimize the pass-fail thresholds, so the central performance claim is an in-sample fit rather than an independent prediction.

  1. fitted input called prediction [§5.6 "Tuning Pass-Fail Thresholds" and §6.1 "Flux-Level Vetting"]
    "We ran an optimization routine by varying pass-fail thresholds and computing the resulting completeness and reliability values, with the goal of minimizing the function sqrt((1-C)^2 + (1-R_FA)^2). … Using the thresholds provided in §3 and §4, LEO-Vetter successfully classified 91.00% of injected planets as PCs, while rejecting 99.91% of simulated false alarms as FAs. … corresponding to an overall catalog reliability of 96.97% (i.e., a noise/systematic false alarm rate of 3.03%) following Eqn. 16."

    The reported C = 91.00% and R_FA = 96.97% are measured on the same injected and scrambled TCE datasets that the differential evolution optimizer used to select the pass-fail thresholds in §5.6. Those two numbers are exactly the components of the minimized objective, so the headline performance is the optimization target evaluated on its own training set, not an independent validation. The optimizer can exploit noise in these specific simulated TCEs, and the paper reports no held-out split or cross-validation. The FGK re-run in §8.2 gives R_FA = 88.19% with the same thresholds, showing dataset sensitivity.

full rationale

The core circularity is in-sample performance evaluation: §5.6 tunes thresholds by minimizing sqrt((1-C)^2 + (1-R_FA)^2) on the injected and scrambled TCE sets, and §6.1 then quotes C = 91.00% and R_FA = 96.97% from those same sets. Because C and R_FA are the two terms of the optimized objective, the headline numbers reduce by construction to fitted values on the training data. The paper reports no held-out split or cross-validation for the M-dwarf set, and the FGK test in §8.2 shows that the M-dwarf-tuned thresholds yield a lower reliability (88.19%) on a different stellar sample, confirming that the quoted 97% is not a robust out-of-sample estimate. No significant self-citation load-bearing circularity is present: the cited Robovetter/KDR25 tests, Model-Shift, and TRICERATOPS are external tools or prior independent work, and the TOI recovery check provides partial independent support for completeness. But because the central performance claim used for occurrence-rate readiness is the fitted in-sample value, the circularity score is 6 rather than lower.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on simulation-based calibration and on stated input assumptions. The threshold parameters are numerous and data-fitted; the paper discloses them, which is good, but they are not derived from first principles. No new physical entities are introduced; LEO-Vetter is software built from published models and tests.

free parameters (5)
  • Optimized false alarm pass-fail thresholds = See Table 1 and §3; e.g., SNR<6.2, CHI<7.8, Chases<0.78, MS1<0.2, MS2/3<0.8, SHP>0.6.
    These thresholds were optimized with differential evolution on injected, scrambled, and observed TCEs to minimize sqrt((1-C)^2+(1-R_FA)^2) (§5.6, Table 1).
  • Astrophysical false positive thresholds = Rp>22 Re, V<1.5, OE>3, secondary MS4>0 etc., delta_theta<15 arcsec.
    Fixed by hand based on testing on observed TCEs and manually identified eclipsing binaries (§4, §5 intro, §5.6).
  • Detrending window = 0.5 days
    Chosen in §5.1 to remove astrophysical variability longer than a few hours; affects which signals survive to vetting.
  • BLS search criteria and Pmax = SNR>9, Ntr>=3, Pmax = half longest continuous stretch or 40 days.
    Chosen in §5.2; median Pmax 13.5 days. These choices define the TCE sample and therefore the completeness and reliability results.
  • Centroid offset threshold = delta_theta < 15 arcsec
    Empirically chosen in §4.5 and §6.3 based on performance on the TOI catalog.
assumptions (6)
  • domain assumption Input light curves are already dilution-corrected, outlier-removed, and quality-flag cleaned before vetting.
    Taken as inputs in §2; if violated, transit depths and SNRs are biased and the thresholds may not apply.
  • domain assumption Transits are strictly periodic with no significant TTVs.
    Stated in §2; violates for TTV systems, and the vetter may misclassify them.
  • domain assumption Scrambled light curves reproduce realistic TESS false alarms.
    §5.4 and Figure 3 show period-histogram agreement; reliability estimate Eqn 16 depends on this.
  • domain assumption Injected quadratic limb-darkened circular-orbit transits represent the real planet population.
    §5.3; completeness is measured against these injections, so biases (e.g., eccentric orbits, limb-darkening mismatch) propagate into the 91% figure.
  • domain assumption The Pont et al. (2006) and Hartman & Bakos (2016) noise model accurately captures TESS correlated noise.
    §2.1 Eqns 2-3 define SNR via white and red noise; all tests depend on this SNR.
  • domain assumption TESS PRF difference-image fitting locates the transit source well enough for the 15 arcsec cutoff.
    §4.5; the centroid offset test relies on this to flag off-target signals, and the paper notes quality degrades at low SNR.

