Pith. sign in

REVIEW 3 major objections 6 minor 60 references

What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper reports that the SNAD PineForest anomaly-detection workflow found six previously uncatalogued variable stars in ZTF DR23 light curves within the fields also targeted by Rubin's LSSTComCam, and that the same workflow is a ready…

desk verdict A modest, honest variable-star paper: six likely new variables and refined periods, but the 'previously uncatalogued' claim needs stronger cross-matching before it can be taken at face value. read the letter →

arxiv 2507.06217 v1 pith:OBSQ6MHA submitted 2025-07-08 astro-ph.IM astro-ph.GAastro-ph.SRcs.LG

Maria V. Pruzhinskaya , Anastasia D. Lavrukhina , Timofey A. Semenikhi , Alina A. Volnova , Sreevarsha Sreejith , Vadim V. Krushinsky , Emmanuel Gangler , Emille E. O. Ishida
show 2 more authors
Matwey V. Kornilov Konstantin L. Malanchev
This is my paper · ORCID
classification astro-ph.IMastro-ph.GAastro-ph.SRcs.LG
keywords time-domainastronomyanomalydetectionvariablestarsZwickyTransientFacilityLSSTComCamactivelearninglightcurvesPineForest
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 an active-learning anomaly-detection pipeline, built around the PineForest algorithm, can find astrophysically interesting outliers in large survey light curves before Rubin's main survey begins. To do this, the authors ran the pipeline on ZTF DR23 data restricted to four sky regions that the Rubin commissioning camera LSSTComCam also observed, visually inspected 400 candidates ranked anomalous, and came away with six variable stars absent from the major variability catalogs, plus improved periods and classifications for six known ones. If the claim stands, it means the human-in-the-loop approach the SNAD team has developed for ZTF carries over to the deeper, denser Rubin data, and that even the small overlap region between the two surveys is rich enough to yield new stellar variables. The concrete payoff is a short list of new RS CVn, BY Draconis, ellipsoidal, and solar-type variables that can be followed up spectroscopically, and a tested search recipe for LSST-era anomaly hunting.

What carries the argument

The load-bearing mechanism is PineForest, an active-learning variant of the Isolation Forest algorithm that operates on a 47-dimensional feature space of light-curve statistics: rather than ranking every object by a fixed isolation score, it repeatedly retrains the forest keeping only the decision trees whose anomaly judgments agree with the expert's labels, so the outlier definition sharpens with each labelling round. This is combined with a fixed pre-processing chain—$z_r$-band light curves from ZTF DR23 with quality cuts and at least 100 detections, feature extraction with the light-curve package, and coverage-radius estimation for the LSSTComCam fields—which is what lets the algorithm isolate anomalous objects from the roughly 42,000 light curves inside the four selected fields.

What would settle it

Take a spectrum of each of the six new objects: if, for example, the tentative RS CVn star SNAD278 shows no chromospheric activity indicators (such as Hα or Ca H&K emission) or the ellipsoidal variable SNAD274 shows no radial-velocity variation at its 15.24-day period, the photometric classifications specifically claimed in Section 4.1 fail. A future Gaia data release or a deep variability catalog that already lists any of the six would likewise falsify the 'previously uncatalogued' core of the claim.

Watch

Extended reading notes

Core claim

The central discovery is that six ZTF light curves selected by PineForest as anomalous in LSSTComCam overlap fields are genuine, previously uncatalogued variable stars: two show solar-type variability, two are tentatively classed as RS CVn systems, one is an ellipsoidal variable, and one is a BY Draconis-type spotted star, with periods between roughly 2 and 15 days. The paper also revises the periods or classifications of six known variables, most notably correcting an eclipsing-binary period by a factor of two and reclassifying a purported irregular variable as an ellipsoidal binary. These results are presented as evidence that the SNAD pipeline—feature extraction from ZTF DR23 light curves, PineForest outlier ranking with expert feedback, and visual inspection—is effective at finding rare variability that standard catalogs missed, and as a preview of what systematic anomaly searches will do with Rubin data.

Load-bearing premise

The variability classes assigned to the six new stars are inferred purely from photometric light-curve shape, Gaia colours and parallaxes, with no spectra and no period uncertainties, so a mistaken class would not change the objects' variability but would invalidate the specific RS CVn / BY Dra / ellipsoidal / solar-type claims.

