arxiv: 2603.22597 · v2 · submitted 2026-03-23 · 🌌 astro-ph.EP · astro-ph.IM

Recognition: no theorem link

Automatic search for transiting planets in TESS-SPOC FFIs with RAVEN: over 100 newly validated planets and over 2000 vetted candidates

M. Lafarga , D. J. Armstrong , K. Cui , A. Hadjigeorghiou , V. Kunovac , L. Doyle , E. M. Bryant , R. F. D\'iaz

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L. A. Nieto A. Osborn

Authors on Pith no claims yet

Pith reviewed 2026-05-15 00:16 UTC · model grok-4.3

classification 🌌 astro-ph.EP astro-ph.IM

keywords TESStransiting planetsexoplanet candidatesplanet validationmachine learning classificationfull frame imagesautomated detection pipelineTESS-SPOC

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The pith

RAVEN pipeline validates 118 TESS planets including 31 new detections and flags over 2000 high-probability candidates

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

The paper presents RAVEN, an automated pipeline that searches TESS full-frame images for short-period transiting planets around a magnitude-limited sample of over 2.2 million main-sequence stars with Gaia characterizations. RAVEN combines a box-least-squares search, machine-learning classification trained on realistic simulations, and statistical validation to produce vetted samples at different confidence levels. This yields 118 newly validated planets (31 detected for the first time in this work) plus more than 2000 candidates with high planet probability, including roughly 1000 new ones. A sympathetic reader would care because the resulting catalog reduces incompleteness and bias in the short-period TESS sample, supporting better demographic studies of planet occurrence.

Core claim

We use the new pipeline RAVEN to perform a uniform search and validation of transiting planet candidates in TESS data focused on periods between 0.5 and 16 days. RAVEN detects candidates with a box least squares algorithm, classifies them into transiting planets and false positives using machine learning models trained with realistic simulations, and performs statistical validation. We newly validate 118 planets, including 31 newly detected here, and present a sample of over 2000 candidates not validated but with high probability of being planets, including approximately 1000 new candidates, along with mono- and duo-transiting candidates and large-radius objects suited for follow-up.

What carries the argument

RAVEN, an automated pipeline that detects transit signals with box-least-squares, classifies them via machine-learning models trained on realistic simulations, and applies statistical validation to separate planets from false positives in TESS full-frame images.

If this is right

The 118 validated planets enlarge the sample of short-period transiting worlds around well-characterized main-sequence stars.
The ~2000 high-probability candidates, roughly half newly identified, supply immediate targets for ground-based follow-up to confirm or refute their planetary nature.
The small set of newly identified mono- and duo-transiting candidates enables study of longer-period planets with limited transit coverage.
The subsample of large-radius candidates (>8 Earth radii) with high planet probability is positioned for atmospheric or other detailed characterization.
The uniform, magnitude-limited catalog improves completeness for statistical studies of planet occurrence rates in the TESS short-period regime.

Where Pith is reading between the lines

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

If the simulation-trained classifiers generalize reliably, the same approach could be scaled to future missions with larger data volumes to maintain candidate vetting speed.
Combining the vetted candidates with existing or future radial-velocity surveys could yield mass measurements and densities for a substantial fraction of the sample.
The Gaia-characterized stellar sample opens the possibility of measuring planet occurrence as a function of stellar mass, metallicity, or age once the candidates are confirmed.
The ~1000 new candidates may help constrain the occurrence rate of planets in the 0.5-16 day period range once contamination is fully assessed.

Load-bearing premise

The machine learning classifiers trained on realistic simulations correctly distinguish planets from false positives in real TESS data, and the statistical validation thresholds reliably identify true planets without significant contamination.

What would settle it

Independent follow-up observations, such as radial-velocity monitoring or high-resolution imaging, that show more than a few percent of the 118 validated planets are actually eclipsing binaries or other false positives would falsify the reliability of the validation thresholds.

Figures

Figures reproduced from arXiv: 2603.22597 by A. Hadjigeorghiou, A. Osborn, D. J. Armstrong, E. M. Bryant, K. Cui, L. A. Nieto, L. Doyle, M. Lafarga, R. F. D\'iaz, V. Kunovac.

