Pith. sign in

REVIEW 4 major objections 4 minor 71 references

Life in the Slow Lane: A Search for Long Term Variability in ASAS-SN

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read The paper reports that a decade-long automated sky survey of nine million bright stars contains 782 systems changing by more than 0.03 magnitudes per year, 433 of which are newly identified variables.

desk verdict A genuinely useful first catalog of slow variables in ASAS-SN, with an honest but unquantified contamination risk; deserves refereeing. read the letter →

arxiv 2501.14058 v1 pith:DNSKKJHS submitted 2025-01-23 astro-ph.SR astro-ph.GA

Sydney Petz , Christopher S. Kochanek This is my paper · ORCID
classification astro-ph.SRastro-ph.GA
keywords long-termstellarvariabilityslowvariablestarsdecade-scaletime-domainphotometryactivitycyclescircumstellardustsemi-regularvariablescolor-magnitudediagramphotometriccalibration
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 asks whether a ten-year baseline of wide-field automated sky-survey photometry can systematically uncover slow stellar variability, a regime transient surveys usually ignore. It establishes that, among about nine million isolated stars in a narrow bright-sky magnitude range, 782 show a steady brightness trend of more than roughly 0.03 magnitudes per year; 433 of these are new variables and 349 were previously classified under standard classes that do not capture this slow behavior. The sample separates into five behavioral groups, with the largest being red subgiants and lower main-sequence stars, and about 70 percent also show shorter-period periodic variability. If true, this means slowly varying stars are common, have distinct physical drivers such as activity cycles, pulsation, and dust, and can be found systematically in decade-long photometry.

What carries the argument

The load-bearing procedure is the construction of decade-long seasonal-median light curves. Each source's camera and filter data are intercalibrated using a damped random-walk Gaussian process, then median-averaged per observing season to remove short-timescale variability, and then fit with linear and quadratic functions of time. The linear slope and the quadratic fit's maximum magnitude excursion select candidates; four classes of false positives are removed, namely bright-star artifacts, south-pole field rotation, high proper motion, and failed intercalibration between two filter bands. This machinery converts raw survey photometry into a slow-variability rate and a maximum magnitude change, and the equal counts of brightening and fading sources serve as the internal check that the trends are not a systematic drift.

What would settle it

A direct test would feed thousands of light curves of stars known to be constant through the same intercalibration, seasonal-median binning, and slope-selection pipeline; if a comparable fraction of those stable stars are flagged at more than 0.03 magnitudes per year, the catalog's trends are substantially contaminated by systematics.

Watch

Extended reading notes

Core claim

The paper's central claim is that a systematic, false-positive-controlled search of 9,361,613 isolated sources with $13 < g < 14.5$ mag in a ten-year time-domain survey yields 782 genuine slowly variable systems with slopes exceeding roughly $0.03$ mag/yr. It argues these trends are astrophysical rather than instrumental, citing the near balance of brightening and fading sources and the fact that known variable classes such as semi-regular, slow irregular, and spotted stars appear only at the high-amplitude tail of their class distributions. The candidates occupy distinct regions of the observed color–magnitude diagram and are split into main-sequence, subgiant/giant, AGB, luminous blue, and nova-like groups. The paper also shows that 551 candidates are periodic on shorter timescales, mostly longer than 10 days, and that 191 are plausibly linked to circumstellar dust through infrared excess or optical-versus-infrared slope behavior.

Load-bearing premise

The premise is that a 0.03-magnitude-per-year drift in the processed decade-long light curve is a real change in the star rather than a small residual calibration artifact, and the paper's checks for this are only a count showing roughly equal numbers of brightening and fading sources plus visual review of each light curve.

Editorial extensions

If this is right

  • A ten-year survey of a single magnitude range already yields hundreds of slowly varying stars, so the slowly varying sky is not rare.
  • Standard variable-star taxonomies miss this regime: most new systems have no clean standard class, and known SR/L/ROT variables appear as the extreme high-amplitude tail of their populations.
  • About 70 percent of slow variables also vary periodically on shorter timescales, meaning long-term trends and rotation or pulsation commonly coexist.
  • Roughly 191 candidates show optical and infrared changes consistent with dust formation or destruction, identifying a substantial dust-linked subset.
  • The five behavioral groups imply distinct physical drivers: magnetic activity for lower main-sequence stars, spot activity for subgiants, pulsation for AGB stars, eruptive behavior for blue stars, and mass transfer for nova-like systems.

