Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data

TL;DR

This paper introduces two real-time anomaly detection methods for astronomical transient light curves, leveraging neural networks and Bayesian models to efficiently identify unusual stellar events in large sky surveys.

Contribution

It presents two novel approaches—probabilistic neural networks and Bayesian models—for automatic, real-time detection of anomalies in astronomical transient data.

Findings

Bayesian model outperforms neural network in anomaly detection accuracy.

Neural networks are less suitable for anomaly detection despite their flexibility.

Proposed methods enable rapid identification of unusual stellar events.

Abstract

Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy. Some of these transients are the explosive deaths of stars known as supernovae while others are rare, exotic, or entirely new kinds of exciting stellar explosions. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients, making standard approaches of visually identifying new and interesting transients infeasible. To meet this demand, we present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies. The first approach is a probabilistic neural network built using Temporal Convolutional Networks (TCNs) and the second is an interpretable Bayesian parametric model of a transient. We show that the flexibility of neural networks, the attribute that makes them such a powerful tool for many regression tasks, is what makes them less suitable for anomaly detection when compared with our parametric model.