Pith. sign in

REVIEW 2 major objections 7 minor 125 references

Variability of massive stars in M31 from the Palomar Transient Factory

T0 review · 2 major / 7 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read This paper characterizes photometric variability of ~491 massive stars in M31, finding variability widespread and rising toward late spectral types, near 100% for M supergiants, with cool supergiants on longer timescales than hotter stars.

desk verdict A competent, honest observational census of massive-star variability in M31 whose headline t_ch map is real but has uncertainties that are not quantified. read the letter →

arxiv 1908.02439 v2 pith:SMDDOBGM submitted 2019-08-07 astro-ph.SR astro-ph.GA

Monika D. Soraisam , Lars Bildsten , Maria R. Drout , Thomas A. Prince , Thomas Kupfer , Frank Masci , Russ R. Laher , Shrinivas R. Kulkarni This is my paper · ORCID
classification astro-ph.SRastro-ph.GA
keywords massivestarsM31stellarvariabilitysupergiantswavelettransformGaussianprocessPalomarTransientFactory
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

This paper establishes the first comprehensive photometric variability census of roughly 491 spectroscopically confirmed massive stars in the Andromeda galaxy (M31) using five years of R-band data from the Palomar Transient Factory survey. It shows that variability is widespread across the upper color-magnitude diagram, with the fraction of variable stars rising toward later spectral types and reaching near 100% for M-type supergiants. Redder stars ($V-I > 1.5$) display larger amplitude fluctuations than bluer ones. By reconstructing unevenly sampled light curves with a Gaussian Process and applying a wavelet transform, the paper extracts a characteristic variability timescale $t_{\rm ch}$ for each star: cool supergiants typically vary on timescales longer than 100 days, whereas hotter stars have timescales of tens of days, with some extending down to the resolution limit of a few days. A 60-night high-cadence block uncovers 13 stars with significant variability on 0.1–10 day timescales, matching short-timescale variability seen in space-based data.

What carries the argument

The load-bearing tool is a continuous wavelet transform with a Morlet wavelet applied to light curves that have been gap-filled by a Gaussian Process reconstruction using a critical filter, an approach imported from an earlier study of red supergiants. The wavelet transform maps correlation power as a function of both timescale and time, allowing a characteristic timescale $t_{\rm ch}$ to be extracted for signals that are periodic, stochastic, or transient. The reconstruction is limited to a maximum resolution of about 3 days, so reported long-baseline timescales are only trusted when $t_{\rm ch}$ exceeds 10 days. The transform normalization ($1/a$) makes the square root of the correlation power equal to the signal amplitude in flux units, which is then compared with empirically measured noise floors to reject timescales consistent with photometric noise. For short timescales, a 60-night block with resolution of about 30 minutes is analyzed separately.

What would settle it

Take a few of the 356 stars with measured $t_{\rm ch} \geq 10$ days and compare their reconstructed wavelet spectra against well-sampled continuous light curves from space-based photometry (for example, TESS sectors or K2 campaigns covering similar or longer baselines): if the independently measured periodograms or wavelet spectra lack the timescales claimed by the Gaussian Process reconstruction, the reported $t_{\rm ch}$ values would fail to represent the true variability.

Watch

Extended reading notes

Core claim

The central claim is that massive stars in M31 are almost universally variable, and that the pattern of their variability—its prevalence, amplitude, and characteristic timescale—is organized by spectral type in a way that current stellar models can broadly reproduce. From a spectroscopically typed sample of 491 stars, the paper finds that the observed variability fraction climbs from early spectral types to nearly 100% for M supergiants. The RMS amplitude is systematically larger for red evolved stars than for blue ones. The wavelet-derived characteristic timescale $t_{\rm ch}$ separates the population: cool supergiants sit at hundreds of days or more, while hotter stars cluster around tens of days but extend to both longer and shorter values. For luminous blue variables, the combination of short characteristic timescales and few-percent amplitudes is interpreted as stochastic microvariability driven by envelope convection, in line with recent three-dimensional hydrodynamical simulations.

Load-bearing premise

The entire timescale analysis rests on the assumption that the Gaussian Process reconstruction faithfully preserves real variability power on timescales above about 10 days, because if the reconstruction smooths away genuine short-timescale signal or introduces spurious correlated power, the reported characteristic timescales and their spectral-type trends would be artifacts.

Editorial extensions

If this is right

  • Variability in M31's massive-star population is common and becomes nearly universal among cool supergiants, so any complete model of late-stage massive-star evolution must account for such pervasive photometric variation.
  • The trend of larger amplitudes in red evolved stars on timescales of hundreds of days supports pulsation (likely fundamental and first-overtone modes) as a dominant driver in cool supergiants.
  • The wavelet timescales of the hotter massive stars, often tens of days, are consistent with a mixture of opacity-driven pulsation, rotational modulation, and possibly stochastic low-frequency variability from internal gravity waves.
  • The detection of 13 stars with 0.1–10 day variability at a few percent amplitude shows that ground-based surveys can find short-timescale variability in the brightest massive stars, providing a bridge to space-based studies with TESS.