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Cite this review

Pith. "Pith review of LEO-Vetter: Fully Automated Flux- and Pixel-Level Vetting of TESS Planet Candidates to Support Occurrence Rates." pith.science (2026). https://pith.science/paper/OKS4ELHA

@misc{pith2026250910619,
  author       = {Pith},
  title        = {Pith review of: LEO-Vetter: Fully Automated Flux- and Pixel-Level Vetting of TESS Planet Candidates to Support Occurrence Rates},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OKS4ELHA}},
  note         = {Machine review of arXiv:2509.10619}
}
read the original abstract

The Transiting Exoplanet Survey Satellite (TESS) has identified several thousand planet candidates orbiting a wide variety of stars, and has provided an exciting opportunity for demographic studies. However, current TESS planet searches require significant manual inspection efforts to identify planets among the enormous number of detected transit-like signatures, which limits the scope of such searches. Demographic studies also require a detailed understanding of the relationship between observed and true exoplanet populations; a task for which current TESS planet catalogs are rendered unsuitable by the subjectivity of vetting by eye. We present LEO-Vetter, a publicly available and fully automated exoplanet vetting system designed after the Kepler Robovetter, which is capable of efficiently producing catalogs of promising planet candidates and making statistically robust TESS demographic studies possible. LEO-Vetter implements flux- and pixel-level tests against noise/systematic false positives and astrophysical false positives. The vetter achieves high completeness (91%) and high reliability against noise/systematic false alarms (97%) based on its performance on simulated data. We demonstrate the usefulness of the vetter by searching ~200,000 M dwarf light curves, and reducing ~20,000 transit-like detections down to 172 uniformly vetted planet candidates. LEO-Vetter facilitates analyses that would otherwise be impractical to perform on all possible signals due to time constraints or computational limitations. Users will be able to efficiently produce their own TESS planet catalog starting with transit-like detections, as well as have the framework needed to characterize their catalog's completeness and reliability for occurrence rates.

Figures

Figures reproduced from arXiv: 2509.10619 by the authors.

Figure 1
Figure 1. Examples of non-planetary TCEs to demonstrate the typical false alarms and false positives targeted by select tests in LEO-Vetter. We show TCEs caused by stellar variability that failed the SWEET test (§3.3, top left) and the asymmetry test (§3.5, top right), a scattered light event that failed the single event domination test (§3.11, bottom left), and an eclipsing binary that failed the significant secondary test (… view at source ↗
Figure 2
Figure 2. An example of difference image analysis of the known false positive TOI-164.01. Left: average out-of-transit pixel image showing the pixels near TOI-164.01 (the average in-transit image looks very similar). Stars are shown as white-bordered disks with size and color determined by their TIC magnitudes, and the target star TOI-164 is marked by the magenta ’⋆’. Center: the difference pixel image, showing that the large… view at source ↗
Figure 3
Figure 3. Period histograms of TCEs from observed (un-scrambled) light curves (blue) and scrambled light curves (orange), demonstrating that scrambling effectively simu￾lates false alarm TCEs. The dotted grey lines indicate half, one, and two times the TESS orbital period (13.7 days). These histograms also demonstrate that a large majority of TESS TCEs are false alarms. However, LEO-Vetter is capa￾ble of producing planet cata… view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Results from testing LEO-Vetter on simulated planets and false alarms, as functions of SNR and either number of transits (left panels; note that the boundaries between bins are inclusive for the bin to the right, so, e.g., the leftmost boxes have either 3 or 4 candidat…
Figure 5
Figure 5. Figure 5: Period histograms from [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Period-radius diagram of systematics-dominated observed TCEs (left) and only PCs passing both flux- and pixel-level vetting (right). The properties of observed TCEs were estimated using the simple LEO-Vetter-implemented transit model fits, while the properties of PCs w…
Figure 7
Figure 7. Figure 7: An example of the analysis described in §7.3 , demonstrating the standard deviation of the MES metric as a function of period and MES. There is a similar grid for every metric. cause most values of Chases are 0. An example 2D grid for the standard deviation of the MES …
Figure 8
Figure 8. Figure 8: Promising PCs identified by LEO-Vetter that are not yet TOIs. The thirteen PCs which are new CTOIs as a result of this work are indicated by the label “New”. The rest are known CTOIs from other works (Eschen & Kunimoto 2024; Montalto et al. 2020; Feliz et al. 2021; Har…
Figure 8
Figure 8. Figure 8: Continued from previous page [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]

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  1. COUNTESS I: A Uniformly Vetted Catalog of Known and New Transiting Exoplanets in the TESS Northern Continuous Viewing Zone

    astro-ph.EP 2026-06 conditional novelty 6.0 of 10

    A multi-cadence TESS pipeline recovers 72% of known northern-CVZ TOIs and delivers 10 new planet candidates, including two statistically validated sub-Neptunes.

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.