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arxiv: 2604.07573 · v2 · submitted 2026-04-08 · 🌌 astro-ph.SR

Recognition: unknown

The ZTF-ULTRASAT experiment: Characterizing the non-transients in ULTRASAT's high cadence survey

Daniel Warshofsky , Michael W. Coughlin , Theophile Jegou Du Laz , Anna Y. Q. Ho , S. Bradley Cenko , Andrew Drake , Jesper Sollerman , Argyro Sasli
show 11 more authors
Ben Rusholme Frank J. Masci Roger Smith A.M. Krassilchtchikov David Berge Eran O. Ofek Yossi Shvartzvald Reed L. Riddle Mansi M. Kasliwal Matthew J. Graham Eric C. Bellm
Authors on Pith no claims yet

Pith reviewed 2026-05-10 17:02 UTC · model grok-4.3

classification 🌌 astro-ph.SR
keywords ZTFULTRASATvariable starstransient contaminationRR Lyraehigh-cadence surveymachine learning catalogsultraviolet transients
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The pith

Short-timescale, high-amplitude variable stars can mimic transient alerts in high-cadence ultraviolet surveys.

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

The paper conducted high-cadence observations with the Zwicky Transient Facility in five fields near ULTRASAT's planned northern targets. It identified seven apparent transient candidates, but five turned out to be persistent variables including RR Lyrae stars and flaring sources, with two being spurious. A sympathetic reader would care because this shows a systematic source of contamination for ULTRASAT's transient searches that could be addressed using existing catalogs. Without mitigation, many false positives would reduce the efficiency of identifying true rapid transients. The authors provide a practical strategy to filter these using machine learning classifications from prior surveys.

Core claim

The experiment observed five fields at high cadence over three nights with ZTF, applying a real-time filter that flagged seven transient candidates. Analysis using periods and amplitudes from the Source Classification Project revealed that three were RR Lyrae stars with short periods and high amplitudes, two showed flaring behavior, and two were spurious. This establishes that short-timescale, high-amplitude variables can systematically mimic transient alerts in high-cadence UV surveys, with pre-existing machine learning catalogs offering a concrete mitigation strategy.

What carries the argument

The Source Classification Project machine learning catalogs that supply periods and amplitudes to identify and remove variable star contaminants from transient candidate lists.

Load-bearing premise

Optical variability and contamination rates from ZTF observations are representative of the ultraviolet behavior ULTRASAT will encounter in its high-cadence fields.

What would settle it

Simultaneous or follow-up ultraviolet observations of the same fields revealing substantially different variable contamination rates or types than predicted from the optical ZTF data.

Figures

Figures reproduced from arXiv: 2604.07573 by A.M. Krassilchtchikov, Andrew Drake, Anna Y. Q. Ho, Argyro Sasli, Ben Rusholme, Daniel Warshofsky, David Berge, Eran O. Ofek, Eric C. Bellm, Frank J. Masci, Jesper Sollerman, Mansi M. Kasliwal, Matthew J. Graham, Michael W. Coughlin, Reed L. Riddle, Roger Smith, S. Bradley Cenko, Theophile Jegou Du Laz, Yossi Shvartzvald.