Reading between the lines

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

  • If the Gaussian Process reconstruction tends to smooth over low-amplitude, short-timescale fluctuations, the true frequency of short-timescale variables in M31 could be higher than the 13 stars reported here; a targeted TESS-like campaign on the brightest M31 supergiants could test this.
  • The same wavelet-plus-Gaussian-Process pipeline could be applied to other Local Group galaxies with comparable time-domain coverage, allowing a first direct comparison of massive-star variability across metallicity environments.
  • The two stars dropped for showing sub-resolution variability hint that the population of very short-timescale variables is partially hidden by the 3-day reconstruction limit; a higher-resolution reconstruction of those specific light curves could verify whether their true timescales fall in the 1–3 day range.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 7 minor

Summary. This paper presents a photometric variability study of 1050 spectroscopically classified massive stars in M31 using roughly five years of iPTF R-band forced photometry. After applying a magnitude cut and a noise-floor-based variability selection, the authors retain 491 variables, and through a Gaussian-process reconstruction of the unevenly sampled light curves plus a Morlet wavelet transform they derive characteristic variability timescales t_ch for 356 stars on timescales above 10 days. The three headline results are: (1) the detected variability fraction increases from early to late spectral types, approaching 100% for M supergiants; (2) redder stars (V-I > 1.5) show larger RMS amplitudes than bluer stars; and (3) cool supergiants have longer t_ch (typically hundreds of days) than hotter stars (typically tens of days), with an additional high-cadence analysis of a 60-night block yielding short timescales of 0.1-10 days for 13 stars.

Significance. If the t_ch map survives scrutiny, this is the first comprehensive characterization of massive-star variability in M31 across all spectral types, and the first wavelet-based characteristic-timescale map in the upper CMD of an external galaxy. The study extends Paper I's RSG analysis to the full massive-star population, provides a machine-readable catalog (Table 2) and publicly hosted light curves on DataLab, uses binomial confidence intervals on the variability fractions, gives a validation appendix for the wavelet extraction on simulated signals, and carries out visual inspection of all reconstructed light curves. The agreement with the M51 results of Conroy et al. (2018) on the variability-fraction and amplitude maps lends external support to those claims. The central t_ch claim, however, rests entirely on the fidelity of the Gaussian-process reconstruction on the real iPTF sampling; the validation gap described below is the main barrier to accepting the reported timescale trends at face value.

major comments (2)
  1. [§3.2, Appendix C] The Gaussian-Process reconstruction that underpins every t_ch measurement is validated in Appendix C only against simulated light curves with a uniform 2.5-day cadence, a gap-free baseline, high signal-to-noise, and signals that are exactly Morlet wavelets, whereas the actual iPTF data (Section 2.2) have irregular cadence and multi-month seasonal gaps over the five-year baseline. Because the critical filter infers both the signal and its power spectrum, the smoothness prior could bridge seasonal gaps with spurious long-timescale correlated power, and the two sources dropped in Section 3.2 (J004026.84+403504.6 and J004509.86+413031.5) show that genuine variability can be lost when it falls near the 3-day resolution. Since the cool-versus-hot t_ch trend in Figs. 9-10 is the paper's central novel claim, I ask that the recovery tests be redone on the actual iPTF time grid (or a representative gap-structured grid) with injected signals spanning the 10-1200 day range, including sines and red noise at the measured noise levels, and that the recovered t_ch be reported as a function of input timescale, spectral type, and signal-to-noise.
  2. [§3.2, Table 2, Figs. 9-11] The t_ch values in Table 2 and the distributions in Fig. 10 are reported without uncertainties, yet the method contains three sources of measurement error: the 5-sigma island-detection threshold, the automated island finder's fragmentation or merging of connected power regions, and the amplitude-versus-noise filter based on the Fig. 3 curve. The fragmentation is visible already in Table 2, where J003953.55+402827.7 is assigned 1130 and 1131 days as separate timescales. A modest change in the threshold, the wavelet scale grid, or the GP resolution could split or merge islands and move a star between the 'tens of days' and 'hundreds of days' regimes on which the spectral-type comparison in Fig. 10 rests; I ask for a robustness analysis (e.g., threshold variation or GP-posterior-based uncertainties) so that the trend claims carry a quantitative statement of precision.
minor comments (7)
  1. [Fig. 6, §4.2.3] The right-panel axis label in Fig. 6 reads 'Varaibility fraction' and should read 'Variability fraction'; the same misspelling ('varaibility') appears in Section 4.2.3 in 'Characterization of observed photometric varaibility in LBVs based on a large sample size'.
  2. [§3.2, Table 2] The machine-readable table should include the t_ch-specific amplitude (defined in Section 3.2 as alpha P^{1/2}) alongside each t_ch, since the text notes that some rows contain multiple t_ch values arising from fragmentation of a single connected island; this would allow readers to apply their own amplitude-versus-noise filter.
  3. [§3.2] The sentence stating that the two sources were 'vetted out' is ambiguous; 'dropped from the long-baseline t_ch analysis and re-examined in the high-cadence block' would be more precise, since Section 4.1 later includes them among the 13 short-timescale stars.
  4. [§3.3, Fig. 3] The high-cadence noise threshold is set to a constant 100 DN, while Fig. 3 shows that the noise estimates vary with timescale near the 60-day baseline; a short statement of the resulting systematic uncertainty in the short-t_ch detections for the 13 stars would strengthen the claim.
  5. [Appendix C] The calibration factor alpha = 1.7 x pixel/2.5 days should be explicitly identified as the conversion applied to the real light curves when computing t_ch-specific amplitudes in Section 3.2, as the text currently introduces it only as a scaling factor.
  6. [§4.2.1, Fig. 6] The variability-fraction map would be more informative with completeness-corrected fractions or at least the number of non-variable stars per spectral-type bin, since the early-type fractions are lower limits set by the Fig. 1 noise floor; the existing caveat in the text is helpful, but the figure itself does not convey this.
  7. [§3.1, Fig. 1] The functional form of the orange noise line, which defines the variability selection in Section 3.1 and is reused in Fig. 3, is not given in the text or in a table; quoting it would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the variability fractions, amplitudes, and tch maps are observational characterizations calibrated against external noise estimates and simulation tests, not predictions reduced to fitted inputs.

