# Deep learning detection of transients

## TL;DR

This paper introduces a deep learning-based algorithm for detecting astrophysical transients, capable of blind searches and targeted detection, outperforming existing methods especially for complex spectral features.

## Contribution

A novel, data-driven deep learning framework combining anomaly detection and classification for unbiased transient detection in astrophysics.

## Key findings

- More robust than existing methods for complex spectral features
- Effective in detecting low-luminosity gamma-ray bursts
- Applicable to multiwavelength and multi-messenger data

## Abstract

The next generation of observatories will facilitate the discovery of new types of astrophysical transients. The detection of such phenomena, whose characteristics are presently poorly constrained, will hinge on the ability to perform blind searches. We present a new algorithm for this purpose, based on deep learning. We incorporate two approaches, utilising anomaly detection and classification techniques. The first is model-independent, avoiding the use of background modelling and instrument simulations. The second method enables targeted searches, relying on generic spectral and temporal patterns as input. We compare our methodology with the existing approach to serendipitous detection of gamma-ray transients. The algorithm is shown to be more robust, especially for non-trivial spectral features. We use our framework to derive the detection prospects of low-luminosity gamma-ray bursts with the upcoming Cherenkov Telescope Array. Our method is an unbiased, completely data-driven approach for multiwavelength and multi-messenger transient detection.