SNIa detection in the SNLS photometric analysis using Morphological
  Component Analysis
  Detection of supernovae and, more generally, of transient events in large
surveys can provide numerous false detections.In the case of a deferred
processing of survey images, this implies reconstructing complete light curves
for all detections, requiring sizable processing time and resources.Optimizing
the detection of transient events is thus an important issue for both present
and future surveys.We present here the optimization done in the SuperNova
Legacy Survey (SNLS) for the 5-year data deferred photometric analysis. In this
analysis, detections are derived from stacks of subtracted images with one
stack per lunation.The 3-year analysis provided 300,000 detections dominated by
signals of bright objects that were not perfectly subtracted.Allowing these
artifacts to be detected leads not only to a waste of resources but also to
possible signal coordinate contamination.We developed a subtracted image stack
treatment to reduce the number of non SN-like events using morphological
component analysis.This technique exploits the morphological diversity of
objects to be detected to extract the signal of interest.At the level of our
subtraction stacks, SN-like events are rather circular objects while most
spurious detections exhibit different shapes.A two-step procedure was necessary
to have a proper evaluation of the noise in the subtracted image stacks and
thus a reliable signal extraction.We also set up a new detection strategy to
obtain coordinates with good resolution for the extracted signal.SNIa MC
generated images were used to study detection efficiency and coordinate
resolution.When tested on SNLS 3 data this procedure decreases the number of
detections by a factor of two, while losing only 10% of SN-like events, almost
all faint.MC results show that SNIa detection efficiency is equivalent to that
of the original method for bright events, while the coordinate resolution is
improved.