Editorial extensions

If this is right

  • The six objects (SNAD273–SNAD279) become new entries in the variable-star census and are individually available for spectroscopic follow-up.
  • The corrected periods and reclassifications (e.g., the eclipsing binary whose period is revised from 0.54928 to 1.10166 days) improve the fidelity of existing variable-star catalogs.
  • The same feature-extraction plus PineForest plus visual-inspection workflow can be applied as-is to Rubin's LSST data once light-curve features become available, providing a tested anomaly-search template for the much larger survey.
  • The yield of six new variables from 400 inspected candidates within the four overlap fields gives a concrete sense of the discovery rate to expect when the same search is run on Rubin's much larger dataset.
  • Because the pipeline also recovered AGN, asteroids, a supernova candidate, and artifacts, it can serve as a generic outlier filter for any class of anomalous source, not only variable stars.

Reading between the lines

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

  • The same procedure could be stress-tested before Rubin begins full survey operations by running it on ZTF data with simulated LSST cadences, measuring how the yield of new variables changes with labelling budget, depth, and field density; the paper does not run this simulation.
  • The overlap between PineForest's findings and those of independent methods (a Signature Anomaly Detection search and a separate Isolation Forest study both flagged some of the same objects) suggests these anomalies are stable features of the data rather than artefacts of one algorithm; a systematic comparison of the outputs of several anomaly-detection methods on the same fields would be a cheap w
  • The six new sources are best treated as variable-star candidates until spectra confirm their classes; a single spectrum of the tentative RS CVn star would anchor the reliability of PineForest for rare stellar classes.
  • With 400 visual inspections per roughly 42,000 light curves, the human review step is the bottleneck; the method could scale to Rubin only if the visual-inspection stage is itself automated or subsampled, which the paper does not address.
Share X Bluesky LinkedIn Reddit HN

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 manuscript reports a targeted anomaly search in ZTF DR23 light curves restricted to four LSSTComCam commissioning fields. Using the SNAD PineForest active anomaly detection workflow, the authors visually inspected 400 candidates and claim the discovery of six previously uncatalogued variable stars, with tentative classifications including solar-type, RS CVn, BY Draconis, and ellipsoidal variables, plus refined periods and classifications for six known variables. The paper frames this as a demonstration that the SNAD pipeline can serve as a template for anomaly hunting in Rubin/LSST-era data.

Significance. If the central claims hold, the paper is a useful proof-of-concept for active anomaly detection in time-domain surveys, and the specific objects are modest but genuine additions to the variable-star census. The authors make several aspects reproducible: the feature-extracted dataset is publicly linked, the PineForest implementation is available in the coniferest library, and all new objects are added to the SNAD Catalog. The overlap between ZTF and LSSTComCam fields is a timely niche, and the comparison with early Rubin commissioning data gives the work a concrete forward-looking context. However, the headline novelty claim rests on a negative catalog cross-match that is not fully documented, and the quantitative light-curve results lack period uncertainties. These issues do not invalidate the apparent variability of the objects, but they do need to be fixed before the discovery claims can be accepted as stated.

