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.Comment: Accepted for MNRAS. The code used for the analysis can be found at
https://github.com/BayesFlare/bayesflare/releases/tag/v1.0.