• DOI:10.1093/mnras/stu1889
• Corpus ID: 3620114

A Bayesian method for detecting stellar flares

@article{Pitkin2014ABM,
  title={A Bayesian method for detecting stellar flares},
  author={Matthew Pitkin and D. P. Williams and Lyndsay Fletcher and Samuel D. T. Grant},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2014},
  volume={445},
  pages={2268-2284}
}

• M. Pitkin, D. Williams, +1 author S. Grant
• Published 6 June 2014
• Physics
• Monthly Notices of the Royal Astronomical Society

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. (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. 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 characterisations of their durations and and signal-to-noise ratios.