Towards Real-time Classification of Astronomical Transients

TL;DR

This paper discusses developing real-time classification methods for astronomical transient events using Bayesian and machine learning techniques to improve follow-up observations amid increasing data streams from sky surveys.

Contribution

It introduces a framework combining Bayesian methods and machine learning for automatic, dynamic classification of transient astronomical events using multi-source data.

Findings

Bayesian methods effectively classify transient events.

Machine learning models like Neural Nets and SVMs are being deployed.

Framework enables rapid prioritization for follow-up observations.

Abstract

Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up.