full rationale

The paper is an observational characterization of iPTF light curves rather than a derivation in which an output is constructed from its own input. Variability selection (Sect. 3.1) uses the Paper I noise floor ('The orange line shows the noise level of PTF from Paper I, which was computed considering only stars without PTF-detectable variability and with ⟨mR⟩ < 20 and extrapolated to fainter stars'), but this is a published, externally determined calibration curve, not a parameter fitted to the current sample's target claims; stars are then classified by whether their measured RMS exceeds that floor. The tch analysis (Sect. 3.2) reconstructs light curves with the critical filter, applies a Morlet wavelet transform, identifies islands above a 5σ background threshold, and filters against noise estimates from static stars; the amplitude calibration constant α is set using simulated wavelet signals in Appendix C, independent of the actual stellar light curves. The central claims (variability fraction increasing toward later types, redder stars having larger RMS amplitudes, cool supergiants having longer tch) are direct summaries of the resulting measurements and are benchmarked against independent work (Conroy et al. 2018, Dorn-Wallenstein et al. 2019) and external theoretical predictions. The only self-citations are to Paper I for the noise curve and for having previously implemented the reconstruction; these do not encode the present results. The statement that LBVs have a variability fraction near 100% is explicitly definitional ('LBVs by definition are variables') but is not load-bearing for the paper's main conclusions. Potential limitations of the Gaussian-process reconstruction for uneven sampling are correctness and robustness concerns, not circularity, because no target quantity was fitted and then re-predicted.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The paper's central claims depend on the MNS16 spectral catalog, the adopted distance and reddening to M31, a noise model inherited from Paper I, and the fidelity of the Gaussian Process reconstruction used for wavelet timescale extraction. The free parameters are analysis thresholds and a simulation-derived amplitude calibration constant; none of these are fitted to the astrophysical data to produce a 'prediction', which keeps the circularity burden moderate.

free parameters (4)
  • Wavelet amplitude calibration factor = 1.7 x (pixel size / 2.5 days)
    Derived in Appendix C by matching wavelet transform power to simulated signal RMS amplitudes; converts wavelet power to physical RMS amplitude for t_ch. This calibration directly affects the reported t_ch-specific amplitudes (coefficient of variation) in Fig. 11 and the short-timescale amplitude values in Fig. 5.
  • Wavelet power threshold = 5 sigma above background (background = mean of pixels below 75th percentile)
    Adopted in Section 3.2 to define islands of significant wavelet power. The authors state this threshold 'appears optimal' after experiments in Appendix C. It determines which t_ch values are reported and can fragment or merge islands.
  • High-cadence noise threshold = 100 DN (constant)
    Used in Section 3.3 to filter t_ch < 10 days in the high-cadence block, based on approximately constant noise at short timescales.
  • GP reconstruction resolution = ~3 days (long baseline), ~30 minutes (high-cadence block)
    Maximum resolution of the Gaussian Process reconstruction, chosen due to computational limitations (Section 3.2). Sets the lower bound of 10 days for long-baseline t_ch and affects which short-timescale variability can be recovered.
assumptions (6)
  • domain assumption The adopted distance modulus to M31 is 24.36 mag (Vilardell et al. 2010).
    Used to convert apparent to absolute magnitudes for the CMDs in Figures 6, 7, 9, and 10 (Section 4.2.1). If wrong, the CMD positions shift, though the qualitative spectral-type trends would persist.
  • domain assumption The foreground reddening toward M31 is E(B-V) = 0.062 mag (Schlegel et al. 1998), with no correction for internal M31 extinction.
    Applied to colors and magnitudes in Section 4.2.1. Internal extinction is not accounted for, which could affect colors of individual stars, especially in the disk.
  • domain assumption Spectral types and M31 membership flags from Massey et al. (2016, MNS16) are correct.
    The entire sample definition rests on this catalog (Section 2.1). Misclassification or foreground contamination would bias the variability statistics per spectral type.
  • domain assumption The forced photometry on iPTF difference images, calibrated via template-image PSF photometry with a 2-inch search radius, yields accurate R-band fluxes for the sample stars.
    All measured amplitudes and variability fractions depend on the photometric accuracy of the iPTF forced photometry pipeline (Section 2.2). Blending between close pairs (16 stars with separation under 5 inches) is acknowledged but not corrected.
  • domain assumption The photometric noise model (orange line in Fig. 1) from Paper I, computed from stars deemed non-variable in that earlier study, is applicable to the M31 sample and its extrapolation to fainter magnitudes is valid.
    Used in Section 3.1 to separate variable from non-variable stars and in Section 3.2 to filter t_ch amplitudes. If the noise model is underestimated, spurious variability and t_ch values would be reported, particularly for faint stars.
  • domain assumption The Gaussian Process reconstruction with the critical filter (Oppermann et al. 2013) preserves the true variability power at timescales above 10 days, neither smoothing away real signal nor creating spurious power.
    This is the load-bearing premise of the t_ch analysis (Section 3.2). The reconstructed light curve is the input to the wavelet transform; if the GP introduces or removes power, the reported t_ch values are artifacts. Validation is provided on simulated signals (Appendix C) but not on the actual unevenly sampled data.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Variability of massive stars in M31 from the Palomar Transient Factory." pith.science (2026). https://pith.science/paper/SMDDOBGM

@misc{pith2026190802439,
  author       = {Pith},
  title        = {Pith review of: Variability of massive stars in M31 from the Palomar Transient Factory},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SMDDOBGM}},
  note         = {Machine review of arXiv:1908.02439}
}
read the original abstract

Using data from the (intermediate) Palomar Transient Factory (iPTF), we characterize the time variability of ~500 massive stars in M31. Our sample is those stars which are spectrally typed by Massey and collaborators, including Luminous Blue Variables, Wolf-Rayets, and warm and cool supergiants. We use the high-cadence, long-baseline (~5 years) data from the iPTF survey, coupled with data-processing tools that model complex features in the light curves. We find widespread photometric (R-band) variability in the upper Hertzsprung Russell diagram (or CMD) with an increasing prevalence of variability with later spectral types. Red stars (V-I>1.5) exhibit larger amplitude fluctuations than their bluer counterparts. We extract a characteristic variability timescale, tch, via wavelet transformations that are sensitive to both continuous and localized fluctuations. Cool supergiants are characterized by longer timescales (>100 days) than the hotter stars. The latter have typical timescales of tens of days but cover a wider range, from our resolution limit of a few days to longer than 100 days timescales. Using a 60-night block of data straddling two nights with a cadence of around 2 minutes, we extracted tch in the range 0.1--10 days with amplitudes of a few percent for 13 stars. Though there is broad agreement between the observed variability characteristics in the different parts of the upper CMD with theoretical predictions, detailed comparison requires models with a more comprehensive treatment of the various physical processes operating in these stars such as pulsation, subsurface convection, and the effect of binary companions.

Figures

Figures reproduced from arXiv: 1908.02439 by the authors.

Figure 1
Figure 1. Distribution of standard deviation ∆mR against mean magnitude hmRi for the massive stars in M31 based on iPTF light curves. The red points mark the RSGs with observed variability from Paper I. Points above the orange line are stars with their computed RMS variability exceeding that from noise. the standard 200 between the position of the detection on the difference images and the tabulated source po￾sition in the te… view at source ↗
Figure 2
Figure 2. Examples of iPTF light curves along with the corresponding wavelet transform maps for an LBV (cross-id AE And), YSG, and WR star (top, left to right), and for supergiants of type O, B, A (bottom, left to right). The red curve in each panel with the observed light curve shows the reconstruction (see text). The ID of the star from MNS16 is shown on top of each plot. The time axis in the light curve plots is with respe… view at source ↗
Figure 3
Figure 3. Approximate values of noise (in flux units) for PTF data against characteristic timescales over which they are applicable, obtained by smoothing out the contributions from smaller timescales. The blue circles denote noise es￾timates extracted using the long baseline PTF light curves of static stars from Paper I and the red line is the fit to them. The green square symbols denote noise estimates ob￾tained using the h… view at source ↗
Figures from the paper (21 more)
Figure 4
Figure 4. Figure 4: iPTF light curves for the two stars—J004026.84+403504.6 (top) and J004509.86+413031.5 (bottom)—vetted out as discussed in Sect. 3.2. Their wavelet transforms are shown in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Coefficient of variation, i.e., the ratio of the lo￾calized RMS amplitude to the average flux value of the light curve, as a function of the extracted tch for the 13 stars hav￾ing significant variations in the high-cadence blocks. The data points from the same star are…
Figure 6
Figure 6. Figure 6: CMD (left) showing the fraction of massive stars in M31 identified by MNS16 with observed variability on timescales ≥ 10 days from iPTF, and as a function of spectral types for the supergiants (right). The vertical error bars in the right panel indicate the 95% binomia…
Figure 7
Figure 7. Figure 7: CMDs based on the LGGS photometry of the massive stars in M31 from MNS16, color-coded by the observed variability amplitude from iPTF, expressed as the RMS deviation ∆mR from the mean of the light curve. Grey plus symbols signify objects that did not have detectable va…
Figure 8
Figure 8. Figure 8: Left: Range of RMS amplitudes for the different spectral types of supergiants in M31. The box extends from the first quartile to the third quartile of the corresponding RMS distribution, and the orange line shows the median while the whiskers extend to the minimum and …
Figure 9
Figure 9. Figure 9: CMDs similar to [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Left: Characteristic timescales tch, obtained from the wavelet transforms of the iPTF light curves (Sect. 3.2), against color obtained from the LGGS photometry of massive stars in M31. It is to be noted that the density of points should not be over-interpreted, as our…
Figure 11
Figure 11. Figure 11: Coefficient of variation as a function of characteristic timescales tch for the different types of supergiants as indicated in the respective legends. The grey squares are the data points, while the blue circles denote the median after binning the data logarithmically…
Figure 12
Figure 12. Figure 12: iPTF light curve for the W UMa candidate along with its wavelet transform based on the high-cadence block (right). The reconstructed signals are shown in red for both the lower and higher resolution in the left and middle panels, respectively, and the wavelet transfor…
Figure 13
Figure 13. Figure 13: iPTF light curves along with the corresponding wavelet transform maps for O stars (top) and B stars (bottom). The red curve shows the reconstructed signal, and the ID of the star from MNS16 is indicated on top of the plot. similar to the input values for the three lig…
Figure 13
Figure 13. Figure 13: Contd. for AI stars (top) and FI stars (bottom). amplitude of the simulated light curve, evaluated over an interval of width 2 × ttrue, with the root of the transform power corresponding to tch, and find it to be 1.7 × pixel/2.5(days). We also perform the wavelet tran…
Figure 13
Figure 13. Figure 13: Contd. for GI stars (top) and KI stars (bottom) [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 13
Figure 13. Figure 13: Contd. for M stars (top) and WR stars (bottom) [PITH_FULL_IMAGE:figures/full_fig_p025_13.png]
Figure 14
Figure 14. Figure 14: iPTF light curves along with the corresponding wavelet transform maps for known and candidate LBVs in M31 from MNS16. The red curve in each panel with the observed light curve shows the reconstruction (see text). The ID of the star from MNS16 is shown on top of each p…
Figure 14
Figure 14. Figure 14: Contd [PITH_FULL_IMAGE:figures/full_fig_p027_14.png]
Figure 14
Figure 14. Figure 14: Contd [PITH_FULL_IMAGE:figures/full_fig_p028_14.png]
Figure 14
Figure 14. Figure 14: Contd [PITH_FULL_IMAGE:figures/full_fig_p029_14.png]
Figure 15
Figure 15. Figure 15: iPTF light curves along with the corresponding wavelet transform maps for stars with significant timescales in the high-cadence block. The ID of the star from MNS16 is shown on top of each plot and the spectral types of the stars (going from left to right) are O7+O9f:…
Figure 15
Figure 15. Figure 15: Contd. The spectral types of the stars (going from left to right) are B0Ia, B1.5I:, B2.5Ia, (top panels), and B8I, B5Ia+Neb, A:I (bottom panels) [PITH_FULL_IMAGE:figures/full_fig_p030_15.png]
Figure 15
Figure 15. Figure 15: Contd. The spectral types of the stars are YSG: (all three top panels) and M0I (bottom) [PITH_FULL_IMAGE:figures/full_fig_p031_15.png]
Figure 16
Figure 16. Figure 16: Simulated light curves and their corresponding wavelet transform signal (top three panels). The input and recovered characteristic timescales, ttrue and tch, respectively, are also indicated in the legend. The bottom panel shows the theoretical lightcurve of the LBV m…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

125 extracted references · 58 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 note 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.co...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.doi doi empty "" "doi:" doi * if FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix ":" * if eprint field.or.null * if FUNCTION format.pid eprint empty format.doi format.eprint if FUNCTION n.dashify 't := "" t...

  3. [3]

    1B·Z)Vd#>ػЋ X` EFRuQRw g|tP yb넷 <K oC5W^xYR8\ fJ åbq -b14u&)yi R 5x1rD J s O(W# n\ & #1|Q0B^! I ԔaD0 jG

    thebibliography [1] 20pt to REFERENCES 6pt =0pt 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 on reference command Each re...

    work page arXiv 2017

  4. [4]

    Abbott , D. C. 1982, , 263, 723

    1982

  5. [5]

    2011, New Astronomy, 16, 421

    Abolmasov , P. 2011, New Astronomy, 16, 421

    2011

  6. [6]

    Aerts , C., & Rogers , T. M. 2015, , 806, L33

    2015

  7. [7]

    M., S \' mon-D \' az , S., et al

    Aerts , C., Bowman , D. M., S \' mon-D \' az , S., et al. 2018, , 476, 1234

    2018

  8. [8]

    P., Tollerud , E

    Astropy Collaboration , Robitaille , T. P., Tollerud , E. J., et al. 2013, , 558, A33

    2013

Show all 125 references
  1. [9]

    Bailer-Jones , C. A. L., Rybizki , J., Fouesneau , M., Mantelet , G., & Andrae , R. 2018, , 156, 58

    2018

  2. [10]

    C., Kulkarni , S

    Bellm , E. C., Kulkarni , S. R., Graham , M. J., et al. 2019, , 131, 018002

    2019

  3. [11]

    D., Harper , G

    Bennett , P. D., Harper , G. M., Brown , A., & Hummel , C. A. 1996, , 471, 454

    1996

  4. [12]

    C., Stanek , K

    Bird , J. C., Stanek , K. Z., & Prieto , J. L. 2009, , 695, 874

    2009

  5. [13]

    1999 a , , 512, 711

    Bono , G., Caputo , F., Castellani , V., & Marconi , M. 1999 a , , 512, 711

    1999

  6. [14]

    Bono , G., Marconi , M., & Stellingwerf , R. F. 1999 b , , 122, 167

    1999

  7. [15]

    M., Burssens , S., Pedersen , M

    Bowman , D. M., Burssens , S., Pedersen , M. G., et al. 2019, Nature Astronomy, 320

    2019

  8. [16]

    1978, , 65, 363

    Burki , G., Maeder , A., & Rufener , F. 1978, , 65, 363

    1978

  9. [17]

    2019, arXiv e-prints, arXiv:1904.02161

    Cantiello , M., & Braithwaite , J. 2019, arXiv e-prints, arXiv:1904.02161

    2019 arXiv

  10. [18]

    2009, , 499, 279

    Cantiello , M., Langer , N., Brott , I., et al. 2009, , 499, 279

    2009

  11. [19]

    2018, , 859, 140

    Chen , X., Deng , L., de Grijs , R., Wang , S., & Feng , Y. 2018, , 859, 140

    2018

  12. [20]

    2009, , 506, 1351

    Chiavassa , A., Plez , B., Josselin , E., & Freytag , B. 2009, , 506, 1351

    2009

  13. [21]

    U., & Kim , J

    Chun , S.-H., Yoon , S.-C., Jung , M.-K., Kim , D. U., & Kim , J. 2018, , 853, 79

    2018

  14. [22]

    2018, , 864, 111

    Conroy , C., Strader , J., van Dokkum , P., et al. 2018, , 864, 111

    2018

  15. [23]

    A., Cox , N

    Cordiner , M. A., Cox , N. L. J., Evans , C. J., et al. 2011, , 726, 39

    2011

  16. [24]

    N., Morgan , S

    Cox , A. N., Morgan , S. M., Rogers , F. J., & Iglesias , C. A. 1992, , 393, 272

    1992

  17. [25]

    2005, Journal of Parallel and Distributed Computing, 65, 1108

    Dalc\'in, L., Paz, R., & Storti, M. 2005, Journal of Parallel and Distributed Computing, 65, 1108 . http://www.sciencedirect.com/science/article/pii/S0743731505000560

    2005

  18. [26]

    Z., Levesque , E

    Dorn-Wallenstein , T. Z., Levesque , E. M., & Davenport , J. R. A. 2019, arXiv e-prints, arXiv:1901.09930

    2019 arXiv

  19. [27]

    R., Dufton , P

    Dunstall , P. R., Dufton , P. L., Sana , H., et al. 2015, , 580, A93

    2015

  20. [28]

    A., Moskalik , P., & Pamyatnykh , A

    Dziembowski , W. A., Moskalik , P., & Pamyatnykh , A. A. 1993, , 265, 588

    1993

  21. [29]

    1996, , 112, 1709

    Foster , G. 1996, , 112, 1709

    1996

  22. [30]

    Freyer , T., Hensler , G., & Yorke , H. W. 2003, , 594, 888

    2003

  23. [31]

    2006, , 638, 262

    ---. 2006, , 638, 262

    2006

  24. [32]

    L., Galyagin , D., Sokoloff , D., & Soon , W

    Frick , P., Baliunas , S. L., Galyagin , D., Sokoloff , D., & Soon , W. 1997, , 483, 426

    1997

  25. [33]

    Gaia Collaboration , Prusti , T., de Bruijne , J. H. J., et al. 2016, , 595, A1

    2016

  26. [34]

    2018 a , arXiv e-prints, arXiv:1804.09382

    Gaia Collaboration , Eyer , L., Rimoldini , L., et al. 2018 a , arXiv e-prints, arXiv:1804.09382

    2018 arXiv

  27. [35]

    Gaia Collaboration , Brown , A. G. A., Vallenari , A., et al. 2018 b , , 616, A1

    2018

  28. [36]

    2018 c , , 616, A1

    ---. 2018 c , , 616, A1

    2018

  29. [37]

    2010, , 463, 203

    Governato , F., Brook , C., Mayer , L., et al. 2010, , 463, 203

    2010

  30. [38]

    H., & Li , Y

    Guo , J. H., & Li , Y. 2002, , 565, 559

    2002

  31. [39]

    M., Dettmar , R.-J., Beckman , J

    Haffner , L. M., Dettmar , R.-J., Beckman , J. E., et al. 2009, Reviews of Modern Physics, 81, 969

    2009

  32. [40]

    1997, , 327, 224

    Heger , A., Jeannin , L., Langer , N., & Baraffe , I. 1997, , 327, 224

    1997

  33. [41]

    1953, , 118, 353

    Hubble , E., & Sandage , A. 1953, , 118, 353

    1953

  34. [42]

    M., & Davidson , K

    Humphreys , R. M., & Davidson , K. 1994, , 106, 1025

    1994

  35. [43]

    M., Davidson , K., Hahn , D., Martin , J

    Humphreys , R. M., Davidson , K., Hahn , D., Martin , J. C., & Weis , K. 2017, , 844, 40

    2017

  36. [44]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90

    2007

  37. [45]

    2015, , 813, 74

    Jiang , Y.-F., Cantiello , M., Bildsten , L., Quataert , E., & Blaes , O. 2015, , 813, 74

    2015

  38. [46]

    2018, , 561, 498

    Jiang , Y.-F., Cantiello , M., Bildsten , L., et al. 2018, , 561, 498

    2018

  39. [47]

    2001--, SciPy : Open source scientific tools for Python , , , http://www.scipy.org/

    Jones, E., Oliphant, T., Peterson, P., et al. 2001--, SciPy : Open source scientific tools for Python , , , http://www.scipy.org/. http://www.scipy.org/

    2001

  40. [48]

    2010, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Kaiser , N., Burgett , W., Chambers , K., et al. 2010, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7733, The Pan-STARRS wide-field optical/NIR imaging survey , 77330E

    2010

  41. [49]

    Kervella , P., Decin , L., Richards , A. M. S., et al. 2018, , 609, A67

    2018

  42. [50]

    J., & Glatzel , W

    Kiriakidis , M., Fricke , K. J., & Glatzel , W. 1993, , 264, 50

    1993

  43. [51]

