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Llamaradas Estelares: Modeling the Morphology of White-Light Flares

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arxiv 2205.05706 v1 pith:PQDTVEIJ submitted 2022-05-11 astro-ph.SR astro-ph.EPastro-ph.IM

Guadalupe Tovar Mendoza , James R. A. Davenport , Eric Agol , James A. G. Jackman , Suzanne L. Hawley This is my paper · ORCID
classification astro-ph.SRastro-ph.EPastro-ph.IM
keywords flaremodeleventsflaresminuteactivecadencemorphology
verification ladder T0 review T1 audit T2 compute T3 formal

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Stellar variability is a limiting factor for planet detection and characterization, particularly around active M-type stars. Here we revisit one of the most active stars from the Kepler mission, the M4 star GJ 1243, and use a sample of 414 flare events from 11 months of 1-minute cadence light curves to study the empirical morphology of white-light stellar flares. We use a Gaussian process detrending technique to account for the underlying starspots. We present an improved analytic, continuous flare template that is generated by stacking the flares onto a scaled time and amplitude and uses a Markov Chain Monte Carlo analysis to fit the model. Our model is defined using classical flare events, but can also be used to model complex, multi-peaked flare events. We demonstrate the utility of our model using TESS data at the 10-minute, 2-minute and 20-second cadence modes. Our new flare model code is made publicly available on GitHub.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. First JWST thermal phase curves of temperate terrestrial exoplanets reveal no thick atmosphere around TRAPPIST-1 b and c

    astro-ph.EP 2025-09 accept novelty 8.0 of 10

    First JWST phase curves of TRAPPIST-1 b and c rule out thick, heat-redistributing atmospheres; b is likely airless, c may retain a tenuous O2 atmosphere.

  2. Microlensing Detection and Inference via Learned Bayes Factors

    astro-ph.IM 2026-07 conditional novelty 6.0 of 10

    A unified transformer-based pipeline detects 99.9% of recoverable simulated microlensing events and outperforms literature hard cuts in the short-duration finite-source regime with amortized neural posterior inference.

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