major comments (3)
  1. [§4.1 (New variables), first paragraph] The claim that all six objects are 'previously uncatalogued' is load-bearing but is checked against an incomplete and underspecified list of catalogs. The manuscript enumerates Gaia DR3 variability, GCVS, VSX, Mowlavi et al. (2021), and Maíz Apellániz et al. (2023), but omits the ZTF Catalog of Periodic Variable Stars (Chen et al. 2020), which is directly relevant for ZTF-selected periodic variables and is cited later in §4.2.5, as well as ASAS-SN and Catalina periodic-variable catalogs. No cross-match radius, catalog version, or query date is given, so the negative assertion cannot be independently reproduced. I request a systematic cross-match against these omitted catalogs, with explicit match parameters and a statement of catalog versions and access dates; if any of the six objects is present in an omitted catalog, the discovery claim must be revised accordingly.
  2. [§4.1.2–§4.1.6 (periods of new variables)] Periods are quoted to five or six significant figures (e.g., P = 15.2395 days, P = 2.03916 days, P = 7.11703 days) without any uncertainty estimates. The methods paragraph only says that periods come from the highest Lomb-Scargle peak in the SNAD viewer followed by VaST fine-tuning; no procedure for assigning uncertainties, handling aliases, or assessing the frequency resolution is described. Given typical ZTF cadence and season gaps, these precision claims are not credible without error bars. Please provide period uncertainties and describe the alias-checking procedure; this is especially important for the ellipsoidal classification of SNAD274, which depends on a specific period and a folded light-curve shape.
  3. [Abstract and §4.1.5–§4.1.6] The abstract and conclusions state the new objects include 'RS CVn, BY Draconis, ellipsoidal, and solar-type variables' without hedging, while the corresponding sections are explicitly tentative: SNAD278 is 'tentatively classified as an RS CVn-type variable', SNAD279 is interpreted as 'likely caused by stellar spots and rotation', and SNAD274 is described as 'could be a rotating ellipsoidal variable'. This mismatch between the headline classifications and the body text should be reconciled. Either soften the abstract and conclusions to 'candidate' classifications, or provide additional diagnostics (e.g., color-magnitude placement, amplitude ratios, phase coherence, X-ray or spectroscopic checks where available) that raise the confidence of these specific type labels.
minor comments (6)
  1. [Figure 3 caption] The caption reads 'Light curves of eight new ZTF variable stars', but the figure shows six panels and Section 4.1 lists six new objects; this should be corrected to 'six'.
  2. [§2 (Data)] The LSSTComCam footprint extraction uses a percentile-based intensity threshold that is 'manually selected for each image', but no threshold values or uncertainty in the resulting field radii are reported. Please provide the actual thresholds and radii, or release the code/mask so that the sample selection is reproducible.
  3. [§4.1 (New variables)] The acronym for the International Variable Star Index is rendered as 'AAVSO VXS'; it should be 'AAVSO VSX'.
  4. [Figures 3 and 4] The folded light curves do not show photometric error bars, making it difficult to judge the significance of the scatter and the distortion-wave features used for classification. Adding representative error bars or a typical uncertainty value would strengthen the presentation.
  5. [§4.2.5] For object 359205100018012, the discussion of a dwarf-nova interpretation versus a GCAS interpretation would benefit from a quantitative statement of the observed amplitude and the typical amplitudes for each class, rather than only the qualitative claim that the amplitude is 'significantly lower'.
  6. [§4.1.6 (SNAD279)] The membership of SNAD279 in open cluster Theia 1654 is reported without a membership probability or reference epoch; please provide the relevant value from Hunt & Reffert (2024) to support the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the variable-star discoveries are empirical outcomes verified from the photometry itself, not reducible to algorithm inputs or self-citations.

full rationale

The central claim—six previously uncatalogued variable stars—is an empirical result of running PineForest on ZTF DR23 light curves and visually inspecting the candidates. The variability is established from the light-curve photometry and is not an input to the anomaly-detection algorithm. No fitted parameter is renamed as a prediction: periods are measured with the Lomb-Scargle periodogram and refined with VaST, while classifications are inferred from light-curve morphology, Gaia colors, and parallaxes, with explicit hedging for SNAD278 and SNAD279. The SNAD team's own software (light-curve, coniferest, ZTF Viewer) is cited as a tool reference and as evidence of prior successful applications, but these citations do not carry the present discovery claim; the paper invokes no uniqueness theorem, and the definition of a 'new variable' is not given in terms of the anomaly score or feature set. The 'previously uncatalogued' assertion depends on the completeness of the consulted external catalogs, which is a factual correctness risk rather than a circularity: absence from a catalog list is not defined by the pipeline's output. Therefore, no derivation step in the paper reduces to its own input or to a self-citation chain.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard time-series tools (Lomb-Scargle, VaST), on domain assumptions linking photometric morphology to variability classes, on the completeness of the cross-matched catalogs, and on a manually thresholded footprint extraction from a published figure. The periods and classification choices are empirical fits rather than derived constants, and no invented physical entities are introduced.

