A Bayesian method for detecting stellar flares

Pitkin, M. and Williams, D. and Fletcher, L. and Grant, S. D. T. (2014) A Bayesian method for detecting stellar flares. Monthly Notices of the Royal Astronomical Society, 445. pp. 2268-2284. ISSN 0035-8711

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We present a Bayesian-odds-ratio-based algorithm for detecting stellar flares in light-curve data. We assume flares are described by a model in which there is a rapid rise with a half-Gaussian profile, followed by an exponential decay. Our signal model also contains a polynomial background model required to fit underlying light-curve variations in the data, which could otherwise partially mimic a flare. We characterize the false alarm probability and efficiency of this method under the assumption that any unmodelled noise in the data is Gaussian, and compare it with a simpler thresholding method based on that used in Walkowicz et al. We find our method has a significant increase in detection efficiency for low signal-to-noise ratio (S/N) flares. For a conservative false alarm probability our method can detect 95 per cent of flares with S/N less than 20, as compared to S/N of 25 for the simpler method. We also test how well the assumption of Gaussian noise holds by applying the method to a selection of ‘quiet’ Kepler stars. As an example we have applied our method to a selection of stars in Kepler Quarter 1 data. The method finds 687 flaring stars with a total of 1873 flares after vetos have been applied. For these flares we have made preliminary characterizations of their durations and and S/N.

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Journal Article
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Monthly Notices of the Royal Astronomical Society
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This is a pre-copy-editing, author-produced PDF of an article accepted for publication in onthly Notices of the Royal Astronomical Society following peer review. The definitive publisher-authenticated version M. Pitkin, D. Williams, L. Fletcher, S. D. T. Grant, A Bayesian method for detecting stellar flares, Monthly Notices of the Royal Astronomical Society, Volume 445, Issue 3, 11 December 2014, Pages 2268–2284 is available online at: https://doi.org/10.1093/mnras/stu1889
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24 Jul 2019 08:40
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19 Sep 2023 02:14