Real Time Classification of Transient Events in Synoptic Sky Surveys

TL;DR

This paper discusses the development of automated, Bayesian-based methods for real-time classification of transient astronomical events in sky surveys, aiming to improve scientific analysis and follow-up efficiency.

Contribution

It introduces novel Bayesian classification techniques tailored for rapid analysis of transient events in large-scale sky surveys.

Findings

Effective Bayesian methods demonstrated on CRTS data

Improved speed and accuracy in transient classification

Framework adaptable to future large surveys

Abstract

An automated, rapid classification of transient events detected in the modern synoptic sky surveys is essential for their scientific utility and effective follow-up using scarce resources. This problem will grow by orders of magnitude with the next generation of surveys. We are exploring a variety of novel automated classification techniques, mostly Bayesian, to respond to these challenges, using the ongoing CRTS sky survey as a testbed. We describe briefly some of the methods used.