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

arXiv: https://arxiv.org/abs/1911.11779 · HTML: https://mlsanity.com/paper/1911.11779/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 18 of 18)

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- [Interpreting a Machine Learning Model for Detecting Gravitational Waves](https://mlsanity.com/paper/2202.07399/) (2022)
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- [APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics](https://mlsanity.com/paper/2308.07954/) (2023)
- [Machine-Learning Love: classifying the equation of state of neutron stars with Transformers](https://mlsanity.com/paper/2210.08382/) (2022)
- [AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers](https://mlsanity.com/paper/2310.00052/) (2023)
- [NeuralMAG: Fast and Generalizable Micromagnetic Simulation with Deep Neural Nets](https://mlsanity.com/paper/2410.14986/) (2024)
- [CLARGA: Multimodal Graph Representation Learning over Arbitrary Sets of Modalities](https://mlsanity.com/paper/2512.11901/) (2025)

## Abstract (arXiv)

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.