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ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read ExoMiner++ claims that a deep network fed with five new diagnostic inputs—difference images, periodograms, flux trends, unfolded flux, and momentum-dump flags—can classify TESS 2-minute transit signals and rank candidates so precisely…

desk verdict Useful model and public catalog, but the headline precision belongs to the easy labeled subset; the only holdout from the ambiguous catalog population shows 0.76 precision, so the 7,330-PC claim needs a caveat. read the letter →

arxiv 2502.09790 v4 pith:RWZEEJAI submitted 2025-02-13 astro-ph.EP astro-ph.IMcs.LG

Hamed Valizadegan , Miguel J. S. Martinho , Jon M. Jenkins , Joseph D. Twicken , Douglas A. Caldwell , Patrick Maynard , Hongbo Wei , William Zhong
show 19 more authors
Charles Yates Sam Donald Karen A. Collins David Latham Khalid Barkaoui Michael L. Calkins Kylee Carden Nikita Chazov Gilbert A. Esquerdo Tristan Guillot Vadim Krushinsky Grzegorz Nowak Benjamin V. Rackham Amaury Triaud Richard P. Schwarz Denise Stephens Chris Stockdale Cristilyn N. Watkins Francis P. Wilkin
This is my paper · ORCID
classification astro-ph.EPastro-ph.IMcs.LG
keywords exoplanettransitclassificationTESSthresholdcrossingeventsdeeplearningfalsepositivevettingmulti-sourceplanetcandidatecatalogconvolutionalneuralnetwork
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

ExoMiner++ aims to establish that a deep-learning model can vet TESS 2-minute transit signals as reliably as experienced human reviewers, despite TESS's short observation windows, blended light, and noisy labels. The key proposal is to feed the model the same diagnostics a human would consult, including difference images, periodograms, flux trends, unfolded flux, and spacecraft attitude events, and to train on a mix of clean Kepler labels and noisier TESS labels. The paper reports that this combination reaches precision 0.933 and recall 0.951 on the labeled TESS subset, and that the top 1,000 ranked signals are nearly all exoplanets. Applied to 147,568 unlabeled threshold-crossing events, the model classifies 7,330 as planet candidates, including 50 new community TESS Objects of Interest. If the claims hold, follow-up efforts can concentrate on a few thousand candidates rather than the full catalog, raising the yield of confirmed planets per unit of telescope time.

What carries the argument

The engine is a multi-branch convolutional network whose branches mirror the diagnostic tests in the telescope's Data Validation report: phase-folded flux, odd/even flux, weak secondary, centroid motion, unfolded flux, difference image, periodogram, flux trend, and momentum-dump time series, plus stellar parameters and detection statistics as scalars. Each branch has its own feature extractor, and the three transit-view flux branches share low-level convolutional features; the model also receives the standard error of each phase bin as an extra channel. Two additional mechanisms drive much of the gain: multi-source learning, which mixes clean Kepler labels into the noisier TESS training set, and multi-sector aggregation, which assigns one score per physical event based on the longest sector run. Detrending was switched from a spline fit to a Savitzky-Golay filter, and the removed trend is fed back in as its own branch, letting the model see signals like ellipsoidal variations that occur at the orbital period timescale.

What would settle it

Follow up the 50 newly introduced CTOIs with radial-velocity and high-resolution imaging; if most turn out to be eclipsing binaries, background transits, or other false positives rather than planets, the claim that ExoMiner++ ranks the most likely candidates would be refuted.

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Extended reading notes

Core claim

The paper's central claim is that one convolutional architecture, ExoMiner++, can perform automated transit vetting for TESS at a level that narrows the follow-up search space while keeping few false positives at the top. For each TCE the model outputs a score between 0 and 1; a score above 0.5 counts as a planet candidate. On the labeled TESS dataset, the best configuration, trained on TESS plus Kepler and aggregated across sectors, reports precision 0.933, recall 0.951, PR AUC 0.976, and ROC AUC 0.998. Ranking quality is the headline: every one of the top 200 TCEs is an exoplanet, Precision@1000 is at least 0.99 across models, and Precision@3000 is 0.977 for the TESS+Kepler aggregate model. The paper argues this ranking transfers to unlabeled data: 7,330 of 147,568 unlabeled TCEs are classified as planet candidates, corresponding to 1,868 matched TOIs and 50 new CTOIs introduced by the authors. A temporal check adds support: of 151 TCEs promoted from uncertain TOI dispositions to confirmed planets between January and December 2024, ExoMiner++ scored 147 as planet candidates.

