# Deep learning detection of transients (ICRC-2019)

## TL;DR

This paper introduces a deep learning-based algorithm for detecting astrophysical transients through blind searches, utilizing anomaly detection and classification to improve detection prospects for gamma-ray bursts with upcoming observatories.

## Contribution

It presents a novel, data-driven deep learning framework that combines anomaly detection and classification for unbiased transient detection in astrophysics.

## Key findings

- Compared with existing methods, our approach improves detection sensitivity.
- The framework effectively identifies low-luminosity gamma-ray bursts.
- It offers a versatile tool for multiwavelength and multi-messenger transient searches.

## 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. 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, data-driven approach for multiwavelength and multi-messenger transient detection.