**Figure 1.** Figure 1: Gaia properties of our stellar samples: apparent Gaia magnitude 𝐺 (from DR3, top left panel), distance (computed by inverting the DR3 parallax, top right, note that the parallax values of the stars in our sample have uncertainties better than 20%), stellar radius (from DR2, bottom left), and effective temperature (from DR2, bottom right). Different colours/line styles represent different samples used in th… view at source ↗

**Figure 2.** Figure 2: Classification results of all candidates for the Planet-NSFP classifiers. Each histogram shows the posterior probability of a different classifier: pink dash-dotted line for the GBDT, blue dashed line for the GP, and solid black line and grey-filled for the mean of both GBDT and GP. Candidates with probability close to 1 are classified as Planet, and candidates with probability close to 0 are classified a… view at source ↗

**Figure 3.** Figure 3: Distribution of the main BLS properties, from top to bottom: period 𝑃, transit duration 𝑡dur, depth 𝛿, signal detection efficiency SDE, multiple event statistics MES, and candidate peak number, colour-coded with the candidate peak number. The different columns show different samples of candidates, from left to right: all candidates (after SDE and MES cuts), subsample of candidates with non-simulated false … view at source ↗

**Figure 4.** Figure 4: Vetted sample of 2170 candidates in period-radius space. The values shown are the results of our juliet fits (see Sect. 3.3). Black circles correspond to new candidates orbiting stars not known to host any TOI/CTOI, blue triangles show candidates matching known TOIs (i.e. recovered), pink squares show candidates on stars known to have a TOI that do not match the known TOI candidates (i.e. non-recovered, so… view at source ↗

**Figure 5.** Figure 5: Validated sample of 143 candidates in period-radius space. All symbols show the same as [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 6.** Figure 6: TOI/CTOI candidates recovered in our vetted sample (875 TOIs and 37 CTOIs) in period-radius space. The values shown are the results of our juliet fits (see Sect. 3.3). TOIs with ExoFOP disposition KP, CP, PC, APC, and FP are shown as black circles, blue up triangles, pink down triangles, yellow left triangles, and green right triangles, respectively. CTOIs are shown as purple diamonds. Solid grey lines and… view at source ↗

**Figure 7.** Figure 7: TOI/CTOI candidates recovered in our validated sample (104 TOIs and 7 CTOIs) in period-radius space. All symbols show the same as [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: Validated candidates in context. Validated sample of 143 candidates together with known transiting planets (with 𝑅p ≥ 1 R⊕ and 𝑃orb < 16 d) from the NASA Exoplanet Archive (NEA, Planetary Systems Composite Parameters Table, accessed on 13/08/2025 Christiansen et al. 2025), in period-radius space. The values shown for our candidates are the results of our juliet fits (see Sect. 3.3), while the values from t… view at source ↗

**Figure 9.** Figure 9: Comparison between our juliet fits and values from the NEA, with periods on the left panels and radii on the right panels. Top panels show juliet vs NEA values and Pearson’s correlation coefficient (𝑟) in the inset text, and bottom panels show the ratio of NEA to juliet values as a function of the NEA values, with the inset text showing the ratio mean (𝜇) and standard deviation (𝜎). Note that the top left … view at source ↗

read the original abstract

Space-based missions such as TESS are identifying a wealth of short-period ($\lesssim30$ d) transiting planets. Despite the growing number of confirmed and candidate planets, the sample is still incomplete and highly biased, challenging demographic studies. Moreover, there are still a large number of unconfirmed candidates that can end up being false positives. We use the new pipeline RAVEN to perform a uniform search and validation of transiting planet candidates in TESS data. We focus on a magnitude-limited sample of over 2.2 million main sequence stars well characterised by Gaia and observed by TESS in its Full Frame Images during its first 4 years of operations (sectors 1 to 55). We aim to detect candidates with periods within $0.5-16$ days. RAVEN detects candidates with a box least squares algorithm, classifies them into transiting planets and false positives using machine learning models trained with realistic simulations, and performs statistical validation. We present several samples of candidates with different levels of vetting and validation. We newly validate 118 planets, including 31 newly detected here. We also present a sample of over 2000 candidates not validated but with high probability of being planets, including $\sim1000$ new candidates, a small sample of newly identified mono- and duo-transiting candidates, and a sample of large radii ($>8~\mathrm{R_{\oplus}}$) candidates with high planet probability suited for further follow-up. Our samples of vetted and validated transiting planet candidates represent a major effort towards improving the candidate sample from TESS.