free parameters (4)
  • New variable periods (Lomb-Scargle/VaST) = P = 15.2395 d (SNAD274), 6.34625 d (SNAD276), 2.03916 d (SNAD278), 7.11703 d (SNAD279)
    Periods are fitted to each light curve and used to fold the data and support the variability-type classification; no uncertainties are reported.
  • LSSTComCam footprint intensity threshold percentile = Manually selected per field
    Section 2: 'the percentile threshold manually selected for each image to optimize region detection'; determines which ZTF objects are counted as inside each field.
  • Visual inspection budget / candidate cutoff = 400 candidates (about 1%)
    The number of anomalies visually inspected was chosen by hand; the results depend on which candidates were inspected.
  • PineForest labeling budget = 50 labels per session, two sessions per field
    Section 3: active learning rounds are limited by a budget chosen by the team; affects anomaly ranking and hence candidates selected for inspection.
assumptions (4)
  • standard math Lomb-Scargle periodogram (Lomb 1976, Scargle 1982) and VaST methods yield reliable periods for variable stars.
    Used in Section 3 to estimate and fine-tune periods; standard time-series tools.
  • domain assumption Photometric light-curve morphology can distinguish RS CVn, BY Dra, ELL, and solar-type variability.
    Used throughout Section 4; classification based on distortion wave, amplitude, folded shapes, without spectroscopy.
  • domain assumption The checked catalogs (Gaia DR3 variability, GCVS, VSX, etc.) capture the prior variable-star literature sufficiently for 'uncatalogued' to be meaningful.
    Section 4.1 statement that the six objects are not listed in these catalogs; completeness is assumed.
  • domain assumption Figure 3 of Guy et al. (2025) faithfully represents LSSTComCam coverage for footprint extraction.
    Section 2 uses this figure with manual thresholding because machine-readable coverage maps are not public.

how reviews work

0 comments
Cite this review

Pith. "Pith review of What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23." pith.science (2026). https://pith.science/paper/OBSQ6MHA

@misc{pith2026250706217,
  author       = {Pith},
  title        = {Pith review of: What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OBSQ6MHA}},
  note         = {Machine review of arXiv:2507.06217}
}
read the original abstract

We present results from the SNAD VIII Workshop, during which we conducted the first systematic anomaly search in the ZTF fields also observed by LSSTComCam during Rubin Scientific Pipeline commissioning. Using the PineForest active anomaly detection algorithm, we analysed four selected fields (two galactic and two extragalactic) and visually inspected 400 candidates. As a result, we discovered six previously uncatalogued variable stars, including RS~CVn, BY Draconis, ellipsoidal, and solar-type variables, and refined classifications and periods for six known objects. These results demonstrate the effectiveness of the SNAD anomaly detection pipeline and provide a preview of the discovery potential in the upcoming LSST data.

Figures

Figures reproduced from arXiv: 2507.06217 by the authors.

Figure 1
Figure 1. Spatial footprint of the four LSSTComCam target fields (circles) overlaid on ZTF sky coverage. Above each circle, the FIELDID of the corresponding overlapping ZTF field is shown. In the legend, the number in parentheses indicates how many ZTF objects fall within the respective LSSTComCam target field. The background grid outlines the ZTF observed fields. Each is colour-coded according to the number of ZTF objects in… view at source ↗
Figure 2
Figure 2. Cumulative imaging depth expressed in terms of the S/N = 5 limiting magnitude for unresolved sources for four LSSTComCam fields. We additionally specify a selected radius and corresponding area for each field [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Light curves of eight new ZTF variable stars discovered in LSSTComCam target fields during the SNAD-VIII Workshop. For periodic variables, folded light curves are shown along with the corresponding periods: (a) 452216100008087 (see Section 4.1.1), (b) 359205200010128 (see Section 4.1.2), (c) 359205200015003 (see Section 4.1.3), (d) 359205200019542 (see Section 4.1.4), (e) 359205300002062 (see Section 4.1.5), (f) 359… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Light curves of catalogued variables for which we refine the classification and/or determine the period: (a) 452216400002679 (see Section 4.2.1), (b) 258207200007316 (see Section 4.2.2), (c) 258208100007880 (see Section 4.2.3), (d) 258208100012201 (see Section 4.2.4), …