Figure 1
Figure 1. Figure 1: The depicted trasmission for ULTRASAT is the full throughput of the entire optical system. ULTRASAT will observe wavelengths that are impossible to observe with ZTF. 3. ZTF-ULTRASAT EXPERIMENT 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Time Since Experiment Start (days) 18.0 18.5 19.0 19.5 20.0 20.5 21.0 21.5 22.0 22.5 Limiting Magnitude ZTFi ZTFr ZTFg [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: 5σ Limiting magnitude of all the exposures cap￾tured during the experiment duration. A limiting magnitude of 20.5 is typical for ZTF’s normal survey (M. J. Graham et al. 2019). The ZTF-ULTRASAT experiment was conducted June 4th 2024 at 04:00 UTC to June 7th 2024 at 10:15 UTC. The limiting magnitude during the experiment [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ULTRASAT’s north high cadence fields N1,N2 and N3 as blue circles and the ZTF-ULTRASAT experiment fields as the boxes. The ZTF fields are labeled with their field id number. Note that while the N1 and N3 fields over￾lap with the experiment, N2 does not have overlap with the experiment fields. A modified version of the SCoPe (M. W. Coughlin et al. 2021) feature generation pipeline was used to com￾pute perio… view at source ↗
Figure 5
Figure 5. Figure 5: Science image (left), Reference (middle) and Dif￾ference image (right) of ZTF24aaqrjdd. Like ZTF24aaqqtht an additional source (red arrow) in the difference and science images indicates some data quality issues. 4.3. ZTF18aajtlgu SCoPe classified ZTF18aajtlgu as an RR Lyrae; there was no strong classification for which RR Lyrae type. The standard pipeline found a period of 1.533 days. When using only the e… view at source ↗
Figure 6
Figure 6. Figure 6: The full DR16 and alert light curve for ZTF18aajtlgu is shown in the top panel. In the shaded region in the middle panel the experiment data is shown and in the bottom panel the light curve is folded at its period of 0.255 days (bottom). Note that ZTF18aajtlgu is not detectable in the ZTFg band during the minima of its period (phase ≈ 0.8) in the alert data. pipeline. As seen in [PITH_FULL_IMAGE:figures/f… view at source ↗
Figure 7
Figure 7. Figure 7: All classification for ZTF18aajtkma. The top panel shows the phenomenological classifications and the bottom show the ontological classifications. The top half of the circles show the DNN score while the lower half show the XGB scores. Note that while the DNN and XGB models disagree on the CV classification (1.00 vs 0.26), both have great agreement on the iregular classification (0.99 vs 0.97). 5. CONCLUSI… view at source ↗
Figure 8
Figure 8. Figure 8: Full light curve of ZTF18aajtkma (top). The light curve just during the experiment (bottom). In the top panel it is clear that a large spike occurred during the time of the experiment. trophysical events and common variable sources. Lever￾aging catalogs such as SCoPe will be critical for auto￾mated filtering, allowing high-confidence identification of genuine transients while excluding known variable stars… view at source ↗
Figure 9
Figure 9. Figure 9: Full light curve of ZTF18aakfqxu in the 3 ZTF bands (top). The light curve just during the experiment (middle) and the light curve folded at its period of 0.241 days (bottom). Foundation, Minerva, and Israel Council for Higher Ed￾ucation (VATAT). The Gordon and Betty Moore Foundation, through both the Data-Driven Investigator Program and a ded￾icated grant, provided critical funding for SkyPortal. D.E.W. a… view at source ↗
Figure 10
Figure 10. Figure 10: Full light curve of ZTF18aapnpxp in the 3 ZTF bands (top). The light curve just during the experiment (middle) and the light curve folded at its period of 0.490 days (bottom). du Laz, T. J., Coughlin, M. W., Bachant, P., et al. 2025, BOOM and Babamul: a real-time, multi-survey, optical alert broker system operating at scale, https://arxiv.org/abs/2511.00164 Duev, D. A., Mahabal, A., Masci, F. J., et al. 2… view at source ↗
Figure 11
Figure 11. Figure 11: ZTF21aajkbd exhibits two phases in its history. In P1 (middle) there is no periodic variability at SCoPe’s found 1.216 days, but in P2 (bottom) the periodic variability is clear. 200 210 220 230 240 250 260 270 280 RA (deg) 40 45 50 55 60 65 70 75 80 DEC (deg) High amplitude low period sources [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Circles denote the ULTRASAT northern high cadence field, Black outlined regions are the ZTF fields from the experiment, blue points are light curves with periods less than a day and amplitudes greater than 0.8 which would trigger the real time filter used for the experiment. Marocco, F., Eisenhardt, P. R. M., Fowler, J. W., et al. 2021, The Astrophysical Journal Supplement Series, 253, 8, doi: 10.3847/153… view at source ↗
Figure 13
Figure 13. Figure 13: The number and density of light curves classified as flaring changes as the threshold for classification becomes more strict from top to bottom. Sesar, B., Ivezi, ., Stuart, J. S., et al. 2013, The Astronomical Journal, 146, 21, doi: 10.1088/0004-6256/146/2/21 Sesar, B., Hernitschek, N., Mitrovi, S., et al. 2017, The Astronomical Journal, 153, 204, doi: 10.3847/1538-3881/aa661b Shvartzvald, Y., Waxman, E.… view at source ↗
read the original abstract