Reading between the lines

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

  • The paper does not run a control sample of known constant stars, so I infer the absolute size of the catalog is not yet pinned down; an injection-recovery experiment would convert the brightening-versus-fading balance into a direct false-positive rate and a completeness estimate.
  • I infer the same intercalibration-plus-seasonal-median method transfers directly to other decade-long surveys, so running this selection on fainter sources or independent fields would test whether the subgiant/giant dominance is universal or magnitude-dependent.
  • The 191 dust-related candidates are the most promising subset for follow-up spectroscopy: measuring temperature and luminosity at high and low states could separate temperature-driven variability from true obscuration by circumstellar dust.
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

4 major / 4 minor

Summary. The manuscript presents a search for long-term (decade-scale) photometric variability in 9,361,613 ASAS-SN sources with 13<g<14.5 mag. Using linear and quadratic fits to seasonal median light curves after intercalibrating cameras and filters, the authors select candidates with slopes ≳0.03 mag/yr and Δg>0.3 mag, then reject false positives via bright-star proximity, south-pole cuts, proper-motion cuts, and visual inspection. They report 782 candidates, 433 of them newly identified variables, cross-match to Gaia, SIMBAD, AAVSO, and WISE, classify the sources into five CMD groups, and analyze periodicities and mid-IR dust indicators. The central claim is that this is the first systematic catalog of slow variability in this magnitude range.

Significance. If the candidate list is robust, this is a valuable resource: it is the first systematic census of slow variability in ASAS-SN at these magnitudes, it identifies a substantial population of previously unclassified variables, and it links them to astrophysical groups (RS CVn, AGB, Be stars, AGN). The paper's strengths include a clearly specified selection pipeline, extensive external cross-matching, a full electronic table, and public availability of the light curves. The main weakness is that the false-positive rate is not quantified: no control sample or injection-recovery test is presented, and the final acceptance step is subjective visual inspection. The equal brightening/fading balance is suggestive but not a substitute for a systematics estimate.

major comments (4)
  1. [Section 2] The reduction from 36,705 initial candidates to 782 after false-positive rejection is not accompanied by any estimate of the false-positive rate. The statement that the roughly equal numbers of brightening (395) and fading (387) sources imply the sample is 'not affected by systematic drifts' is insufficient, because time-dependent zero-point drifts, color-dependent intercalibration residuals between V and g, and spatially localized systematics can all produce both brightening and fading artifacts. I request a control sample of known stable stars or injection-recovery tests to estimate the false-positive rate; without this, the quoted 782 sources (and 433 new variables) are upper bounds rather than a measured catalog size.
  2. [Section 2, intercalibration] The intercalibration fits constant camera/filter offsets and then seasonal medians are computed. If a star's color evolves slowly or the seasonal mix of V and g observations changes over the decade, the combined seasonal medians can acquire a spurious long-term slope. The paper does not demonstrate that residual systematics are below 0.03 mag/yr on decade timescales. Please quantify the stability of the photometric system (for example, by measuring the scatter of known stable stars or by comparing with external photometry) and show that the reported slopes are significant relative to this noise floor.
  3. [Table 1] Table 1 reports optical slopes, Δg, periods, and W1−W2 colors without uncertainties. Because the selection thresholds (0.03 mag/yr and 0.3 mag) are defined relative to these quantities, the absence of error bars prevents the reader from assessing whether individual candidates are genuinely above threshold. Please provide uncertainties, or at least the photometric noise floor for the slopes and periods, so that the significance of individual entries can be judged.
  4. [Section 4] The final paragraph's caveat that slower changes 'will require significant improvements in false positive rejection' is appropriate, but it also underscores that the rejection power for the 0.03 mag/yr threshold itself is not demonstrated. Given that the central claim is the existence and classification of 782 genuine slow variables, the paper should either provide a quantitative false-positive estimate for the adopted threshold or soften the claim accordingly.
minor comments (4)
  1. [Section 2] The Lomb-Scargle period search uses a false alarm probability threshold of 0.1, which is quite loose; with 551 reported periodic variables, many periods may be spurious. Please state the expected number of false positives at this threshold or justify the choice.
  2. [Figure 5 caption] The caption contains a grammatical error: 'The format is the same is in Fig. 11' should read 'The format is the same as in Fig. 11'.
  3. [Section 3] The text 'Roughly, 10 percent of the sources are listed as YSOs (11) or T Tauri stars (5)' includes an unnecessary comma; the sentence would read more cleanly as 'Roughly 10 percent of the sources...'.
  4. [Section 2] The paper states that periods longer than the average observing season are discarded, but it does not define how the average observing season length is computed. Please clarify this definition.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the 782-source slow-variability catalog is selected directly from ASAS-SN photometry and external classifications; no fitted parameter or self-citation is load-bearing.

full rationale

The paper's central claim is an observational catalog, not a derivation from a model. Candidates are selected by fixed thresholds on the linear slope (>0.03 mag/yr) and quadratic Delta-g (>0.3 mag) of seasonal medians, after intercalibrating cameras and filters using nuisance offset parameters. The final 782 are obtained after geometric cuts (nearby bright stars, south pole, high proper motion) and visual inspection; none of these steps fits a parameter to the reported 782 or to the 433/349 split. The 'new' versus 'previously classified' split is determined by cross-matching to external catalogs (Gaia Alerts, AAVSO VSX, SIMBAD, milliquas), not by the authors' own prior results. The periodicity analysis uses standard Lomb-Scargle after detrending with a false-alarm probability threshold, and the dust-variability flags are thresholded classifications of ASAS-SN and NEOWISE light curves. No equation in Section 2 or 3 reduces to an input fitted to the target claim. The paper's own caveat in Section 4 that 'Searches for still slower changes than ~0.03 mag/year will require significant improvements in false positive rejection and/or longer light curves' is a limitation statement, not evidence that the current claim is derived from itself. The brightening/fading balance check (395 vs 387) is a sanity check, not a circular argument. Self-citations are to ASAS-SN data releases and prior catalog papers used as external benchmarks; they are not invoked to force the selection. Hence no significant circularity.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