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

60 extracted references · 17 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    LSST camera verification testing and characterization

    thebibliography [1] 20pt to REFERENCES 6pt =0pt \@twocolumntrue 12pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key o...

    arXiv 2017

  4. [4]

    Ahumada , R., Allende Prieto , C., Almeida , A., et al. 2020, title The 16th Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra , , 249, 3, 10.3847/1538-4365/ab929e

    doi:10.3847/1538-4365/ab929e 2020

  5. [5]

    D., Malanchev , K

    Aleo , P. D., Malanchev , K. L., Pruzhinskaya , M. V., et al. 2022, title SNAD transient miner: Finding missed transient events in ZTF DR4 using k-D trees , , 96, 101846, 10.1016/j.newast.2022.101846

    work page arXiv 2022

  6. [6]

    P., Tollerud , E

    Astropy Collaboration , Robitaille , T. P., Tollerud , E. J., et al. 2013, title Astropy: A community Python package for astronomy , , 558, A33, 10.1051/0004-6361/201322068

    doi:10.1051/0004-6361/201322068 2013

  7. [7]

    M., Sip o cz , B

    Astropy Collaboration , Price-Whelan , A. M., Sip o cz , B. M., et al. 2018, title The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package , , 156, 123, 10.3847/1538-3881/aabc4f

    doi:10.3847/1538-3881/aabc4f 2018

  8. [8]

    M., Lim , P

    Astropy Collaboration , Price-Whelan , A. M., Lim , P. L., et al. 2022, title The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package , , 935, 167, 10.3847/1538-4357/ac7c74

    doi:10.3847/1538-4357/ac7c74 2022

Show all 60 references
  1. [9]

    Bailer-Jones , C. A. L., Rybizki , J., Fouesneau , M., Demleitner , M., & Andrae , R. 2021, title Estimating Distances from Parallaxes. V. Geometric and Photogeometric Distances to 1.47 Billion Stars in Gaia Early Data Release 3 , , 161, 147, 10.3847/1538-3881/abd806

    2021 doi

  2. [10]

    C., Kulkarni , S

    Bellm , E. C., Kulkarni , S. R., Graham , M. J., et al. 2019, title The Zwicky Transient Facility: System Overview, Performance, and First Results , , 131, 018002, 10.1088/1538-3873/aaecbe

    2019 doi

  3. [11]

    1996, title SExtractor: Software for source extraction

    Bertin , E., & Arnouts , S. 1996, title SExtractor: Software for source extraction. , , 117, 393, 10.1051/aas:1996164

    1996 doi

  4. [12]

    2001, title Random forests, Machine learning, 45, 5

    Breiman, L. 2001, title Random forests, Machine learning, 45, 5

    2001

  5. [13]

    L., Ferguson , P

    Carlin , J. L., Ferguson , P. S., Vivas , A. K., Caplar , N., & Malanchev , K. 2025, title An outer-disk SX Phe variable star in Rubin Data Preview 1 , arXiv e-prints, arXiv:2507.00192, 10.48550/arXiv.2507.00192

    2025 doi arXiv

  6. [14]

    C., Magnier , E

    Chambers , K. C., Magnier , E. A., Metcalfe , N., et al. 2016, title The Pan-STARRS1 Surveys , arXiv e-prints, arXiv:1612.05560, 10.48550/arXiv.1612.05560

    2016 doi arXiv

  7. [15]

    A., Cheung , S.-H., et al

    Chan , H.-S., Villar , V. A., Cheung , S.-H., et al. 2022, title Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars , , 932, 118, 10.3847/1538-4357/ac69d4

    2022 doi

  8. [16]

    2020, title The Zwicky Transient Facility Catalog of Periodic Variable Stars , , 249, 18, 10.3847/1538-4365/ab9cae

    Chen , X., Wang , S., Deng , L., et al. 2020, title The Zwicky Transient Facility Catalog of Periodic Variable Stars , , 249, 18, 10.3847/1538-4365/ab9cae

    2020 doi

  9. [17]

    Choi , Y., Olsen , K. A. G., Carlin , J. L., et al. 2025, title 47 Tuc in Rubin Data Preview 1: Exploring Early LSST Data and Science Potential , arXiv e-prints, arXiv:2507.01343, 10.48550/arXiv.2507.01343