The forthcoming launch of the Ultraviolet Transient Astronomy Satellite (ULTRASAT) will transform our understanding of the transient ultraviolet sky by increasing our ability to identify transients due to its unprecedented 204 deg2 field of view. While rapid (extragalactic) transients are a priority science area for the mission, flaring stars and AGN can often contaminate searches for such objects. To prepare for these challenges, the Zwicky Transient Facility (ZTF)-ULTRASAT experiment observed five fields at high cadence over three nights, in close proximity to ULTRASAT's three northern high-cadence fields. A real-time filter identified seven transient candidates, of which five were persistent variable sources and two were spurious. Periods and amplitudes derived from the ZTF Source Classification Project (SCoPe) showed that three candidates were RR Lyrae stars with short periods and high amplitudes, while the remaining two displayed flaring behavior. We demonstrate that short-timescale, high-amplitude variables can systematically mimic transient alerts in high-cadence UV surveys, and we provide a concrete strategy to this contamination using pre-existing machine learning catalogs.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper reports results from the ZTF-ULTRASAT experiment, consisting of high-cadence ZTF observations of five fields near ULTRASAT's northern high-cadence regions over three nights. A real-time transient filter flagged seven candidates, of which five were persistent variables (three short-period, high-amplitude RR Lyrae stars and two flaring sources) identified via the SCoPe machine-learning catalog, while two were spurious. The authors conclude that such variables can systematically mimic transient alerts in high-cadence UV surveys and propose mitigation via pre-existing optical ML catalogs.

Significance. If the optical results translate to the UV regime, the work supplies a low-cost, immediately applicable strategy for reducing stellar contamination in ULTRASAT transient searches by cross-matching against existing catalogs, thereby increasing the purity of the mission's extragalactic transient sample.

major comments (2)
  1. [Abstract] Abstract: The claim that the identified variables 'can systematically mimic transient alerts in high-cadence UV surveys' rests on ZTF optical data alone. No scaling relations, amplitude corrections, or simulated light curves accounting for the larger UV amplitudes of RR Lyrae pulsations and flares (typically 2-3x optical) are provided to establish equivalent false-positive rates under ULTRASAT's 200-260 nm bandpass, cadence, and depth.
  2. [Observations] The five observed fields are stated to be 'in close proximity' to ULTRASAT's targets, yet no quantitative comparison of stellar densities, variable-star fractions, or sky coverage statistics is given to demonstrate that these fields adequately sample the target high-cadence regions.
minor comments (2)
  1. [Results] No data table or light-curve parameters (coordinates, exact periods, amplitudes, SCoPe probabilities) are supplied for the seven candidates, preventing independent verification of the classifications and measurements.
  2. [Methods] The manuscript lacks any description of the real-time filter thresholds, photometric precision achieved, or error analysis on the derived periods and amplitudes.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped clarify the scope and limitations of our ZTF-ULTRASAT experiment. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the identified variables 'can systematically mimic transient alerts in high-cadence UV surveys' rests on ZTF optical data alone. No scaling relations, amplitude corrections, or simulated light curves accounting for the larger UV amplitudes of RR Lyrae pulsations and flares (typically 2-3x optical) are provided to establish equivalent false-positive rates under ULTRASAT's 200-260 nm bandpass, cadence, and depth.

    Authors: We agree that the demonstration relies on optical ZTF data as a proxy for ULTRASAT's UV observations. The experiment's goal was to identify real contaminants that trigger high-cadence transient filters and to show that pre-existing ML catalogs (SCoPe) provide an immediate mitigation route. Because ULTRASAT has not yet launched, we lack UV light curves; however, the larger UV amplitudes noted by the referee would increase, rather than decrease, the likelihood of these sources producing alerts. We will revise the abstract and add a short discussion paragraph clarifying the proxy nature of the ZTF data and noting that UV amplitudes are expected to be 2-3 times larger, thereby reinforcing the need for catalog-based filtering. No new simulations are added, as they fall outside the paper's scope of characterizing observed contaminants. revision: partial

  2. Referee: [Observations] The five observed fields are stated to be 'in close proximity' to ULTRASAT's targets, yet no quantitative comparison of stellar densities, variable-star fractions, or sky coverage statistics is given to demonstrate that these fields adequately sample the target high-cadence regions.

