SNIa detection in the SNLS photometric analysis using Morphological Component Analysis

Abstract

Detection of supernovae (SNe) 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 Monte-Carlo (MC) generated images were used to study detection efficiency and coordinate resolution. When tested on SNLS 3-year data this procedure decreases the number of detections by a factor of two, while losing only 10% of SN-like events, almost all faint ones. MC results show that SNIa detection efficiency is equivalent to that of the original method for bright events, while the coordinate resolution is improved.

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

“…Full details can be found in Starck et al (2010). This method has been used to extract filamentary clouds in Herschel data (André et al 2010) or, more recently, to improve SNIa detection in the SuperNova Legacy Survey data set (Möller et al 2015).…”

Image Subtraction in Fourier Space

“…Although it is prone to numerical errors and produces noticeable artifacts around bright stars and the central regions of bright galaxies, the Alard & Lupton (1998) method serves as the foundation upon which several successful transient survey programs have been built (e.g., Price & Magnier 2019;Bellm et al 2018). In addition to the image subtraction algorithm, data-driven approaches such as random forest algorithm (e.g., Goldstein et al 2015), morphological component analysis (Möller et al 2015) and deep neural networks (e.g., Cabrera-Vives et al 2016;Cabrera-Vives et al 2017;Reyes et al 2018;du Buisson et al 2015;Morii et al 2016), have been introduced to alleviate the workload of human-scanners for identification of potential transients. Sedaghat & Mahabal (2018) recently suggested the use of deep learning neural networks instead of PSF-matching for image differencing.…”