    L., Szab \'o , G

    Kiss , L. L., Szab \'o , G. M., & Bedding , T. R. 2006, , 372, 1721

    2006

  44. [52]

    Z., Soszy \'n ski , I., et al

    Kourniotis , M., Bonanos , A. Z., Soszy \'n ski , I., et al. 2014, , 562, A125

    2014

  45. [53]

    R., Surace , J., Grillmair , C

    Laher , R. R., Surace , J., Grillmair , C. J., et al. 2014, , 126, 674

    2014

  46. [54]

    Lamers , H. J. G. L. M., Bastiaanse , M. V., Aerts , C., & Spoon , H. W. W. 1998, , 335, 605

    1998

  47. [55]

    M., Kulkarni , S

    Law , N. M., Kulkarni , S. R., Dekany , R. G., et al. 2009, , 121, 1395

    2009

  48. [56]

    H., Riffeser , A., Koppenhoefer , J., et al

    Lee , C. H., Riffeser , A., Koppenhoefer , J., et al. 2012, , 143, 89

    2012

  49. [57]

    Levesque , E. M. 2017, Astrophysics of Red Supergiants , doi:10.1088/978-0-7503-1329-2

    2017 doi

  50. [58]

    Li , Y., & Gong , Z. G. 1994, , 289, 449

    1994

  51. [59]

    Lilly , J. M. 2017, arXiv e-prints, arXiv:1703.04882

    2017 arXiv

  52. [60]

    1984, , 133, 307

    Lovy , D., Maeder , A., Noels , A., & Gabriel , M. 1984, , 133, 307

    1984

  53. [61]

    1980, , 90, 311

    Maeder , A. 1980, , 90, 311

    1980

  54. [62]

    1985, , 147, 300

    ---. 1985, , 147, 300

    1985

  55. [63]

    J., Laher , R

    Masci , F. J., Laher , R. R., Rebbapragada , U. D., et al. 2017, , 129, 014002

    2017

  56. [64]

    2003, , 41, 15

    Massey , P. 2003, , 41, 15

    2003

  57. [65]

    2013, New Astronomy Reviews, 57, 14

    ---. 2013, New Astronomy Reviews, 57, 14

    2013

  58. [66]

    Massey , P., & Evans , K. A. 2016, , 826, 224

    2016

  59. [67]

    T., Olsen , K

    Massey , P., McNeill , R. T., Olsen , K. A. G., et al. 2007, , 134, 2474

    2007

  60. [68]

    F., & Levesque , E

    Massey , P., Neugent , K. F., & Levesque , E. M. 2017, Philosophical Transactions of the Royal Society of London Series A, 375, 20160267

    2017

  61. [69]

    F., & Smart , B

    Massey , P., Neugent , K. F., & Smart , B. M. 2016, , 152, 62

    2016

  62. [70]

    Massey , P., Olsen , K. A. G., Hodge , P. W., et al. 2006, , 131, 2478

    2006

  63. [71]

    Matzner , C. D. 2002, , 566, 302

    2002

  64. [72]

    2010, in Proceedings of the 9th Python in Science Conference, ed

    McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt & J. Millman, 51 -- 56

    2010

  65. [73]

    H., et al

    Mehner , A., Baade , D., Groh , J. H., et al. 2017, , 608, A124

    2017

  66. [74]

    Moffat , A. F. J., & Robert , C. 1991, in IAU Symposium, Vol. 143, Wolf-Rayet Stars and Interrelations with Other Massive Stars in Galaxies, ed. K. A. van der Hucht & B. Hidayat , 109

    1991

  67. [75]

    R., & Lester , J

    Neilson , H. R., & Lester , J. B. 2008, , 684, 569

    2008

  68. [76]

    2019, Galaxies, 7, 74

    Neugent , K., & Massey , P. 2019, Galaxies, 7, 74

    2019

  69. [77]

    F., & Massey , P

    Neugent , K. F., & Massey , P. 2014, , 789, 10

    2014

  70. [78]

    F., Massey , P., & Georgy , C

    Neugent , K. F., Massey , P., & Georgy , C. 2012, , 759, 11

    2012

  71. [79]

    2013, , 51, 457

    Nomoto , K., Kobayashi , C., & Tominaga , N. 2013, , 51, 457

    2013

  72. [80]

    R., & En lin , T

    Oppermann , N., Selig , M., Bell , M. R., & En lin , T. A. 2013, , 87, 032136

    2013

  73. [81]

    2019, arXiv e-prints, arXiv:1904.07878

    Ouchi , R., & Maeda , K. 2019, arXiv e-prints, arXiv:1904.07878

    2019 arXiv

  74. [82]

    R., Lennon , D

    Patrick , L. R., Lennon , D. J., Britavskiy , N., et al. 2019, , 624, A129

    2019

  75. [83]

    2013, , 208, 4

    Paxton , B., Cantiello , M., Arras , P., et al. 2013, , 208, 4

    2013

  76. [84]

    2015, , 220, 15

    Paxton , B., Marchant , P., Schwab , J., et al. 2015, , 220, 15

    2015

  77. [85]

    G., Chowdhury , S., Johnston , C., et al

    Pedersen , M. G., Chowdhury , S., Johnston , C., et al. 2019, , 872, L9

    2019

  78. [86]

    H., Friend , D

    Poe , C. H., Friend , D. B., & Cassinelli , J. P. 1989, , 337, 888

    1989

  79. [87]

