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 mod
el 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.
We describe the consistency testing of a new code for gravitational wave signal parameter estimation in known pulsar searches. The code uses an implementation of nested sampling to explore the likelihood volume. Using fake signals and simulated noise
we compare this to a previous code that calculated the signal parameter posterior distributions on both a grid and using a crude Markov chain Monte Carlo (MCMC) method. We define a new parameterisation of two orientation angles of neutron stars used in the signal model (the initial phase and polarisation angle), which breaks a degeneracy between them and allows more efficient exploration of those parameters. Finally, we briefly describe potential areas for further study and the uses of this code in the future.