Load-bearing premise

The training and evaluation labels for non-planets, mostly non-transiting phenomena from flux triage and eclipsing binaries from the EB catalog, are accurate enough to serve as ground truth, despite acknowledged noise and failed ephemeris matches.

Editorial extensions

If this is right

  • Follow-up programs can target roughly 7,330 planet candidates instead of 147,568 unlabeled threshold-crossing events, with the top of the ranking consisting almost entirely of exoplanets.
  • TOIs with uncertain community dispositions can be re-ranked by model score; among 151 TOIs later confirmed as planets, 147 received candidate scores, suggesting the ranking can anticipate future confirmations.
  • The public catalog provides a confidence score per TCE and per TOI, allowing observers to schedule follow-up by score threshold and to revisit borderline cases.
  • The same architecture and preprocessing are directly portable to TESS full-frame-image data and to future transit surveys, since all branches consume standard diagnostics rather than mission-specific features.
  • If ranking quality persists, the overall yield of confirmed planets per observation hour should rise because telescope time will be spent on high-score candidates.

Reading between the lines

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

  • Because ExoMiner++ scores are not calibrated probabilities, the 0.5 cutoff is a working threshold, not a posterior; comparing scores across surveys or converting them into false-positive rates would require a separate calibration step.
  • The paper's label-noise examples cut both ways: if true planets are sometimes labeled as eclipsing binaries or non-transiting phenomena, the reported recall on TESS may be an underestimate, and the true precision of the 7,330-candidate catalog could be better than the labeled-set metrics suggest.
  • A clean prospective test would be to score each newly dispositioned TOI as the follow-up community updates its catalog over the next year, avoiding the circularity of evaluating on the same surrogate labels used for training.
  • The contaminated-aperture and nearby-background-transit subclasses remain the weakest spots, so adding known nearby-star positions and brightnesses as extra difference-image channels is a natural, testable extension in crowded fields.
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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 / 5 minor

Summary. ExoMiner++ is a deep-learning classifier for TESS 2-minute transit signals that extends the earlier ExoMiner architecture with five new input branches: transit-view unfolded flux, difference image, phase-folded momentum dump flags, a Lomb-Scargle periodogram, and a full-orbit folded flux trend. The model is trained with multi-source learning on Kepler plus TESS labels derived from ExoFOP, the Villanova EB catalog, and TESS-ExoClass NTPs, and is evaluated with 10-fold cross-validation using target-star splits. The authors report precision/recall of 0.933/0.951 and PR AUC 0.976 on the labeled TESS subset with TESS+Kepler aggregate scoring, show via ablations that most new branches improve performance, and release a vetting catalog in which 7,330 of 147,568 previously unlabeled TCEs are classified as planet candidates, including 50 newly introduced CTOIs.

Significance. If the catalog is as reliable as the headline metrics suggest, this is a substantial community resource: it narrows the TESS follow-up target list, provides confidence scores for ranking, and introduces new candidates. The paper has genuine strengths: the code is public, the cross-validation scheme splits on target stars rather than TCEs, the ablation study in Section 6.9 tests each new branch against a baseline, and the authors are unusually candid about label noise and ephemeris-matching failures in Section 4.1 and Section 6.7. The main deliverable is the 7,330-planet-candidate catalog, and the decision-relevant performance question is how well the model discriminates on the ambiguous, previously unlabeled population. The paper's own Section 7 evidence indicates that precision is substantially lower there than on the certain-label test set, so the catalog claim needs additional validation or careful qualification before it can be fully credited.

