Identification of Stellar Flares Using Differential Evolution Template Optimization

We explore methods for the identification of stellar flare events in irregularly sampled data of ground-based time domain surveys. In particular, we describe a new technique for identifying flaring stars, which we have implemented in a publicly available Python module called "PyVAN". The approach uses the Differential Evolution algorithm to optimize parameters of empirically derived light-curve templates for different types of stars to fit a candidate light-curve. The difference of the likelihoods that these best-fit templates produced the observed data is then used to delineate targets that are well explained by a flare template but simultaneously poorly explained by templates of common contaminants. By testing on light-curves of known identity and morphology, we show that our technique is capable of recovering flaring status in $69\%$ of all light-curves containing a flare event above thresholds drawn to include $\lt1\%$ of any contaminant population. By applying to Palomar Transient Factory data, we show consistency with prior samples of flaring stars, and identify a small selection of candidate flaring G-type stars for possible follow-up.