This is an observational catalog paper, so there are no invented physical entities. The free parameters are the selection thresholds and cuts chosen by hand. The axioms are domain assumptions about photometric stability, external catalog reliability, isochrone validity, and WISE data quality.

free parameters (7)
  • Minimum linear slope = 0.03 mag/yr
    Chosen to define slow variability; sources below this cut are excluded. Affects sample size and false positive rate.
  • Maximum magnitude change (Delta g) = 0.3 mag
    Used with quadratic fits to capture non-monotonic trends; roughly matches the slope cut over 10 years.
  • Proper motion cutoff = 100 mas/yr
    Removes sources whose motion through the ASAS-SN aperture creates false trends; also removes nearby stars with flux ratio >0.01.
  • Bright star rejection curve = quadratic curve to 3600 arcsec
    Defined by eye from Figure 2 to separate artifacts caused by bright stars; ad hoc.
  • South pole declination cutoff = -88 degrees
    Removes field-rotation artifacts near the celestial south pole.
  • Lomb-Scargle false alarm probability threshold = 0.1
    Loose threshold for claiming periodicity; approximately 10% of non-periodic variables could be false positives.
  • Mid-IR excess cutoff = W1-W2 > 0.3 mag
    Used to flag circumstellar dust candidates; excludes W1<9 mag saturated sources.
assumptions (5)
  • domain assumption After intercalibration and seasonal median binning, ASAS-SN photometry is stable to better than about 0.03 mag/yr over a decade.
    The selection uses slopes of seasonal medians; if residual zero-point drifts exist, the candidate list would be contaminated. The paper checks only the balance of brightening vs fading sources (Section 2).
  • domain assumption The visual inspection reliably rejects remaining artifacts and noise.
    The final 782 candidates depend on subjective human classification of light curves and images (Section 2).
  • domain assumption External catalogs (Gaia DR3, AAVSO, SIMBAD, milliquas, Bailer-Jones distances) are accurate enough for cross-matching and classification.
    Used to classify candidates and assign CMD groups and AGN status (Section 2).
  • domain assumption MIST solar-metallicity isochrones provide a valid reference for grouping stars on the CMD.
    Groups are defined relative to 1 and 10 Gyr solar metallicity tracks (Figure 3).
  • domain assumption WISE light curves processed with Hwang & Zakamska (2020) are reliable for mid-IR variability.
    Dust variability flags depend on W1-W2 slopes and colors (Section 3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Life in the Slow Lane: A Search for Long Term Variability in ASAS-SN." pith.science (2026). https://pith.science/paper/DNSKKJHS

@misc{pith2026250114058,
  author       = {Pith},
  title        = {Pith review of: Life in the Slow Lane: A Search for Long Term Variability in ASAS-SN},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DNSKKJHS}},
  note         = {Machine review of arXiv:2501.14058}
}
read the original abstract

We search a sample of 9,361,613 isolated sources with 13<g<14.5 mag for slowly varying sources. We select sources with brightness changes larger than ~ 0.03 mag/year over 10 years, removing false positives due to, for example, nearby bright stars or high proper motions. After a thorough visual inspection, we find 782 slowly varying systems. Of these systems, 433 are identified as variables for the first time and 349 are previously classified as variables. Previously classified systems were mostly identified as semi-regular variables (SR), slow irregular variables (L), spotted stars (ROT), or unknown (MISC or VAR), as long time scale variability does not fit into a standard class. The stellar sources are scattered across the CMD and can be placed into 5 groups that exhibit distinct behaviors. The largest groups are very red subgiants and lower main sequence stars. There are also a small number of AGN. There are 551 candidates (~70 percent) that also show shorter time scale periodic variability, mostly with periods longer than 10 days. The variability of 191 of these candidates may be related to dust.

Figures

Figures reproduced from arXiv: 2501.14058 by the authors.

