Enabling real-time multi-messenger astrophysics discoveries with deep learning

TL;DR

This paper discusses how deep learning and advanced cyber-infrastructure can enhance real-time multi-messenger astrophysics, enabling faster detection, analysis, and discovery across various cosmic signals.

Contribution

It provides a comprehensive review of challenges and offers recommendations for integrating scalable machine learning and cyber-infrastructure in multi-messenger astrophysics.

Findings

Highlights the importance of real-time data processing for multi-messenger detection

Recommends scalable machine learning algorithms for astrophysical data analysis

Emphasizes community building for multi-messenger astrophysics advancements

Abstract

Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics.