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تسجيل مستخدم جديدA Bayesian method for detecting stellar flares
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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. This is required to fit underlying light curve variations that are expected in the data, which could otherwise partially mimic a flare. We characterise the false alarm probability and efficiency of this method and compare it with a simpler thresholding method based on that used in Walkowicz et al (2011). 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% of flares with S/N less than ~20, as compared to S/N of ~25 for the simpler method. 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 characterised their durations and and signal-to-noise ratios.
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This summary reports on papers presented at the Cool Stars-16 meeting in the splinter session Solar and Stellar flares. Although many topics were discussed, the main themes were the commonality of interests, and of physics, between the solar and stel
lar flare communities, and the opportunities for important new observations in the near future.
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