    Authors: The fields were selected to overlap with the planned ULTRASAT northern high-cadence footprint using available mission planning information. While the manuscript states 'close proximity,' we did not include explicit statistics. We will add a short paragraph in the Observations section providing quantitative context, such as the stellar density and known variable fraction (from ZTF and Gaia) in the observed fields compared with the broader ULTRASAT target regions, to demonstrate representativeness. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical characterization from new observations and independent catalog

full rationale

The paper reports new ZTF high-cadence observations of five fields, real-time transient filtering yielding seven candidates, and classification of five as persistent variables (RR Lyrae and flares) using periods/amplitudes from the pre-existing independent SCoPe ML catalog. The central claim and mitigation strategy rest on this direct data cross-check rather than any derivation, fit, or self-referential equation. No load-bearing self-citations, ansatzes, or renamings reduce the result to its inputs; the work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The claim depends on the assumption that optical ZTF data serves as a valid proxy for UV contamination rates and that the sampled fields represent the ULTRASAT target areas.

axioms (1)
  • domain assumption Optical variability observed by ZTF accurately predicts contamination behavior in ULTRASAT's ultraviolet bands.
    Invoked when extrapolating ZTF findings to the UV mission without direct UV data.

pith-pipeline@v0.9.0 · 5605 in / 1271 out tokens · 49638 ms · 2026-05-10T17:02:52.462826+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

44 extracted references · 40 canonical work pages · 4 internal anchors

  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]

    , " * 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 ...

  4. [4]

    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...

  5. [5]

    P j*j U 3˶ ysWsv zxm?߮ m8L_|e ŶR<mY=IJz7y< 0' ۳ x1枖ONk Kϯ|^ f <q? @ zGn |mg A

    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...

    work page arXiv 2017

  6. [6]

    2024, title An online framework for fitting fast transient light curves, Monthly Notices of the Royal Astronomical Society, 531, 1084, 10.1093/mnras/stae1164

    Barna, T., Reed, B., Andreoni, I., et al. 2024, title An online framework for fitting fast transient light curves, Monthly Notices of the Royal Astronomical Society, 531, 1084, 10.1093/mnras/stae1164

    work page doi:10.1093/mnras/stae1164 2024

  7. [7]

    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

    work page doi:10.1088/1538-3873/aaecbe 2019

  8. [8]

    The Pan-STARRS1 Surveys

    Chambers, K. C., Magnier, E. A., Metcalfe, N., et al. 2019, The Pan-STARRS1 Surveys, 1612.05560

    work page internal anchor Pith review Pith/arXiv arXiv 2019

  9. [9]

    Chen, T., & Guestrin, C. 2016, title XGBoost: A Scalable Tree Boosting System, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16 (ACM), 785–794, 10.1145/2939672.2939785

    work page doi:10.1145/2939672.2939785 2016

  10. [10]

    , keywords =

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

    work page doi:10.3847/1538-4365/ab9cae 2020

  11. [11]

    W., et al., 2021, @doi [ ] 10.1093/mnras/stab1502 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.2954C 505, 2954

    Coughlin, M. W., Burdge, K., Duev, D. A., et al. 2021, title The ZTF Source Classification Project – II. Periodicity and variability processing metrics, Monthly Notices of the Royal Astronomical Society, 505, 2954, 10.1093/mnras/stab1502

    work page doi:10.1093/mnras/stab1502 2021

  12. [12]

    and Bloom, Joshua S

    Coughlin, M. W., Bloom, J. S., Nir, G., et al. 2023, title A Data Science Platform to Enable Time-domain Astronomy, The Astrophysical Journal Supplement Series, 267, 31, 10.3847/1538-4365/acdee1

    work page doi:10.3847/1538-4365/acdee1 2023

  13. [13]

    , keywords =

    Dekany , R., Smith , R. M., Riddle , R., et al. 2020, title The Zwicky Transient Facility: Observing System , , 132, 038001, 10.1088/1538-3873/ab4ca2

    work page doi:10.1088/1538-3873/ab4ca2 2020

  14. [14]

    BOOM and Babamul: a real-time, multi-survey, optical alert broker system operating at scale

    du Laz, T. J., Coughlin, M. W., Bachant, P., et al. 2025, BOOM and Babamul: a real-time, multi-survey, optical alert broker system operating at scale, 2511.00164

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  15. [15]

    A., Mahabal, A., Masci, F

    Duev, D. A., Mahabal, A., Masci, F. J., et al. 2019, title Real-bogus classification for the Zwicky Transient Facility using deep learning, Monthly Notices of the Royal Astronomical Society, 489, 3582, 10.1093/mnras/stz2357

    work page doi:10.1093/mnras/stz2357 2019

  16. [16]

    A., Magnier, E

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

    work page doi:10.3847/1538-4365/abb82d 2020

  17. [18]

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

    work page doi:10.1051/0004-6361/202243940 2023

  18. [19]