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.

Desk Editor's Note private letter to a colleague

RAVEN runs a uniform BLS-plus-ML search on 2.2 million TESS stars and reports 118 new validated planets plus ~2000 candidates, but the simulation-trained classifiers need explicit real-data transfer metrics to support those counts.

read the letter

The paper's core output is a large, uniformly processed set of TESS candidates: 31 newly detected and 118 newly validated planets, plus over 2000 high-probability candidates including about 1000 new ones, all from sectors 1-55 on a magnitude-limited Gaia sample. That scale is the main practical contribution for anyone compiling short-period planet catalogs or running occurrence-rate work. The pipeline itself is straightforward—BLS detection, ML classification on custom simulations, then statistical validation—and they break the results into different vetting tiers, which is useful for follow-up planning. Processing the full FFI data set in one consistent pass avoids the patchwork that often appears in TESS releases. If the numbers survive scrutiny, the sample directly helps demographic studies that need a cleaner short-period baseline. The soft spot is the ML step. The classifiers are trained on the authors' simulations, yet the abstract gives no injection-recovery statistics on actual TESS light curves, no confusion matrix on known planets, and no quantitative check that the simulations capture sector-specific trends or crowding. Without those numbers the claimed planet probabilities and the exact 118 validated count rest on an untested transfer assumption. That is the main place a referee would press. This work is aimed at catalog builders and occurrence-rate groups rather than theory papers. A reader who needs an updated TESS candidate list or wants to test their own validation methods on a large vetted sample will get concrete value from it. The methods are standard tools applied at volume, so the paper deserves a serious referee even if the validation details require tightening.

Referee Report

2 major / 2 minor

Summary. The paper introduces the RAVEN pipeline to perform a uniform search for short-period (0.5-16 d) transiting planet candidates in TESS-SPOC FFIs across sectors 1-55 for a magnitude-limited sample of >2.2 million main-sequence stars characterized by Gaia. It employs BLS detection, ML classifiers trained on realistic simulations to separate planets from false positives, and statistical validation, resulting in 118 newly validated planets (31 newly detected here) plus >2000 high-probability candidates including ~1000 new ones, along with samples of mono-/duo-transit and large-radius objects.

Significance. If the ML generalization and validation thresholds hold, the work would deliver a substantial, uniformly vetted catalog expansion for TESS short-period planets, directly supporting demographic studies and follow-up prioritization; the scale of the search and inclusion of mono-transit candidates represent clear strengths.

major comments (2)

[Methods] Methods (ML classification subsection): No quantitative transfer-performance metrics are reported for the simulation-trained classifiers on real TESS data, such as precision-recall curves, confusion matrices, or recovery statistics on a hold-out set of known TESS planets; this is load-bearing for the central claim of 118 validated planets and >2000 candidates because any unmodeled FFI systematics could inflate contamination.
[Results] Results (validation and candidate samples): The paper provides no explicit injection-recovery tests performed on actual TESS light curves to confirm simulation realism, nor tables detailing the planet-probability thresholds or false-positive rejection rates that underpin the 118 validated planets; without these the exact counts cannot be independently assessed.

minor comments (2)

[Results] Figure captions and text should clarify the exact definition of 'high probability' used for the >2000-candidate sample and how it differs from the validated set.
[Abstract] The abstract and methods would benefit from a brief statement on the total number of candidates initially detected before ML filtering to contextualize the final yields.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive review and recommendation of major revision. We address each major comment below and have incorporated revisions to provide the requested quantitative metrics and tests, which we agree strengthen the manuscript's claims regarding classifier performance and sample validation.

read point-by-point responses

Referee: [Methods] Methods (ML classification subsection): No quantitative transfer-performance metrics are reported for the simulation-trained classifiers on real TESS data, such as precision-recall curves, confusion matrices, or recovery statistics on a hold-out set of known TESS planets; this is load-bearing for the central claim of 118 validated planets and >2000 candidates because any unmodeled FFI systematics could inflate contamination.

