Flashes in a Star Stream: Automated Classification of Astronomical Transient Events

TL;DR

This paper presents a Bayesian approach for rapid, automated classification of transient astronomical events in large, heterogeneous sky survey data, addressing challenges of data sparsity, incompleteness, and contextual integration.

Contribution

It introduces novel Bayesian techniques tailored for classifying transient events using multi-source archival and contextual data in real-time sky surveys.

Findings

Effective classification of transient events demonstrated in CRTS data

Handling of sparse, heterogeneous, and incomplete data streams

Framework adaptable to future large-scale sky 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 presents some unusual challenges: the data are sparse, heterogeneous and incomplete; evolving in time; and most of the relevant information comes not from the data stream itself, but from a variety of archival data and contextual information (spatial, temporal, and multi-wavelength). We are exploring a variety of novel techniques, mostly Bayesian, to respond to these challenges, using the ongoing CRTS sky survey as a testbed. The current surveys are already overwhelming our ability to effectively follow all of the potentially interesting events, and these challenges will grow by orders of magnitude over the next decade as the more ambitious sky surveys get under way. While we focus on an application in a specific domain (astrophysics), these challenges are more broadly relevant for event or anomaly detection and knowledge discovery in massive data streams.