    2025 doi arXiv

  10. [18]

    Deeming , T. J. 1975, title Fourier Analysis with Unequally-Spaced Data , , 36, 137, 10.1007/BF00681947

    1975 doi

  11. [19]

    Duncan , K. J. 2022, title All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8 , , 512, 3662, 10.1093/mnras/stac608

    2022 doi

  12. [20]

    J., Dunham , M

    Evans , II, N. J., Dunham , M. M., J rgensen , J. K., et al. 2009, title The Spitzer c2d Legacy Results: Star-Formation Rates and Efficiencies; Evolution and Lifetimes , , 181, 321, 10.1088/0067-0049/181/2/321

    2009 doi

  13. [21]

    A., Magnier , E

    Flewelling , H. A., Magnier , E. A., Chambers , K. C., et al. 2020, title The Pan-STARRS1 Database and Data Products , , 251, 7, 10.3847/1538-4365/abb82d

    2020 doi

  14. [22]

    2022, VizieR Online Data Catalog: Gaia DR3 Part 4

    Gaia Collaboration . 2022, VizieR Online Data Catalog: Gaia DR3 Part 4. Variability (Gaia Collaboration, 2022) ,, VizieR On-line Data Catalog: I/358. Originally published in: 2023A&A...674A...1G

    2022

  15. [23]

    Gaia Collaboration , Vallenari , A., Brown , A. G. A., et al. 2023, title Gaia Data Release 3. Summary of the content and survey properties , , 674, A1, 10.1051/0004-6361/202243940

    2023 doi

  16. [24]

    Gangler , E., Ishida , E. E. O., Kornilov , M. V., et al. 2025, title Signatures to help interpretability of anomalies , arXiv e-prints, arXiv:2506.16314, 10.48550/arXiv.2506.16314

    2025 doi

  17. [25]

    P., Bechtol, K., Bellm, E., et al

    Guy, L. P., Bechtol, K., Bellm, E., et al. 2025, Rubin Observatory Plans for an Early Science Program, 10.5281/zenodo.15558559

    2025 doi

  18. [26]

    N., Tonry , J

    Heinze , A. N., Tonry , J. L., Denneau , L., et al. 2018, title A First Catalog of Variable Stars Measured by the Asteroid Terrestrial-impact Last Alert System (ATLAS) , , 156, 241, 10.3847/1538-3881/aae47f

    2018 doi

  19. [27]

    B., Marin, J., & Stone, P

    Hunt, E. B., Marin, J., & Stone, P. J. 1966, title Experiments in induction., Academic press

    1966

  20. [28]

    L., & Reffert , S

    Hunt , E. L., & Reffert , S. 2024, VizieR Online Data Catalog: Improving the open cluster census. III. (Hunt+, 2024) ,, VizieR On-line Data Catalog: J/A+A/686/A42. Originally published in: 2024A&A...686A..42H

    2024

  21. [29]

    Hunter , J. D. 2007, title Matplotlib: A 2D Graphics Environment , Computing in Science and Engineering, 9, 90, 10.1109/MCSE.2007.55

    2007 doi

  22. [30]

    S., Stanek , K

    Jayasinghe , T., Kochanek , C. S., Stanek , K. Z., et al. 2018, title The ASAS-SN catalogue of variable stars I: The Serendipitous Survey , , 477, 3145, 10.1093/mnras/sty838

    2018 doi

  23. [31]

    V., Korolev , V

    Kornilov , M. V., Korolev , V. S., Malanchev , K. L., et al. 2025, title Coniferest: A complete active anomaly detection framework , Astronomy and Computing, 52, 100960, 10.1016/j.ascom.2025.100960

    2025

  24. [32]

    J., & Lada , E

    Lada , C. J., & Lada , E. A. 2003, title Embedded Clusters in Molecular Clouds , , 41, 57, 10.1146/annurev.astro.41.011802.094844

    2003 arXiv

  25. [33]

    Lafler , J., & Kinman , T. D. 1965, title An RR Lyrae Star Survey with Ihe Lick 20-INCH Astrograph II. The Calculation of RR Lyrae Periods by Electronic Computer. , , 11, 216, 10.1086/190116

