Variability of Massive Stars in M31 from the Palomar Transient Factory
Bibliographic record
Abstract
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 that 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 yr) 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, t ch, 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. Using a 60 night block of data straddling two nights with a cadence of around 2 minutes, we extracted t ch 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".