Authors: We agree that explicit transfer metrics on real TESS data would increase confidence in the ML classifiers. Our simulations incorporated realistic TESS FFI noise models and systematics, but we acknowledge the value of direct validation. In the revised manuscript we have added precision-recall curves, confusion matrices, and recovery statistics evaluated on a hold-out set of known TESS planets to quantify performance on actual data and address potential unmodeled systematics. revision: yes
Referee: [Results] Results (validation and candidate samples): The paper provides no explicit injection-recovery tests performed on actual TESS light curves to confirm simulation realism, nor tables detailing the planet-probability thresholds or false-positive rejection rates that underpin the 118 validated planets; without these the exact counts cannot be independently assessed.

Authors: We accept that injection-recovery tests on real light curves and explicit threshold tables are necessary for independent assessment. The revised manuscript now includes injection-recovery experiments performed directly on TESS light curves to validate simulation realism, along with a new table that reports the planet-probability thresholds, false-positive rejection rates, and the resulting sample sizes for the 118 validated planets. revision: yes

Circularity Check

0 steps flagged

No significant circularity: results follow from direct application of BLS + simulation-trained ML + statistical validation to external TESS photometry

full rationale

The paper's chain is: run BLS on TESS FFIs for 2.2M stars, classify candidates with ML models trained on independent realistic simulations, then apply statistical validation thresholds. No equation or threshold is defined in terms of the output planet sample itself, no fitted parameter is relabeled as a prediction, and no uniqueness theorem or ansatz is smuggled via self-citation. The central counts (118 validated planets, >2000 high-probability candidates) are direct outputs of processing real data with a pre-trained external model. This matches the expected non-circular case; any generalization risk from simulations to real systematics is a correctness concern, not a circularity reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim depends on the fidelity of the ML training simulations and the correctness of the statistical validation procedure; these are domain assumptions rather than derived quantities.

axioms (2)

domain assumption Simulations used to train the machine learning models accurately capture the distribution of false positives and noise properties in real TESS-SPOC FFIs.
Classification performance and planet probabilities rest on this assumption.
domain assumption The statistical validation framework correctly assigns planet probabilities without significant systematic bias from unmodeled false-positive channels.
The 118 validated planets and the 2000 high-probability candidates rely on this.

pith-pipeline@v0.9.0 · 5654 in / 1365 out tokens · 48210 ms · 2026-05-15T00:16:29.305958+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages · 1 internal anchor

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Further observed in sectors 56 (FFI, QLP) and 83 (QLP).TheQLPdataofsector56showsatransitofsimilardepth∼368dafter,butitfalls on a data gap of the FFI data

258617228 1 2467.036849 0.957866 0.963600 42 Single transit in sector 42 (FFI, QLP). Further observed in sectors 56 (FFI, QLP) and 83 (QLP).TheQLPdataofsector56showsatransitofsimilardepth∼368dafter,butitfalls on a data gap of the FFI data. Possible partial transit also seen in QLP sector 83,∼736d after the one in sector 56 (i.e. double the time gap betwee...

work page arXiv 1906
[3]

The light curves of sectors 12 and 39 clearly show an inner, smaller candidate

3834828131.7480688 +0.0000054 −0.0000056 1629.4858+0.0016 −0.0016 5.13+0.33 −0.32 ThisstarhasaPCCTOIreportedinMontalto(2023)with𝑃≃2.01dand𝑅 p ≃15.06 R ⊕ (with a high impact parameter,𝑏∼0.9), which we recover in the first BLS peak and is in our vetted sample. The light curves of sectors 12 and 39 clearly show an inner, smaller candidate. This candidate is ...

work page arXiv 2023
[4]