Figure 1
Figure 1. Distributions of the input sources (solid) in linear slope (left) and maximum magnitude change Δ𝑔 in the quadratic fit (right). We considered sources to the right of the red lines with slopes or Δ𝑔/10 year > 0.03 mag/year. The dashed histograms show the distributions of the final sample after eliminating false positives. 10 8 6 4 2 0 Bright Star Magnitude 102 103 Distance from Target (arcsec) Good Candidates False P… view at source ↗
Figure 2
Figure 2. Distribution of the initial candidates in the magnitude and distance of bright nearby stars. We keep stars above the red curve. tude dimming event over a period of more than 6.5 years (Tzanidakis et al. 2023). Though not the only dimming event observed over such a long interval (e.g., Rowan et al. 2021; Smith et al. 2021; Torres & Sakano 2022), it is one of the deepest and longest known events. Although proposed to … view at source ↗
Figure 3
Figure 3. The Gaia DR3 𝑀𝐺 and 𝐵𝑃 − 𝑅𝑃 color-magnitude diagram of the final candidates divided into groups based on the dashed lines. The curves are solar metallicity 1 and 10 Gyr MIST isochrones. One type of light curve is shown in each group. absolute linear slopes greater than 0.03 mag/year in the space of the distance and magnitude of nearby bright stars. We identify two bands, with the lower band consisting of false posit… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: The Gaia DR3 𝑀𝐺 and 𝐵𝑃 − 𝑅𝑃 color-magnitude diagram of all variables in SkyPatrol V2.0 with 13<g<14.5 that are classified as SR, L, and ROT (small points) as compared to the candidates with these classifications (large points). Previously unclassified candidates are ma…
Figure 5
Figure 5. Figure 5: Slope distribution of the candidates (red solid) and all variables (black dashed) in SkyPatrol V2.0 with 13<g<14.5 that are classified as SR (left), L (middle), and ROT (right). 0.5 1.0 1.5 2.0 2.5 3.0 Log Period 0 10 20 30 40 50 60 70 Number Main sequence Subgiants/Gi…
Figure 6
Figure 6. Figure 6: Period distribution for the candidates by group. with 13 < 𝑔 < 14.5 mag in a Gaia DR3 𝑀𝐺 and 𝐵𝑃 − 𝑅𝑃 color magnitude diagram and [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Phased light curves of two candidates displaying regular periodicity, labeled by their 2MASS ID. The top panel shows the light curve and the trend polynomial subtracted before estimating the period. The bottom panel shows the phased, detrended light curve. Left: A subg…
Figure 8
Figure 8. Figure 8: Period distribution of the candidates (red solid) and all variables (black dashed) in SkyPatrol V2.0 with 13<g<14.5 that are classified as SR (left), L (middle), and ROT (right). −0.03 −0.02 −0.01 0.00 0.01 0.02 0.03 W1-W2 Slope −0.10 −0.05 0.00 0.05 0.10 0.15 0.20 Opt…
Figure 9
Figure 9. Figure 9: Left: Optical variability slope as a function of the W1−W2 color variability slope. Three stars are outside the edge of the figure, where 2 had bad WISE light curves and the third is the Nova V0339 Del (see [PITH_FULL_IMAGE:figures/full_fig_p006_9.png]
Figure 10
Figure 10. Figure 10: Comparisons of ASAS-SN and WISE light curves of candidates labeled by their 2MASS ID and CMD group. The top panel shows a candidate which clearly shows variability due to dust formation. The middle panel shows an example with very similar optical and mid-IR variabilit…
Figure 11
Figure 11. Figure 11: Example light curves for the main sequence group (top) and subgiant/giant group (bottom) labeled by their 2MASS ID. The left (right) panels show the 3 dimming (brightening) sources with the largest (top), median (middle), and smallest (bottom) slopes in the sample. Th…
Figure 12
Figure 12. Figure 12: Example light curves for the AGB group (top) and luminous blue group (bottom). The format is the same is in [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]
Figure 13
Figure 13. Figure 13: Example light curves for the novae group (top) and AGN (bottom). The source in the top left novae panel, V0339 Del, is simply a fading classical nova. The format is the same is in [PITH_FULL_IMAGE:figures/full_fig_p010_13.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

71 extracted references · 16 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION fin.entry write newline FUNCTION new.block output.state before.all = 'skip after.block 'output.state := if FUNCTION new.sentence output.state after.block = 'skip output.state before.all = 'skip after.sentence 'output.state := if if FUNCTION not #0 #1 if FUNCTION and 'skip pop #0 if FUNCTION or pop #1...

  3. [3]

    Acero F., et al., 2015, @doi [ ] 10.1088/0067-0049/218/2/23 , https://ui.adsabs.harvard.edu/abs/2015ApJS..218...23A 218, 23

    doi:10.1088/0067-0049/218/2/23 2015

  4. [4]

    J., Erasmus N., Jones D., Mogawana O., 2022, @doi [ ] 10.1093/mnras/stac2685 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1884A 517, 1884

    Addison H., Blagorodnova N., Groot P. J., Erasmus N., Jones D., Mogawana O., 2022, @doi [ ] 10.1093/mnras/stac2685 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.517.1884A 517, 1884

    work page doi:10.1093/mnras/stac2685 2022

  5. [5]

    Bailer-Jones C. A. L., 2023, @doi [ ] 10.3847/1538-3881/ad08bb , https://ui.adsabs.harvard.edu/abs/2023AJ....166..269B 166, 269

    doi:10.3847/1538-3881/ad08bb 2023

  6. [6]

    Baliunas S., Jastrow R., 1990, @doi [ ] 10.1038/348520a0 , https://ui.adsabs.harvard.edu/abs/1990Natur.348..520B 348, 520

    doi:10.1038/348520a0 1990

  7. [7]