    2002, , 52, 397

    Pojmanski , G. 2002, , 52, 397

    2002

  80. [88]

    L., Stanek , K

    Prieto , J. L., Stanek , K. Z., Kochanek , C. S., et al. 2008, , 673, L59

    2008

  81. [89]

    R., Law , N

    Rau , A., Kulkarni , S. R., Law , N. M., et al. 2009, , 121, 1334

    2009

  82. [90]

    2019, , 241, 35

    Ren , Y., Jiang , B.-W., Yang , M., & Gao , J. 2019, , 241, 35

    2019

  83. [91]

    E., Ellis , R

    Robertson , B. E., Ellis , R. S., Furlanetto , S. R., & Dunlop , J. S. 2015, , 802, L19

    2015

  84. [92]

    1976, , 26, 275

    Rufener , F. 1976, , 26, 275

    1976

  85. [93]

    1978, , 31, 179

    Rufener , F., Maeder , A., & Burki , G. 1978, , 31, 179

    1978

  86. [94]

    E., de Koter , A., et al

    Sana , H., de Mink , S. E., de Koter , A., et al. 2012, Science, 337, 444

    2012

  87. [95]

    E., Garrison , R

    Schild , R. E., Garrison , R. F., & Hiltner , W. A. 1983, , 88, 67

    1983

  88. [96]

    J., Finkbeiner , D

    Schlegel , D. J., Finkbeiner , D. P., & Davis , M. 1998, , 500, 525

    1998

  89. [97]

    R., Junklewitz , H., et al

    Selig , M., Bell , M. R., Junklewitz , H., et al. 2013, , 554, A26

    2013

  90. [98]

    A., et al

    Sim \'o n-D \' az , S., Aerts , C., Urbaneja , M. A., et al. 2018, , 612, A40

    2018

  91. [99]

    D., Gilfanov , M., Kupfer , T., et al

    Soraisam , M. D., Gilfanov , M., Kupfer , T., et al. 2017, , 599, A48

    2017

  92. [100]

    D., Bildsten , L., Drout , M

    Soraisam , M. D., Bildsten , L., Drout , M. R., et al. 2018, , 859, 73

    2018

  93. [101]

    2014, , 570, A3

    Sperauskas , J., Za c s , L., Raudeli \= u nas , S., Musaev , F., & Puzin , V. 2014, , 570, A3

    2014

  94. [102]

    T., Bonanos , A

    Spetsieri , Z. T., Bonanos , A. Z., Kourniotis , M., et al. 2018, , 618, A185

    2018

  95. [103]

    Stetson , P. B. 1987, , 99, 191

    1987

  96. [104]

    1990, , 102, 932

    ---. 1990, , 102, 932

    1990

  97. [105]

    1969, , 156, 541

    Stothers , R. 1969, , 156, 541

    1969

  98. [106]

    Stothers , R., & Leung , K. C. 1971, , 10, 290

    1971

  99. [107]

    Stothers , R. B. 2010, , 725, 1170

    2010

  100. [108]

    B., & Chin , C.-W

    Stothers , R. B., & Chin , C.-W. 1995, , 451, L61

    1995

  101. [109]

    M., Stanek , K

    Szczygie , D. M., Stanek , K. Z., Bonanos , A. Z., et al. 2010, , 140, 14

    2010

  102. [110]

    Townsend , R. H. D., & Teitler , S. A. 2013, , 435, 3406

    2013

  103. [111]

    2003, , 53, 291

    Udalski , A. 2003, , 53, 291

    2003

  104. [112]

    C., & Varoquaux, G

    van der Walt, S., Colbert, S. C., & Varoquaux, G. 2011, Computing In Science & Engineering, 13, 22

    2011

  105. [113]

    van Genderen , A. M. 2001, , 366, 508

    2001

  106. [114]

    M., de Groot , M., & Sterken , C

    van Genderen , A. M., de Groot , M., & Sterken , C. 1997, , 124, 517

    1997

  107. [115]

    M., Sterken , C., & Jones , A

    van Genderen , A. M., Sterken , C., & Jones , A. F. 2004, , 419, 667

    2004

  108. [116]

    M., & The , P

    van Genderen , A. M., & The , P. S. 1984, , 39, 317

    1984

  109. [117]

    M., Veijgen , S

    van Genderen , A. M., Veijgen , S. R. G., & van der Hucht , K. A. 2013, , 345, 133

    2013

  110. [118]

    2005, , 43, 769

    Veilleux , S., Cecil , G., & Bland-Hawthorn , J. 2005, , 43, 769

    2005

  111. [119]

    L., & Guinan , E

    Vilardell , F., Ribas , I., Jordi , C., Fitzpatrick , E. L., & Guinan , E. F. 2010, , 509, A70

    2010

  112. [120]

    Vreux , J. M. 1985, , 97, 274

    1985

  113. [121]

    G., McKee , C

    Wolfire , M. G., McKee , C. F., Hollenbach , D., & Tielens , A. G. G. M. 2003, , 587, 278

    2003

  114. [122]

    Wright , K. O. 1977, , 71, 152

    1977

  115. [123]

    Yang , M., & Jiang , B. W. 2011, , 727, 53

    2011

  116. [124]

    2012, , 754, 35

    ---. 2012, , 754, 35

    2012

  117. [125]

    2010, , 717, L62

    Yoon , S.-C., & Cantiello , M. 2010, , 717, L62

    2010

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.