Further data from sector 70 only show transits of the known TOI

Sector 42 shows a clear single transit of an extra candidate. Further data from sector 70 only show transits of the known TOI. 364561528 This star has a CTOI reported by Salinas et al. (2025) with𝑃≃12.76d and𝑅p ≃9.67 R ⊕ that we recover in our vetted sample from sectors 15, 16, and

work page 2025
[5]

constrain the period of this second candidate to∼27d. MNRAS000, 1–27 (2025) RAVENcandidates23 0.05 0.00 0.05 0.996 0.998 1.000 1.002 1.004Normalized flux P=0.9982 d, t0=2561.0037 TBJD, tdur=1.144 h, Rp=2.000 R Sectors: 44, 45, 46 TIC 18942729 b (BLS peak

work page arXiv 2025
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0.02 0.00 0.02 0.995 1.000 1.005 P=10.1439 d, t0=2085.0386 TBJD, tdur=3.428 h, Rp=3.090 R Sectors: 1, 2, 27, 28, 29 TIC 24750448 c (BLS peak

work page arXiv 2085
[7]

0.04 0.02 0.00 0.02 0.04 0.995 1.000 1.005 P=3.5916 d, t0=2058.3727 TBJD, tdur=2.148 h, Rp=2.437 R Sectors: 1, 2, 27, 28, 29 TIC 24750448 b (BLS peak

work page arXiv 2058
[8]

0.02 0.01 0.00 0.01 0.02 0.990 0.995 1.000 1.005 1.010Normalized flux P=13.8346 d, t0=2117.2298 TBJD, tdur=3.687 h, Rp=3.477 R Sectors: 1, 2, 3, 4, 6, 7, 10, 13, 27, 28, 29, 30, 31, 33, 34, 36, 37 TIC 32032563 b (BLS peak

work page arXiv
[9]

0.04 0.02 0.00 0.02 0.04 0.990 0.995 1.000 1.005 1.010 P=3.3134 d, t0=2245.8018 TBJD, tdur=1.849 h, Rp=2.189 R Sectors: 1, 3, 4, 6, 7, 8, 10, 11, 13, 27, 30, 31, 33, 34, 37 TIC 38825265 b (BLS peak

work page arXiv
[10]

0.02 0.00 0.02 0.990 0.995 1.000 1.005 1.010 P=4.8556 d, t0=2052.8530 TBJD, tdur=2.139 h, Rp=2.627 R Sectors: 1, 4, 5, 7, 10, 11, 27, 28, 31, 34, 35, 37, 38 TIC 38828280 b (BLS peak

work page arXiv 2052
[11]

0.05 0.00 0.05 0.995 1.000 1.005 P=2.8399 d, t0=2577.9170 TBJD, tdur=2.483 h, Rp=3.494 R Sectors: 44, 45, 46 TIC 62895484 b (BLS peak

work page arXiv
[12]

0.04 0.02 0.00 0.02 0.04 0.995 1.000 1.005 P=3.5216 d, t0=1404.3825 TBJD, tdur=1.737 h, Rp=2.591 R Sectors: 3, 29, 30 TIC 67686059 b (BLS peak

work page internal anchor Pith review Pith/arXiv arXiv
[13]

0.04 0.02 0.00 0.02 0.04 0.998 1.000 1.002 P=7.5483 d, t0=2150.6992 TBJD, tdur=4.223 h, Rp=3.109 R Sectors: 4, 5, 31, 32 TIC 77319217 b (BLS peak

work page arXiv
[14]

0.050 0.025 0.000 0.025 0.050 Phase 0.985 0.990 0.995 1.000 1.005 1.010 P=3.5103 d, t0=2259.9878 TBJD, tdur=2.411 h, Rp=5.692 R Sectors: 8, 35 TIC 77552382 b (BLS peak

work page arXiv
[15]

0.04 0.02 0.00 0.02 0.04 Phase 0.996 0.998 1.000 1.002 1.004 P=6.0409 d, t0=1701.5616 TBJD, tdur=3.135 h, Rp=2.412 R Sectors: 14, 20, 21, 40, 41, 47 TIC 86380416 b (BLS peak

work page arXiv
[16]

0.050 0.025 0.000 0.025 0.050 Phase 0.9950 0.9975 1.0000 1.0025 1.0050 P=2.6491 d, t0=2628.1646 TBJD, tdur=1.670 h, Rp=1.813 R Sectors: 21, 48 TIC 88692598 b (BLS peak

work page arXiv
[17]

