Statistical characterization and classification of astronomical transients with Machine Learning in the era of the VRST

By: Marco Vicedomini, Massimo Brescia, Stefano Cavuoti, Giuseppe Riccio, Giuseppe

Longo (DS LAB - UNINA), 2020, Springer, in press

arXiv: 

Abstract 

Astronomy has entered the multi-messenger data era and Machine Learning has found widespread use in a large variety of applications. The exploitation of present and future synoptic (multi-band and multi-epoch) surveys, like LSST (VRST), requires an extensive use of automatic methods for data processing and interpretation. With data volumes in the petabyte domain, the discrimination of time-critical information has already exceeded the capabilities of human operators and even crowds of scientists have extreme difficulty to manage such amounts of data in multi-dimensional domains. This work is focused on an analysis of critical aspects related to the approach, based on Machine Learning, to variable sky sources classification, with special care to the various types of Supernovae, one of the most. important subjects of Time Domain Astronomy, due to their crucial role in Cosmology. The work is based on a test campaign, with incremental complexity, performed on simulated data. The classification was carried out by comparing the performances among several Machine Learning algorithms on statistical parameters extracted from the light curves. The results show some critical aspects related to the data quality and their parameter space characterization, propaedeutic to the preparation of processing machinery for the real data exploitation in the incoming decade.