SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

TL;DR

This paper presents a morphological component analysis technique to improve supernova detection in large survey data, reducing false positives and enhancing coordinate accuracy without sacrificing detection efficiency for bright events.

Contribution

The authors introduce a novel morphological component analysis method for supernova detection that effectively reduces false detections and improves coordinate resolution in survey data.

Findings

Detection rate for bright SNe is maintained.

False detections are halved using the new method.

Coordinate resolution is improved.

Abstract

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.