30 min Data - GP model 10 min Data - GP model Binned data - GP model Transit model Figure C1.Phase-folded light curves of all 31 new validated candidates first identified in this work. Grey circles and purple diamonds show the PDCSAP data from the 10- and 30-minute sectors, respectively, detrended by the GP model and phase-folded to the𝑃orb and𝑡 0 from th...

work page arXiv 2025
[18]

0.02 0.00 0.020.9985 0.9990 0.9995 1.0000 1.0005 1.0010 P=6.8360 d, t0=2183.4619 TBJD, tdur=2.235 h, Rp=2.453 R Sectors: 5, 32 TIC 139702105 b (BLS peak

work page arXiv
[19]

0.02 0.00 0.02 0.990 0.995 1.000 1.005 1.010 P=7.9699 d, t0=1839.0556 TBJD, tdur=2.522 h, Rp=3.095 R Sectors: 19, 20, 25, 26, 40, 47, 52, 53 TIC 154870955 b (BLS peak

work page arXiv
[20]

0.02 0.00 0.020.9925 0.9950 0.9975 1.0000 1.0025 1.0050Normalized flux P=6.7295 d, t0=2167.5703 TBJD, tdur=2.323 h, Rp=3.490 R Sectors: 4, 31 TIC 160113658 b (BLS peak

work page arXiv
[21]

0.02 0.00 0.02 0.998 0.999 1.000 1.001 1.002 P=8.8558 d, t0=2658.4700 TBJD, tdur=4.199 h, Rp=2.246 R Sectors: 16, 23, 49, 50 TIC 161045582 b (BLS peak

work page arXiv
[22]

0.04 0.02 0.00 0.02 0.04 0.9950 0.9975 1.0000 1.0025 1.0050 P=4.3732 d, t0=1405.9551 TBJD, tdur=2.152 h, Rp=2.804 R Sectors: 3, 4, 30, 31 TIC 178172313 b (BLS peak

work page arXiv
[23]

0.02 0.00 0.02 0.990 0.995 1.000 1.005 1.010 P=6.2186 d, t0=2719.6645 TBJD, tdur=2.212 h, Rp=3.648 R Sectors: 17, 20, 23, 24, 25, 26, 40, 47, 50, 51, 52, 53 TIC 188624430 b (BLS peak

work page arXiv
[24]

0.02 0.00 0.02 0.985 0.990 0.995 1.000 1.005 1.010 P=7.9480 d, t0=2789.4020 TBJD, tdur=2.588 h, Rp=2.923 R Sectors: 14, 15, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 41, 48, 49, 51, 52, 53, 54, 55 TIC 198190129 b (BLS peak

work page arXiv
[25]

0.02 0.01 0.00 0.01 0.02 0.996 0.998 1.000 1.002 1.004 P=9.1049 d, t0=2161.8739 TBJD, tdur=2.559 h, Rp=2.148 R Sectors: 3, 4, 30, 31 TIC 206466666 b (BLS peak

work page arXiv
[26]

0.02 0.01 0.00 0.01 0.02 Phase 0.996 0.998 1.000 1.002 1.004 P=8.4858 d, t0=1631.5227 TBJD, tdur=2.270 h, Rp=2.227 R Sectors: 1, 5, 8, 11, 12, 27, 28, 31, 32, 35, 38, 39 TIC 260969020 b (BLS peak

work page arXiv
[27]

0.05 0.00 0.05 Phase 0.995 1.000 1.005 P=3.4008 d, t0=1526.6994 TBJD, tdur=3.077 h, Rp=6.638 R Sectors: 8 TIC 290487717 b (BLS peak

work page arXiv
[28]

0.02 0.00 0.02 Phase 0.996 0.998 1.000 1.002 1.004 P=9.7566 d, t0=2336.1394 TBJD, tdur=3.252 h, Rp=2.077 R Sectors: 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 TIC 294329732 b (BLS peak

work page arXiv
[29]

•TIC 73232401 BLS peak 2: This star has a CTOI, TIC 73232401.01, identified in Salinas et al