    R., Graham M

    Bellm E., 2014, in Wozniak P. R., Graham M. J., Mahabal A. A., Seaman R., eds, The Third Hot-wiring the Transient Universe Workshop. pp 27--33 ( @eprint arXiv 1410.8185 ), @doi 10.48550/arXiv.1410.8185

    arXiv doi:10.48550/arxiv.1410.8185 2014

  8. [8]

    Blagorodnova N., et al., 2020, @doi [ ] 10.1093/mnras/staa1872 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.496.5503B 496, 5503

    doi:10.1093/mnras/staa1872 2020

Show all 71 references
  1. [9]

    M., Schlafly E

    Bovy J., Rix H.-W., Green G. M., Schlafly E. F., Finkbeiner D. P., 2016, @doi [ ] 10.3847/0004-637X/818/2/130 , https://ui.adsabs.harvard.edu/abs/2016ApJ...818..130B 818, 130

    2016 doi

  2. [10]

    S., et al., 2016, @doi [ ] 10.1093/mnras/stw218 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.457.3988B 457, 3988

    Boyajian T. S., et al., 2016, @doi [ ] 10.1093/mnras/stw218 , https://ui.adsabs.harvard.edu/abs/2016MNRAS.457.3988B 457, 3988

    2016 doi

  3. [11]

    Chen X., Wang S., Deng L., de Grijs R., Yang M., 2018, @doi [ ] 10.3847/1538-4365/aad32b , https://ui.adsabs.harvard.edu/abs/2018ApJS..237...28C 237, 28

    2018 doi

  4. [12]

    Chen X., Wang S., Deng L., de Grijs R., Yang M., Tian H., 2020, @doi [ ] 10.3847/1538-4365/ab9cae , https://ui.adsabs.harvard.edu/abs/2020ApJS..249...18C 249, 18

    2020 doi

  5. [13]

    T., et al., 2023, @doi [ ] 10.1093/mnras/stac3801 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.5271C 519, 5271

    Christy C. T., et al., 2023, @doi [ ] 10.1093/mnras/stac3801 , https://ui.adsabs.harvard.edu/abs/2023MNRAS.519.5271C 519, 5271

    2023 doi

  6. [14]

    D., Henry T

    Clements T. D., Henry T. J., Hosey A. D., Jao W.-C., Silverstein M. L., Winters J. G., Dieterich S. B., Riedel A. R., 2017, @doi [ ] 10.3847/1538-3881/aa8464 , https://ui.adsabs.harvard.edu/abs/2017AJ....154..124C 154, 124

    2017 doi

  7. [15]

    Drimmel R., Cabrera-Lavers A., L \'o pez-Corredoira M., 2003, @doi [ ] 10.1051/0004-6361:20031070 , https://ui.adsabs.harvard.edu/abs/2003A&A...409..205D 409, 205

    2003 doi

  8. [16]

    W., et al., 2000, @doi [ ] 10.1086/301349 , https://ui.adsabs.harvard.edu/abs/2000AJ....119.2360D 119, 2360

    Duerbeck H. W., et al., 2000, @doi [ ] 10.1086/301349 , https://ui.adsabs.harvard.edu/abs/2000AJ....119.2360D 119, 2360

    2000 doi

  9. [17]

    W., 2023, @doi [The Open Journal of Astrophysics] 10.21105/astro.2308.01505 , https://ui.adsabs.harvard.edu/abs/2023OJAp....6E..49F 6, 49

    Flesch E. W., 2023, @doi [The Open Journal of Astrophysics] 10.21105/astro.2308.01505 , https://ui.adsabs.harvard.edu/abs/2023OJAp....6E..49F 6, 49

    2023 arXiv

  10. [18]

    Foukal P., 2017, @doi [ ] 10.3847/2041-8213/aa740f , https://ui.adsabs.harvard.edu/abs/2017ApJ...842L...3F 842, L3

    2017 doi

  11. [19]

    Gaia Collaboration et al., 2023, @doi [ ] 10.1051/0004-6361/202243940 , https://ui.adsabs.harvard.edu/abs/2023A&A...674A...1G 674, A1

    2023 doi

  12. [20]

    R., Kochanek C

    Gerke J. R., Kochanek C. S., Stanek K. Z., 2015, @doi [ ] 10.1093/mnras/stv776 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.3289G 450, 3289

    2015 doi

  13. [21]

    N., Melnikov S

    Grankin K. N., Melnikov S. Y., Bouvier J., Herbst W., Shevchenko V. S., 2007, @doi [ ] 10.1051/0004-6361:20065489 , https://ui.adsabs.harvard.edu/abs/2007A&A...461..183G 461, 183

    2007 doi

  14. [22]

    M., Schlafly E., Zucker C., Speagle J

    Green G. M., Schlafly E., Zucker C., Speagle J. S., Finkbeiner D., 2019, @doi [ ] 10.3847/1538-4357/ab5362 , https://ui.adsabs.harvard.edu/abs/2019ApJ...887...93G 887, 93

    2019 doi

  15. [23]

    M., et al., 2023, @doi [ ] 10.1088/1538-3873/acdb9a , https://ui.adsabs.harvard.edu/abs/2023PASP..135j5002H 135, 105002