major comments (3)
  1. [Section 7 and Table 6] The decision-relevant precision for the catalog is not the Table 6 value of 0.933 on the certain-label TESS set, but the precision on TCEs that were unlabeled or uncertain in January 2024 and later resolved. The paper's own Section 7 calculation gives 147/(147+47)=0.76 precision and 0.97 recall on the 293 TCEs whose labels changed from PC/APC/FA to CP/FP between January and December 2024. That is a large drop from 0.933, and the 293-TCE subset is enriched in planets (prior about 0.5), whereas the full 147,568 UNK pool almost certainly has a much lower planet fraction. At a lower prior, precision at score>0.5 would fall further. Since the abstract's "7,330 planet candidates" and the implied follow-up prioritization rest on the UNK population, please report precision/recall and score-threshold curves directly for the label-transition subset, and either validate the catalog on a held-out ambiguous sample or substantially qualify the headline precision claim.
  2. [Section 4.1 and Section 6.7, item 4] The training and evaluation labels are surrogate labels from ExoFOP, the Villanova EB catalog, and TESS-ExoClass NTP triage. Section 6.7 gives explicit examples of known planets mislabeled as EBs (e.g., TIC 309792357 TCEs) and of planets mislabeled as NTPs due to failed ephemeris matching, and the authors acknowledge that the Villanova catalog has label noise. Because the same label-generation procedure produces both the training targets and the Table 6 evaluation labels, the reported 0.933/0.951 may be optimistically biased, and the 7,330 catalog classifications inherit that bias. Please add a quantitative label-noise robustness analysis, for example by re-evaluating on TCEs whose ExoFOP disposition changed between January and December 2024, or by reporting performance after removing Villanova/TEC labels that cannot be independently confirmed.
  3. [Section 7 (catalog construction)] The 50 newly introduced CTOIs are carefully vetted, but they are only a small subset of the 7,330 planet candidates. After the aggressive period matching, 427 unmatched TCEs remain and are reduced to 288 events and then to 91 high-confidence potential CTOIs by requiring consensus across all ten models and at least three observed transits; only these 91 are manually reviewed. The remaining thousands of PC classifications, including the 6,322 TCEs matched to existing TOIs and the 1,008 unmatched TCEs, do not receive the same SME scrutiny. Please state explicitly how many of the 7,330 are in each category and give separate expected-precision estimates for matched versus unmatched TCEs, so that the catalog's overall precision claim is not conflated with the well-vetted 50-CTOI list.
minor comments (5)
  1. [Abstract vs. Section 7] The abstract states 147,568 unlabeled TCEs, while Section 7 states 147,567 unlabeled TCEs. The numbers should be reconciled.
  2. [Tables and figures throughout] There are many typographical errors in the text and tables, including "T able", "T ransit", "V alizadegan", "TYIC" in Table 15, and "MESS" in figure axis labels. A careful proofread is needed.
  3. [Figure 6 and Figure 7 captions] The Figure 6 caption identifies TIC 82707763-1-S37 as TOI 1991.01, while the Figure 7 caption identifies the same TCE as TOI 1046.01 and refers to the target as TIC 309787037. The target identifier in the Figure 7 caption appears to be a copy-paste error and should be corrected.
  4. [Section 6.4] The sentence "There is no clearn pattern" should read "no clear pattern," and the surrounding discussion of MES and performance would benefit from a more direct presentation of the heatmap values.
  5. [Section 6.2] The description of multi-source learning says the simpler combining approach proved more effective, but the paper does not report the transfer-learning or fine-tuning comparison quantitatively. A sentence stating that those results are omitted for brevity would help the reader interpret the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: ExoMiner++ is a supervised classifier with held-out evaluation; the catalog counts are direct threshold outputs.