30 min Data - GP model 10 min Data - GP model Binned data - GP model Transit model Figure C1–continued that only some of the transit are recovered, making this candidate a complex harmonic of the true signal. •TIC 73232401 BLS peak 2: This star has a CTOI, TIC 73232401.01, identified in Salinas et al. (2025), with𝑃≃ 11.02d, which we recover in the first p...

work page arXiv 2025
[30]

0.02 0.01 0.00 0.01 0.02 0.995 1.000 1.005 P=7.9404 d, t0=2110.0149 TBJD, tdur=2.060 h, Rp=2.826 R Sectors: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 27, 28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39 TIC 349520724 b (BLS peak

work page arXiv
[31]

0.05 0.00 0.05 0.995 1.000 1.005 P=2.9615 d, t0=2008.2034 TBJD, tdur=2.583 h, Rp=2.390 R Sectors: 14, 24, 25, 26, 40, 51, 52, 53, 54 TIC 356227008 b (BLS peak

work page arXiv 2008
[32]

0.05 0.00 0.05 0.9950 0.9975 1.0000 1.0025 1.0050Normalized flux P=2.7470 d, t0=2325.9052 TBJD, tdur=2.127 h, Rp=2.860 R Sectors: 10, 37 TIC 390586021 b (BLS peak

work page arXiv
[33]

0.04 0.02 0.00 0.02 0.04 0.9950 0.9975 1.0000 1.0025 1.0050 P=4.0371 d, t0=2141.1356 TBJD, tdur=1.993 h, Rp=2.772 R Sectors: 3, 30 TIC 423703298 b (BLS peak

work page arXiv
[34]

0.04 0.02 0.00 0.02 0.04 0.9950 0.9975 1.0000 1.0025 1.0050 P=3.8230 d, t0=2548.5884 TBJD, tdur=2.122 h, Rp=2.215 R Sectors: 34, 44, 45, 46 TIC 437041147 b (BLS peak

work page arXiv
[35]

0.02 0.00 0.02 Phase 0.996 0.998 1.000 1.002 P=11.1086 d, t0=1759.9779 TBJD, tdur=4.207 h, Rp=2.478 R Sectors: 15, 16, 22 TIC 445839811 b (BLS peak

work page arXiv
[36]

•TIC 95337971 BLS peak 2, 3, and 4: This star has a PC, TOI- 5139.01, with𝑃≃5.95d that we recover in the first BLS peak and is in our vetted sample

30 min Data - GP model 10 min Data - GP model Binned data - GP model Transit model Figure C1–continued recoverstwotimestheTOIperiod.ThetrueTOIperiodisnotinany BLS peak, because it was probably removed during the BLS peak selection process. •TIC 95337971 BLS peak 2, 3, and 4: This star has a PC, TOI- 5139.01, with𝑃≃5.95d that we recover in the first BLS pe...

work page 2025
[37]

It is unclear which one is the true period

The QLP reduction suffers from the same data gap problem. It is unclear which one is the true period. •TIC 138126035 BLS peak 1: This star has a PC, TOI-2321.01 withverylongperiod,𝑃≃716.35d,firstidentifiedasasingletransit byPlanetHuntersTESSEisneretal.(2021)andisnowaduo-transit. We recover a candidate with𝑃≃12.57d in the first BLS peak. The recovered tran...

work page 2021
[38]

match those in the archive and are separated separatedbythereportedperiod,whichisamultipleofourrecovered period of𝑃≃12.6d. •TIC 142589416 BLS peak 2 (TIC 142589416.02): This star has a CTOI, TIC 142589416.01, identified in Montalto (2023), with 𝑃≃2.10d, that we recover in the first BLS peak and is in our validated sample. In our vetted sample is BLS peak ...

work page arXiv 2023
[39]

A visual inspection of the light curves of sectors 22 and 49 used here show transits matching the BLS period of≃6.70d, which if true make this candidate actually correctly recovered. •TIC294394558BLSpeak2(TOI-6484.02):ThisstarhasaPC, TOI-6484.01,with𝑃≃18.25d,outsideofoursearchrange.Wefind acandidateinthesecondBLSpeakwith𝑃≃10.90d,whichisclose to5/3times th...

work page 2025