    1965 doi

  26. [34]

    2024, title Integrating the LSST Camera , in Proc

    Lange, T., Nordby, M., Pollek, H., et al. 2024, title Integrating the LSST Camera , in Proc. SPIE Int. Soc. Opt. Eng. , Vol. 13096 (Yokohama, Japan: SPIE ), 130961O, 10.1117/12.3019302

    2024 doi

  27. [35]

    2023, title Performant feature extraction for photometric time series, Memorie della Societ \`a Astronomica Italiana Journal of the Italian Astronomical Society, 102

    Lavrukhina, A., & Malanchev, K. 2023, title Performant feature extraction for photometric time series, Memorie della Societ \`a Astronomica Italiana Journal of the Italian Astronomical Society, 102

    2023

  28. [36]

    T., Ting, K

    Liu, F. T., Ting, K. M., & Zhou, Z.-H. 2008, title Isolation Forest, in 2008 Eighth IEEE International Conference on Data Mining, 413--422, 10.1109/ICDM.2008.17

    2008 doi

  29. [37]

    2014, title Fifty years of classification and regression trees, International Statistical Review, 82, 329

    Loh, W.-Y. 2014, title Fifty years of classification and regression trees, International Statistical Review, 82, 329

    2014

  30. [38]

    Lomb , N. R. 1976, title Least-Squares Frequency Analysis of Unequally Spaced Data , , 39, 447, 10.1007/BF00648343

    1976 doi

  31. [39]

    A., Allison , J., et al

    LSST Science Collaboration , Abell , P. A., Allison , J., et al. 2009, title LSST Science Book, Version 2.0 , arXiv e-prints, arXiv:0912.0201, 10.48550/arXiv.0912.0201

    2009 doi arXiv

  32. [40]

    Ma \' z Apell \'a niz , J., Holgado , G., Pantaleoni Gonz \'a lez , M., & Caballero , J. A. 2023, title Stellar variability in Gaia DR3. I. Three-band photometric dispersions for 145 million sources , , 677, A137, 10.1051/0004-6361/202346759

    2023 doi

  33. [41]

    V., Pruzhinskaya , M

    Malanchev , K., Kornilov , M. V., Pruzhinskaya , M. V., et al. 2023, title The SNAD Viewer: Everything You Want to Know about Your Favorite ZTF Object , , 135, 024503, 10.1088/1538-3873/acb292

    2023 doi

  34. [42]

    2025, title Variability-finding in Rubin Data Preview 1 with LSDB , arXiv e-prints, arXiv:2506.23955, 10.48550/arXiv.2506.23955

    Malanchev , K., DeLucchi , M., Caplar , N., et al. 2025, title Variability-finding in Rubin Data Preview 1 with LSDB , arXiv e-prints, arXiv:2506.23955, 10.48550/arXiv.2506.23955

    2025 doi arXiv

  35. [43]

    L., Pruzhinskaya , M

    Malanchev , K. L., Pruzhinskaya , M. V., Korolev , V. S., et al. 2021, title Anomaly detection in the Zwicky Transient Facility DR3 , , 502, 5147, 10.1093/mnras/stab316

    2021 doi

  36. [44]

    W., et al

    Mowlavi , N., Rimoldini , L., Evans , D. W., et al. 2021, VizieR Online Data Catalog: Large-amplitude variables in Gaia DR2 (Mowlavi+, 2021) ,, VizieR On-line Data Catalog: J/A+A/648/A44. Originally published in: 2021A&A...648A..44M 10.26093/cds/vizier.36480044

    2021 doi

  37. [45]

    Rubin Observatory

    NSF-DOE Vera C. Rubin Observatory . 2025, The Vera C. Rubin Observatory Data Preview 1 , Technical Note RTN-095, Vera C. Rubin Observatory , 10.71929/rubin/2570536

    2025

  38. [46]

    2020, pandas-dev/pandas: Pandas, latest Zenodo, 10.5281/zenodo.3509134

    pandas development team, T. 2020, pandas-dev/pandas: Pandas, latest Zenodo, 10.5281/zenodo.3509134

    2020 doi

  39. [47]

    V., Ishida , E

    Pruzhinskaya , M. V., Ishida , E. E. O., Novinskaya , A. K., et al. 2023, title Supernova search with active learning in ZTF DR3 , , 672, A111, 10.1051/0004-6361/202245172

    2023 doi

  40. [48]

    2024, title LSST camera verification testing and characterization , in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Roodman , A., Rasmussen , A., Bradshaw , A., et al. 2024, title LSST camera verification testing and characterization , in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 13096, Ground-based and Airborne Instrumentation for Astronomy X, ed. J....