full rationale

The paper's derivation chain is self-contained: DV diagnostics are preprocessed into fixed-size inputs (Section 4.2), a deep network is trained with binary cross-entropy on labels assembled from ExoFOP, the Villanova EB catalog, and TESS-ExoClass NTPs plus Kepler data (Sections 4.1, 5), and performance is measured by 10-fold cross-validation on held-out target-star folds (Section 5, Table 6). The catalog claim of 7,330 planet candidates among 147,567 unlabeled TCEs is a direct application of the trained ensemble's thresholded scores (Section 7, Table 14), not a quantity that was fit to those TCEs. The additional holdout in Section 7, using 293 TCEs whose labels changed from PC/APC/FA to CP/FP between January and December 2024, is genuine out-of-sample evidence on the previously ambiguous population; the reported precision=0.76 versus the headline 0.933 is a generalization gap, not a circular reduction. The acknowledged label noise and ephemeris-matching failures (Sections 4.1 and 6.7) are validity limitations that could bias metrics, but they do not make any prediction equivalent to its training input by construction. Self-citations to ExoMiner (Valizadegan et al. 2022) describe architectural lineage and prior comparisons, but no load-bearing claim in this paper rests on an unverified self-citation or an imported uniqueness theorem. The central classification and ranking results are independently evaluated on held-out data, so no circular step is exhibited.

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

The central claim depends less on physics axioms than on the validity and completeness of the human-generated labels used for training and evaluation, and on the hand-chosen thresholds used to assemble the final catalog. The model itself introduces no new physical entities; its learned parameters and preprocessing choices are the main 'free' inputs.

free parameters (5)
  • classification score threshold = 0.5
    Binary labels are assigned by thresholding the model's output score at 0.5 (Section 6.1). Changing this threshold changes the catalog counts, and the paper does not justify this value for the unlabeled catalog.
  • ephemeris matching threshold = 0.75 cosine similarity
    TCEs are matched to catalog objects only if the cosine similarity of their ephemeris pulse trains exceeds 0.75 (Section 4.1). This hand-chosen threshold directly controls which TCEs receive labels and which remain unknown.
  • CTOI period matching tolerance = 1%
    TCEs with a period within 1% of a known TOI/CTOI are excluded when determining new CTOIs (Section 7). This tolerance affects the final count of 50 new CTOIs.
  • minimum observed transits for CTOI candidate = 3
    Only TCEs with at least three observed transits pass the conservative CTOI filter (Section 7). This cut changes the number of new CTOIs from 91 to 50.
  • Savitzky-Golay detrending window = 1.2 days
    The detrending window length is set to 1.2 days (Section 4.2), and this affects both the flux trend and periodogram inputs. It was chosen by the authors without a sensitivity analysis.
assumptions (5)
  • domain assumption The ExoFOP, Villanova EB, and TESS-ExoClass catalogs provide sufficiently accurate ground-truth labels for training and evaluation.
    Used throughout Section 4.1 to construct the labeled dataset. The authors explicitly acknowledge label noise and give examples of failed ephemeris matching that produced wrong labels, so the accuracy of these labels is a load-bearing assumption.
  • domain assumption Photometric diagnostics from SPOC data-validation reports contain enough information to distinguish planets from false positives in TESS data.
    The entire architecture (Section 3) processes only photometric and spacecraft data. Section 6.7 shows this is only partially true, particularly for SEB1 and NPC subcategories, which are systematically difficult.
  • domain assumption Kepler labels transfer to TESS through multi-source learning.
    Section 6.2 combines Kepler labels with TESS labels assuming shared discriminative features across telescopes. The improved TESS metrics suggest this holds empirically, but it remains an assumption about distribution shift.
  • standard math Backpropagation and the universal approximation theorem justify training a deep CNN for this classification task.
    Invoked in Section 2 to justify the deep learning approach. This is a standard mathematical background assumption and not specific to the paper.
  • domain assumption Splitting by target star prevents data leakage between training and test folds.
    Section 5 divides target stars into 10 folds. If TCEs from the same event or star leaked across folds, the reported cross-validation metrics would be inflated. The paper does not test this leakage explicitly.