    Hambleton K. M., et al., 2023, @doi [ ] 10.1088/1538-3873/acdb9a , https://ui.adsabs.harvard.edu/abs/2023PASP..135j5002H 135, 105002

    2023 doi

  16. [24]

    arXiv:2304.03791

    Hart K., et al., 2023, @doi [arXiv e-prints] 10.48550/arXiv.2304.03791 , https://ui.adsabs.harvard.edu/abs/2023arXiv230403791H p. arXiv:2304.03791

    2023 doi arXiv

  17. [25]

    N., et al., 2018, @doi [ ] 10.3847/1538-3881/aae47f , https://ui.adsabs.harvard.edu/abs/2018AJ....156..241H 156, 241

    Heinze A. N., et al., 2018, @doi [ ] 10.3847/1538-3881/aae47f , https://ui.adsabs.harvard.edu/abs/2018AJ....156..241H 156, 241

    2018 doi

  18. [26]

    M., Winn J

    Herbst W., LeDuc K., Hamilton C. M., Winn J. N., Ibrahimov M., Mundt R., Johns-Krull C. M., 2010, @doi [ ] 10.1088/0004-6256/140/6/2025 , https://ui.adsabs.harvard.edu/abs/2010AJ....140.2025H 140, 2025

    2010 doi

  19. [27]

    Heydari-Malayeri M., 1990, , https://ui.adsabs.harvard.edu/abs/1990A&A...234..233H 234, 233

    1990

  20. [28]

    T., et al., 2021, @doi [ ] 10.1051/0004-6361/202140735 , https://ui.adsabs.harvard.edu/abs/2021A&A...652A..76H 652, A76

    Hodgkin S. T., et al., 2021, @doi [ ] 10.1051/0004-6361/202140735 , https://ui.adsabs.harvard.edu/abs/2021A&A...652A..76H 652, A76

    2021 doi

  21. [29]

    D., Henry T

    Hosey A. D., Henry T. J., Jao W.-C., Dieterich S. B., Winters J. G., Lurie J. C., Riedel A. R., Subasavage J. P., 2015, @doi [ ] 10.1088/0004-6256/150/1/6 , https://ui.adsabs.harvard.edu/abs/2015AJ....150....6H 150, 6

    2015 doi

  22. [30]

    L., 2020, @doi [ ] 10.1093/mnras/staa400 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.2271H 493, 2271

    Hwang H.-C., Zakamska N. L., 2020, @doi [ ] 10.1093/mnras/staa400 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.2271H 493, 2271

    2020 doi

  23. [31]

    J., Munari U., Marang F., 1993, , https://ui.adsabs.harvard.edu/abs/1993A&A...277..510I 277, 510

    Ivison R. J., Munari U., Marang F., 1993, , https://ui.adsabs.harvard.edu/abs/1993A&A...277..510I 277, 510

    1993

  24. [32]

    Jayasinghe T., et al., 2018, @doi [ ] 10.1093/mnras/sty838 , https://ui.adsabs.harvard.edu/abs/2018MNRAS.477.3145J 477, 3145

    2018 doi

  25. [33]

    Jayasinghe T., et al., 2020, @doi [ ] 10.1093/mnras/stz2711 , https://ui.adsabs.harvard.edu/abs/2020MNRAS.491...13J 491, 13

    2020 doi

  26. [34]

    Jayasinghe T., et al., 2021, @doi [ ] 10.1093/mnras/stab114 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.503..200J 503, 200

    2021 doi

  27. [35]

    Kato T., Ishioka R., Uemura M., 2002, @doi [ ] 10.1093/pasj/54.6.1033 , https://ui.adsabs.harvard.edu/abs/2002PASJ...54.1033K 54, 1033

    2002 doi

  28. [36]

    S., Beacom J

    Kochanek C. S., Beacom J. F., Kistler M. D., Prieto J. L., Stanek K. Z., Thompson T. A., Y \"u ksel H., 2008, @doi [ ] 10.1086/590053 , https://ui.adsabs.harvard.edu/abs/2008ApJ...684.1336K 684, 1336

    2008 doi

  29. [37]

    S., Adams S

    Kochanek C. S., Adams S. M., Belczynski K., 2014, @doi [ ] 10.1093/mnras/stu1226 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.443.1319K 443, 1319

    2014 doi

  30. [38]

    S., et al., 2017, @doi [ ] 10.1088/1538-3873/aa80d9 , https://ui.adsabs.harvard.edu/abs/2017PASP..129j4502K 129, 104502

    Kochanek C. S., et al., 2017, @doi [ ] 10.1088/1538-3873/aa80d9 , https://ui.adsabs.harvard.edu/abs/2017PASP..129j4502K 129, 104502

    2017 doi

  31. [39]

    Koz owski S., et al., 2010, @doi [ ] 10.1088/0004-637X/708/2/927 , https://ui.adsabs.harvard.edu/abs/2010ApJ...708..927K 708, 927

    2010 doi

  32. [40]

