Identifying low-amplitude pulsating stars through microlensing observations
Bibliographic record
Abstract
ABSTRACT One possibility for detecting low-amplitude pulsational variations is through gravitational microlensing. During a microlensing event, the temporary brightness increase leads to improvement in the signal-to-noise ratio, and thereby better detectability of pulsational signatures in light curves. We explore this possibility under two primary considerations. The first is when the standard point-source and point-lens approximation applies. In this scenario, dividing the observed light curve by the best-fitted microlensing model leads to residuals that result in pulsational features with improved uncertainties. The second is for transit events (single lens) or caustic crossing (binary lens). The point-source approximation breaks down, and residuals relative to a simple best-fitted microlensing model display more complex behaviour. We employ a Monte Carlo simulation of microlensing of pulsating variables toward the Galactic bulge for the surveys of OGLE and of KMTNet. We demonstrate that the efficiency for detecting pulsational signatures with intrinsic amplitudes of <0.25 mag during single and binary microlensing events, at differences in χ2 of Δχ2 > 350, is $\sim \!50\!-\!60{{\ \rm per\ cent}}$. The maximum efficiency occurs for pulsational periods P ≃ 0.1–0.3 d. We also study the possibility that high-magnification microlensing events of non-radially pulsating stars could be misinterpreted as planetary or binary microlensing events. We conclude that small asymmetric features around light curve peaks due to stellar pulsations could be misdiagnosed with crossing (or passing close to) small caustic curves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".