A novel method for transient detection in high-cadence optical surveys:   Its application for a systematic search for novae in M31
  [abridged] In large-scale time-domain surveys, the processing of data, from procurement up to the detection of sources, is generally automated. One of the main challenges is contamination by artifacts, especially in regions of strong unresolved emission. We present a novel method for identifying candidates for variables and transients from the outputs of such surveys' data pipelines. We use the method to systematically search for novae in iPTF observations of the bulge of M31. We demonstrate that most artifacts produced by the iPTF pipeline form a locally uniform background of false detections approximately obeying Poissonian statistics, whereas genuine variables and transients as well as artifacts associated with bright stars result in clusters of detections, whose spread is determined by the source localization accuracy. This makes the problem analogous to source detection on images produced by X-ray telescopes, enabling one to utilize tools developed in X-ray astronomy. In particular, we use a wavelet-based source detection algorithm from the Chandra data analysis package CIAO. Starting from ~2.5x10^5 raw detections made by the iPTF data pipeline, we obtain ~4000 unique source candidates. Cross-matching these candidates with the source-catalog of a deep reference image, we find counterparts for ~90% of them. These are either artifacts due to imperfect PSF matching or genuine variable sources. The remaining ~400 detections are transient sources. We identify novae among these candidates by applying selection cuts based on the expected properties of nova lightcurves. Thus, we recovered all 12 known novae registered during the time span of the survey and discovered three nova candidates. Our method is generic and can be applied for mining any target out of the artifacts in optical time-domain data. As it is fully automated, its incompleteness can be accurately computed and corrected for. 
Monika D. Soraisam, Marat Gilfanov, Thomas Kupfer, Frank Masci, Allen   W. Shafter, Thomas A. Prince, Shrinivas R. Kulkarni,...
astro-ph.IM astro-ph.GA