    P., 1967, @doi [ ] 10.1086/149359 , https://ui.adsabs.harvard.edu/abs/1967ApJ...150..551K 150, 551

    Kraft R. P., 1967, @doi [ ] 10.1086/149359 , https://ui.adsabs.harvard.edu/abs/1967ApJ...150..551K 150, 551

    1967 doi

  33. [41]

    R., 1976, @doi [ ] 10.1007/BF00648343 , https://ui.adsabs.harvard.edu/abs/1976Ap&SS..39..447L 39, 447

    Lomb N. R., 1976, @doi [ ] 10.1007/BF00648343 , https://ui.adsabs.harvard.edu/abs/1976Ap&SS..39..447L 39, 447

    1976 doi

  34. [42]

    Mainzer A., et al., 2014, @doi [ ] 10.1088/0004-637X/792/1/30 , https://ui.adsabs.harvard.edu/abs/2014ApJ...792...30M 792, 30

    2014 doi

  35. [43]

    J., Robin A

    Marshall D. J., Robin A. C., Reyl \'e C., Schultheis M., Picaud S., 2006, @doi [ ] 10.1051/0004-6361:20053842 , https://ui.adsabs.harvard.edu/abs/2006A&A...453..635M 453, 635

    2006 doi

  36. [44]

    D., 2022, @doi [ ] 10.3847/1538-4357/ac6269 , https://ui.adsabs.harvard.edu/abs/2022ApJ...938....5M 938, 5

    Matsumoto T., Metzger B. D., 2022, @doi [ ] 10.3847/1538-4357/ac6269 , https://ui.adsabs.harvard.edu/abs/2022ApJ...938....5M 938, 5

    2022 doi

  37. [45]

    D., Pejcha O., 2017, @doi [ ] 10.1093/mnras/stx1768 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.3200M 471, 3200

    Metzger B. D., Pejcha O., 2017, @doi [ ] 10.1093/mnras/stx1768 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.471.3200M 471, 3200

    2017 doi

  38. [46]

    D., Shen K

    Metzger B. D., Shen K. J., Stone N., 2017, @doi [ ] 10.1093/mnras/stx823 , https://ui.adsabs.harvard.edu/abs/2017MNRAS.468.4399M 468, 4399

    2017 doi

  39. [47]

    A., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6ba7 , https://ui.adsabs.harvard.edu/abs/2017ApJ...840....1M 840, 1

    Molnar L. A., et al., 2017, @doi [ ] 10.3847/1538-4357/aa6ba7 , https://ui.adsabs.harvard.edu/abs/2017ApJ...840....1M 840, 1

    2017 doi

  40. [48]

    T., Simon J

    Montet B. T., Simon J. D., 2016, @doi [ ] 10.3847/2041-8205/830/2/L39 , https://ui.adsabs.harvard.edu/abs/2016ApJ...830L..39M 830, L39

    2016 doi

  41. [49]

    B., Robin A., Venot O., eds, SF2A-2018: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics

    Neiner C., 2018, in Di Matteo P., Billebaud F., Herpin F., Lagarde N., Marquette J. B., Robin A., Venot O., eds, SF2A-2018: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics. p. Di ( @eprint arXiv 1811.05261 ), @doi 10.48550/arXiv.1811.05261

    2018 doi arXiv

  42. [50]

    Neustadt J. M. M., Kochanek C. S., Stanek K. Z., Basinger C., Jayasinghe T., Garling C. T., Adams S. M., Gerke J., 2021, @doi [ ] 10.1093/mnras/stab2605 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508..516N 508, 516

    2021 doi

  43. [51]

    G., Kriskovics L., Vida K., 2014, @doi [ ] 10.1051/0004-6361/201424695 , https://ui.adsabs.harvard.edu/abs/2014A&A...572A..94O 572, A94

    Ol \'a h K., Mo \'o r A., K o v \'a ri Z., Granzer T., Strassmeier K. G., Kriskovics L., Vida K., 2014, @doi [ ] 10.1051/0004-6361/201424695 , https://ui.adsabs.harvard.edu/abs/2014A&A...572A..94O 572, A94

    2014 doi

  44. [52]

    Paiano S., Landoni M., Falomo R., Treves A., Scarpa R., Righi C., 2017, @doi [ ] 10.3847/1538-4357/837/2/144 , https://ui.adsabs.harvard.edu/abs/2017ApJ...837..144P 837, 144

    2017 doi

  45. [53]

    Paxton B., et al., 2018, @doi [ ] 10.3847/1538-4365/aaa5a8 , https://ui.adsabs.harvard.edu/abs/2018ApJS..234...34P 234, 34

    2018 doi

  46. [54]

    J., Hartmann L., 1978, @doi [ ] 10.1086/156363 , https://ui.adsabs.harvard.edu/abs/1978ApJ...224..182P 224, 182

    Phillips M. J., Hartmann L., 1978, @doi [ ] 10.1086/156363 , https://ui.adsabs.harvard.edu/abs/1978ApJ...224..182P 224, 182

    1978 doi

  47. [55]