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Cite this review

Pith. "Pith review of ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data." pith.science (2026). https://pith.science/paper/RWZEEJAI

@misc{pith2026250209790,
  author       = {Pith},
  title        = {Pith review of: ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RWZEEJAI}},
  note         = {Machine review of arXiv:2502.09790}
}
read the original abstract

We present ExoMiner++, an enhanced deep learning model that builds on the success of ExoMiner to improve transit signal classification in 2-minute TESS data. ExoMiner++ incorporates additional diagnostic inputs, including periodogram, flux trend, difference image, unfolded flux, and spacecraft attitude control data, all of which are crucial for effectively distinguishing transit signals from more challenging sources of false positives. To further enhance performance, we leverage multi-source training by combining high-quality labeled data from the Kepler space telescope with TESS data. This approach mitigates the impact of TESS's noisier and more ambiguous labels. ExoMiner++ achieves high accuracy across various classification and ranking metrics, significantly narrowing the search space for follow-up investigations to confirm new planets. To serve the exoplanet community, we introduce new TESS catalog containing ExoMiner++ classifications and confidence scores for each transit signal. Among the 147,568 unlabeled TCEs, ExoMiner++ identifies 7,330 as planet candidates, with the remainder classified as false positives. These 7,330 planet candidates correspond to 1,868 existing TESS Objects of Interest (TOIs), 69 Community TESS Objects of Interest (CTOIs), and 50 newly introduced CTOIs. 1,797 out of the 2,506 TOIs previously labeled as planet candidates in ExoFOP are classified as planet candidates by ExoMiner++. This reduction in plausible candidates combined with the excellent ranking quality of ExoMiner++ allows the follow-up efforts to be focused on the most likely candidates, increasing the overall planet yield.

Figures

Figures reproduced from arXiv: 2502.09790 by the authors.