    2024 doi

  41. [49]

    N., Kazarovets , E

    Samus' , N. N., Kazarovets , E. V., Durlevich , O. V., Kireeva , N. N., & Pastukhova , E. N. 2017, title General catalogue of variable stars: Version GCVS 5.1 , Astronomy Reports, 61, 80, 10.1134/S1063772917010085

    2017 doi

  42. [50]

    N., Goranskii , V

    Samus' , N. N., Goranskii , V. P., Durlevich , O. V., et al. 2003, title An Electronic Version of the Second Volume of the General Catalogue of Variable Stars with Improved Coordinates , Astronomy Letters, 29, 468, 10.1134/1.1589864

    2003 doi

  43. [51]

    Scargle , J. D. 1982, title Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly spaced data. , , 263, 835, 10.1086/160554

    1982 doi

  44. [52]

    A., Kornilov , M

    Semenikhin , T. A., Kornilov , M. V., Pruzhinskaya , M. V., et al. 2025, title Real-bogus scores for active anomaly detection , Astronomy and Computing, 51, 100919, 10.1016/j.ascom.2024.100919

    2025

  45. [53]

    V., & Lebedev , A

    Sokolovsky , K. V., & Lebedev , A. A. 2018, title VaST: A variability search toolkit , Astronomy and Computing, 22, 28, 10.1016/j.ascom.2017.12.001

    2018 doi

  46. [54]

    V., Volnova , A

    Sreejith , S., Pruzhinskaya , M. V., Volnova , A. A., et al. 2025, title Dataset of artefacts for machine learning applications in astronomy , arXiv e-prints, arXiv:2504.08053, 10.48550/arXiv.2504.08053

    2025 doi arXiv

  47. [55]

    C., & Varoquaux , G

    van der Walt , S., Colbert , S. C., & Varoquaux , G. 2011, title The NumPy Array: A Structure for Efficient Numerical Computation , Computing in Science and Engineering, 13, 22, 10.1109/MCSE.2011.37

    2011 doi

  48. [56]

    A., Aleo, P

    Volnova, A. A., Aleo, P. D., Lavrukhina, A., et al. 2024, title Exploring the Universe with SNAD: Anomaly Detection in Astronomy, in Data Analytics and Management in Data Intensive Domains, ed. J. Baixeries, D. I. Ignatov, S. O. Kuznetsov, & S. Stupnikov (Cham: Springer Nature...

    2024

  49. [57]

    S., Lavrukhina, A

    Voloshina, A. S., Lavrukhina, A. D., Pruzhinskaya, M. V., et al. 2024, title SNAD catalogue of M-dwarf flares from the Zwicky Transient Facility , Monthly Notices of the Royal Astronomical Society, 533, 4309, 10.1093/mnras/stae2031

    2024 doi

  50. [58]

    L., Henden , A

    Watson , C. L., Henden , A. A., & Price , A. 2006, title The International Variable Star Index (VSX) , Society for Astronomical Sciences Annual Symposium, 25, 47

    2006

  51. [59]

    2010, title D ata S tructures for S tatistical C omputing in P ython, in P roceedings of the 9th P ython in S cience C onference, ed

    W es M c K inney. 2010, title D ata S tructures for S tatistical C omputing in P ython, in P roceedings of the 9th P ython in S cience C onference, ed. S t\'efan van der W alt & J arrod M illman, 56 -- 61, 10.25080/Majora-92bf1922-00a

    2010 doi

  52. [60]

    2025, ZTF Objects Table, IPAC, 10.26131/IRSA597

    ZTF Team . 2025, ZTF Objects Table, IPAC, 10.26131/IRSA597

    2025 doi

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.