Abstract
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.
Original language | English |
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Pages (from-to) | 2268-2284 |
Number of pages | 17 |
Journal | Monthly Notices of the Royal Astronomical Society |
Volume | 445 |
Issue number | 3 |
Early online date | 17 Oct 2014 |
DOIs | |
Publication status | Published - 11 Dec 2014 |
Keywords
- methods: data analysis
- methods: statistical
- stars: flare
- SYSTEMATIC-ERROR CORRECTION
- WHITE-LIGHT FLARES
- SOLAR-TYPE STARS
- UV CETI STARS
- PHOTOMETRIC VARIABILITY
- OBSERVATIONAL DATA
- KEPLER DATA
- QUARTER 1
- 1ST
- STATISTICS