    O., et al

    Ganot , N., Gal-Yam , A., Ofek , E. O., et al. 2016, title The Detection Rate of Early UV Emission from Supernovae: A Dedicated Galex/PTF Survey and Calibrated Theoretical Estimates , , 820, 57, 10.3847/0004-637X/820/1/57

    work page doi:10.3847/0004-637x/820/1/57 2016

  19. [20]

    O., Gal-Yam , A., et al

    Ganot , N., Ofek , E. O., Gal-Yam , A., et al. 2022, title The GALEX-PTF Experiment. II. Supernova Progenitor Radius and Energetics via Shock-cooling Modeling , , 931, 71, 10.3847/1538-4357/ac67ef

    work page doi:10.3847/1538-4357/ac67ef 2022

  20. [21]

    , keywords =

    Graham, M. J., Drake, A. J., Djorgovski, S. G., Mahabal, A. A., & Donalek, C. 2013, title Using conditional entropy to identify periodicity, Monthly Notices of the Royal Astronomical Society, 434, 2629, 10.1093/mnras/stt1206

    work page doi:10.1093/mnras/stt1206 2013

  21. [22]

    , keywords =

    Graham, M. J., Kulkarni, S. R., Bellm, E. C., et al. 2019, title The Zwicky Transient Facility: Science Objectives, Publications of the Astronomical Society of the Pacific, 131, 078001, 10.1088/1538-3873/ab006c

    work page doi:10.1088/1538-3873/ab006c 2019

  22. [23]

    F., Coughlin, M

    Healy, B. F., Coughlin, M. W., Mahabal, A. A., et al. 2024, The ZTF Source Classification Project: III. A Catalog of Variable Sources, 2312.00143

    work page arXiv 2024

  23. [24]

    arXiv e-prints , year =

    Junell, A., Sasli, A., Nunes, F. F., et al. 2025, Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure, 2507.16088

    work page arXiv 2025

  24. [25]

    Lomb, N. R. 1976, title Least-squares frequency analysis of unequally spaced data, Astrophys Space Sci, 39, 447, https://doi.org/10.1007/BF00648343

    work page doi:10.1007/bf00648343 1976

  25. [26]

    2017, title Deep-learnt classification of light curves, in 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE), 1–8, 10.1109/ssci.2017.8280984

    Mahabal, A., Sheth, K., Gieseke, F., et al. 2017, title Deep-learnt classification of light curves, in 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE), 1–8, 10.1109/ssci.2017.8280984

    work page doi:10.1109/ssci.2017.8280984 2017

  26. [27]

    , archivePrefix = "arXiv", eprint =

    Mainzer , A., Bauer , J., Grav , T., et al. 2011, title Preliminary Results from NEOWISE: An Enhancement to the Wide-field Infrared Survey Explorer for Solar System Science , , 731, 53, 10.1088/0004-637X/731/1/53

    work page doi:10.1088/0004-637x/731/1/53 2011

  27. [28]

    Marocco, F., Eisenhardt, P. R. M., Fowler, J. W., et al. 2021, title The CatWISE2020 Catalog, The Astrophysical Journal Supplement Series, 253, 8, 10.3847/1538-4365/abd805

    work page doi:10.3847/1538-4365/abd805 2021

  28. [29]

    Publications of the Astronomical Society of the Pacific , author =

    Masci , F. J., Laher , R. R., Rusholme , B., et al. 2019, title The Zwicky Transient Facility: Data Processing, Products, and Archive , , 131, 018003, 10.1088/1538-3873/aae8ac

    work page internal anchor Pith review doi:10.1088/1538-3873/aae8ac 2019

  29. [30]

    M., Wehlau , A., & Mendes de Oliveira , C

    Nemec , J. M., Wehlau , A., & Mendes de Oliveira , C. 1988, title Variable Stars in the Ursa Minor Dwarf Galaxy , , 96, 528, 10.1086/114830

    work page doi:10.1086/114830 1988

  30. [31]

    T., Bellm , E

    Patterson , M. T., Bellm , E. C., Rusholme , B., et al. 2019, title The Zwicky Transient Facility Alert Distribution System , , 131, 018001, 10.1088/1538-3873/aae904

    work page doi:10.1088/1538-3873/aae904 2019

  31. [32]

    A., Coughlin , M

    Rehemtulla , N., Miller , A. A., Coughlin , M. W., & Jegou du Laz , T. 2023, title BTSbot : A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility , arXiv e-prints, arXiv:2307.07618, 10.48550/arXiv.2307.07618

    work page doi:10.48550/arxiv.2307.07618 2023

  32. [33]