    S., Jayasinghe T., Cao L., Christy C

    Phillips A., Kochanek C. S., Jayasinghe T., Cao L., Christy C. T., Rowan D. M., Pinsonneault M., 2024, @doi [ ] 10.1093/mnras/stad3564 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.5588P 527, 5588

    2024 doi

  48. [56]

    M., et al., 2021, @doi [Research Notes of the American Astronomical Society] 10.3847/2515-5172/ac0c83 , https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..147R 5, 147

    Rowan D. M., et al., 2021, @doi [Research Notes of the American Astronomical Society] 10.3847/2515-5172/ac0c83 , https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..147R 5, 147

    2021 doi

  49. [57]

    D., 1982, @doi [ ] 10.1086/160554 , https://ui.adsabs.harvard.edu/abs/1982ApJ...263..835S 263, 835

    Scargle J. D., 1982, @doi [ ] 10.1086/160554 , https://ui.adsabs.harvard.edu/abs/1982ApJ...263..835S 263, 835

    1982 doi

  50. [58]

    J., et al., 2014, @doi [ ] 10.1088/0004-637X/788/1/48 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788...48S 788, 48

    Shappee B. J., et al., 2014, @doi [ ] 10.1088/0004-637X/788/1/48 , https://ui.adsabs.harvard.edu/abs/2014ApJ...788...48S 788, 48

    2014 doi

  51. [59]

    N., Skoda P., Rutsch P., 2013, The Astronomer's Telegram, https://ui.adsabs.harvard.edu/abs/2013ATel.5282....1S 5282, 1

    Shore S. N., Skoda P., Rutsch P., 2013, The Astronomer's Telegram, https://ui.adsabs.harvard.edu/abs/2013ATel.5282....1S 5282, 1

    2013

  52. [60]

    D., Shappee B

    Simon J. D., Shappee B. J., Pojma \'n ski G., Montet B. T., Kochanek C. S., van Saders J., Holoien T. W. S., Henden A. A., 2018, @doi [ ] 10.3847/1538-4357/aaa0c1 , https://ui.adsabs.harvard.edu/abs/2018ApJ...853...77S 853, 77

    2018 doi

  53. [61]

    C., et al., 2021, @doi [ ] 10.1093/mnras/stab1211 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.1992S 505, 1992

    Smith L. C., et al., 2021, @doi [ ] 10.1093/mnras/stab1211 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.505.1992S 505, 1992

    2021 doi

  54. [62]

    Tang S., Grindlay J., Los E., Laycock S., 2010, @doi [ ] 10.1088/2041-8205/710/1/L77 , https://ui.adsabs.harvard.edu/abs/2010ApJ...710L..77T 710, L77

    2010 doi

  55. [63]

    Teixeira G. D. C., et al., 2018, @doi [ ] 10.1051/0004-6361/201833667 , https://ui.adsabs.harvard.edu/abs/2018A&A...619A..41T 619, A41

    2018 doi

  56. [64]

    Torres G., Sakano K., 2022, @doi [ ] 10.1093/mnras/stac2322 , https://ui.adsabs.harvard.edu/abs/2022MNRAS.516.2514T 516, 2514

    2022 doi

  57. [65]

    Tylenda R., et al., 2011, @doi [ ] 10.1051/0004-6361/201016221 , https://ui.adsabs.harvard.edu/abs/2011A&A...528A.114T 528, A114

    2011 doi

  58. [66]

    Tzanidakis A., Davenport J. R. A., Bellm E. C., Wang Y., 2023, @doi [ ] 10.3847/1538-4357/aceda7 , https://ui.adsabs.harvard.edu/abs/2023ApJ...955...69T 955, 69

    2023 doi

  59. [67]

    Cambridge Astrophysics Series Vol

    Warner B., 1995, Cataclysmic variable stars . Cambridge Astrophysics Series Vol. 28, Cambridge University Press

    1995

  60. [68]

    L., Henden A

    Watson C. L., Henden A. A., Price A., 2006, Society for Astronomical Sciences Annual Symposium, https://ui.adsabs.harvard.edu/abs/2006SASS...25...47W 25, 47

    2006

  61. [69]

    W., 1994, @doi [ ] 10.1086/116925 , https://ui.adsabs.harvard.edu/abs/1994AJ....107.1135W 107, 1135

    Weis E. W., 1994, @doi [ ] 10.1086/116925 , https://ui.adsabs.harvard.edu/abs/1994AJ....107.1135W 107, 1135

    1994 doi

  62. [70]

    Wenger M., et al., 2000, @doi [ ] 10.1051/aas:2000332 , https://ui.adsabs.harvard.edu/abs/2000A&AS..143....9W 143, 9

    2000 doi

  63. [71]

    S., 2024, @doi [ ] 10.3847/1538-4357/ad5a0b , https://ui.adsabs.harvard.edu/abs/2024ApJ...971...61W 971, 61

    Winecki D., Kochanek C. S., 2024, @doi [ ] 10.3847/1538-4357/ad5a0b , https://ui.adsabs.harvard.edu/abs/2024ApJ...971...61W 971, 61

    2024 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.