Figure 1
Figure 1. Classification Architecture. 3. Feature summarization: This component summa￾rizes the features extracted by the feature extractor block. This is necessary to obtain more complex features on top of those obtained from the feature extractor block and provides a way to combine rele￾vant scalar values with features extracted from com￾plex data types such as time series or images. For example, in the presence of a weak s… view at source ↗
Figure 2
Figure 2. Deep Learning Implementation of the Classification Design in [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Difference image data for TESS SPOC TCE TIC 309787037-1-S35 (TOI 1046.01, a nearby EB), as shown in the DV mini-report. From left to right, top to bottom, the images show the difference flux, out-of-transit flux, in-transit flux, and difference SNR flux images. The red triangle marks the estimated transit source location, the red cross indicates the TIC’s coordinates in the CCD frame, the green plus represents the o… view at source ↗
Figures from the paper (18 more)
Figure 4
Figure 4. Figure 4: PDCSAP flux for TIC 167526485 in sector 6. • Added Momentum Dump branch: This branch is ded￾icated to processing momentum dump data. Space￾craft reaction wheel momentum management cycles are known to introduce artifacts in the data due to changing pointing behavior. Th…
Figure 5
Figure 5. Figure 5: PDCSAP flux for TIC 82707763 in sector 37. The occurrence of momentum dump events is shown as vertical dashed green lines. 3. For those TCEs without a label in the previous two catalogs, we label them as non-planets if they did not pass the TESS-ExoClass (TEC5 ) flux t…
Figure 6
Figure 6. Figure 6: Momentum dump data for TESS SPOC TCE TIC 82707763-1-S37 (TOI 1991.01, classified as a spectroscopic EB). The top panel displays the phase-folded time series for the momentum dump flag array, while the bottom panel presents the corresponding binned version obtained thro…
Figure 7
Figure 7. Figure 7: Preprocessed difference image data for TESS SPOC TCE TIC 82707763-1-S37 (TOI 1046.01, a nearby EB), following the steps outlined in Section 4.2 without normalization. From left to right, the panels display the difference image, out-of-transit image, and target position…
Figure 8
Figure 8. Figure 8: Unfolded binned flux data for TESS SPOC TCE TIC 98545929-1-S1-36 (TOI 1916.01, KP). The red dashed lines highlight the transit duration estimate of the detected TCE (∼ 3.68 hours) centered on the estimated transit mid￾point. • Periodogram pipeline: We applied the Lomb-…
Figure 9
Figure 9. Figure 9: Periodogram data for TESS SPOC TCE TIC 232568235-1-S24 (TOI 2260.01, CP) after preprocessing, as described in Section 4.2, without normalization. The fig￾ures display the flux and transit model periodograms. TOI 2260.01 is classified as a CP orbiting a variable star. T…
Figure 10
Figure 10. Figure 10: shows an example of flux trend data before it is normalized and fed to the model. In this case, the ellipsoidal variations occurring at half period of the EB in TIC 167526485 are made even more clear by phase-folding the flux trend time series using the orbital period…
Figure 11
Figure 11. Figure 11: Score distribution versus orbital period (top) and planet radius (bottom) of non-exoplanet categories for the TESS+Kepler-Aggregate model. Note: The width of bars does not carry any meaning. plot of scores versus orbital period and planet radius. An ideal model gives …
Figure 12
Figure 12. Figure 12: PR-Curve and Confidence of different models. factors: 1) most Kepler exoplanets were validated using a MES > 10 or MES > 10.5 threshold, and 2) some exoplanets in TESS’s KP category, previously detected by other telescopes, exhibit lower MES values in TESS data due to…
Figure 14
Figure 14. Figure 14: Kepler versus TESS histograms of large values of MES for exoplanets (KP+CP) and AFPs (EB+FP+BD). explores this key question, with implications for model trust and validation. 6.5. Ranking Performance [PITH_FULL_IMAGE:figures/full_fig_p020_14.png]
Figure 15
Figure 15. Figure 15: Precision and recall heatmaps as functions of MES, orbital period (days), and planet radius (Earth radii). White cells indicate regions with insufficient data, where the denominator is zero. including spectroscopic observations. Since performance metrics for KP and CP…
Figure 16
Figure 16. Figure 16: Precision@k for the top k TCEs for different values of k and different models. with the VPC+ category, which we discuss further in Section 6.6.2. To further examine the model’s behavior on these sub￾categories, we plotted the score histogram in [PITH_FULL_IMAGE:figur…
Figure 18
Figure 18. Figure 18: Score histograms of different subcategories in photometric disposition. 1. Insufficiency of photometric data: As dis￾cussed in Section 6.6.2, there are spectroscopic subcategories, such as SEB1, that exhibit clean photometric data (i.e., without any indications of bei…
Figure 17
Figure 17. Figure 17: Number of non-planets in the top k TCEs as ranked by ExoMiner++ using TESS+Kepler-Aggregate strat￾egy. 0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-1.0 Score Values 10 0 10 1 10 2 Log Count NEB NPC VPC PC EB VPC￾BEB VPC+ (a) Photometric …
Figure 19
Figure 19. Figure 19: Score histograms of different subcategories in spectroscopic disposition. on brighter stars, which facilitates better spectro￾scopic follow-up. A higher percentage of brighter stars with planets naturally results in more SEB1 detections. For example, TESS SPOC TCEs co…
Figure 20
Figure 20. Figure 20: SPOC DV summary report for TESS SPOC TCE TIC 55383975-1-S1-65 (TOI 694.01). Photometric disposition is PC however the follow up spectroscopic indicates that this is a SEB1 indeed. 5 15 25 35 45 55 65 75 85 95 Weak Secondary MES 50 40 30 20 10 0 10 20 30 40 50 Albedo C…
Figure 21
Figure 21. Figure 21: Albedo and planet effective temperature comparison statistics. When weak secondary MES > 7.1, the high values of these statistics indicate a FP. However, small values are uninformative [PITH_FULL_IMAGE:figures/full_fig_p026_21.png]
Figure 22
Figure 22. Figure 22: Distribution of centroid offset statistic. ual classification of photometric data, is subject to some inaccuracies. Furthermore, it is important to note that the Villanova EB catalog was constructed using only the first two years of TESS data, and with additional data…

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    astro-ph.SR 2025-06 conditional novelty 6.0 of 10

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