    A., Laz, T

    Rehemtulla, N., Miller, A. A., Laz, T. J. D., et al. 2024, The Zwicky Transient Facility Bright Transient Survey. III. BTSbot : Automated Identification and Follow-up of Bright Transients with Deep Learning, 2401.15167

    work page arXiv 2024

  33. [34]

    2025, A Census of NUV M-Dwarf Flares Using Archival GALEX Data and the gPhoton2 Pipeline, 2306.17045

    Rekhi, P., Ben-Ami, S., Perdelwitz, V., & Shvartzvald, Y. 2025, A Census of NUV M-Dwarf Flares Using Archival GALEX Data and the gPhoton2 Pipeline, 2306.17045

    work page arXiv 2025

  34. [35]

    Scargle, J. D. 1982, title STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. II. STATISTICAL ASPECTS OF SPECTRAL ANALYSIS OF UNEVENLY SPACED DATA, The Astrophysical Journal, 263, 835, 10.1086/160554

    work page doi:10.1086/160554 1982

  35. [36]

    1998, title Period search in large datasets, Open Astronomy, 7, 43, doi:10.1515/astro-1998-0109

    Schwarzenberg-Czerny, A. 1998, title Period search in large datasets, Open Astronomy, 7, 43, doi:10.1515/astro-1998-0109

    work page doi:10.1515/astro-1998-0109 1998

  36. [37]

    S., et al

    Sesar, B., Ivezić, ., Stuart, J. S., et al. 2013, title EXPLORING THE VARIABLE SKY WITH LINEAR. II. HALO STRUCTURE AND SUBSTRUCTURE TRACED BY RR LYRAE STARS TO 30 kpc, The Astronomical Journal, 146, 21, 10.1088/0004-6256/146/2/21

    work page doi:10.1088/0004-6256/146/2/21 2013

  37. [38]

    2017, title Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample, The Astronomical Journal, 153, 204, 10.3847/1538-3881/aa661b

    Sesar, B., Hernitschek, N., Mitrović, S., et al. 2017, title Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample, The Astronomical Journal, 153, 204, 10.3847/1538-3881/aa661b

    work page doi:10.3847/1538-3881/aa661b 2017

  38. [39]

    2024, title ULTRASAT: A Wide-field Time-domain UV Space Telescope , , 964, 74, 10.3847/1538-4357/ad2704

    Shvartzvald , Y., Waxman , E., Gal-Yam , A., et al. 2024, title ULTRASAT: A Wide-field Time-domain UV Space Telescope , , 964, 74, 10.3847/1538-4357/ad2704

    work page doi:10.3847/1538-4357/ad2704 2024

  39. [40]

    F., Ag \"u eros , M., et al

    Szkody , P., Anderson , S. F., Ag \"u eros , M., et al. 2002, title Cataclysmic Variables from The Sloan Digital Sky Survey. I. The First Results , , 123, 430, 10.1086/324734

    work page doi:10.1086/324734 2002

  40. [42]

    2019 , pages =

    van der Walt, S. J., Crellin-Quick, A., & Bloom, J. S. 2019 b , title SkyPortal: An Astronomical Data Platform, Journal of Open Source Software, 4, 1247, 10.21105/joss.01247

    work page doi:10.21105/joss.01247 2019

  41. [43]

    A., Mahabal , A

    van Roestel , J., Duev , D. A., Mahabal , A. A., et al. 2021, title The ZTF Source Classification Project. I. Methods and Infrastructure , , 161, 267, 10.3847/1538-3881/abe853

    work page doi:10.3847/1538-3881/abe853 2021

  42. [44]

    L., Eisenhardt, P

    Wright , E. L., Eisenhardt , P. R. M., Mainzer , A. K., et al. 2010, title The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance , , 140, 1868, 10.1088/0004-6256/140/6/1868

    work page internal anchor Pith review doi:10.1088/0004-6256/140/6/1868 2010

  43. [45]

    Yann LeCun, Yoshua Bengio, G. H. 2015, title Deep learning, Nature, 512, 436, 10.1038/nature14539

    work page doi:10.1038/nature14539 2015

  44. [46]

    O., & Gal-Yam , A

    Zackay , B., Ofek , E. O., & Gal-Yam , A. 2016, title Proper Image Subtraction Optimal Transient Detection, Photometry, and Hypothesis Testing , , 830, 27, 10.3847/0004-637X/830/1/27

    work page doi:10.3847/0004-637x/830/1/27 2016