Machine Learning in Low-Latency Electromagnetic Counterpart Inference

Explore applications of machine learning in low-latency electromagnetic counterpart inference from gravitational waves in this conference talk. Delve into the challenges and opportunities in multi-messenger astrophysics, focusing on the detection of electromagnetic counterparts to gravitational wave events. Learn about the significance of GW170817, the first observed binary neutron star merger with an electromagnetic counterpart, and understand why such detections remain rare. Discover how machine learning techniques are being applied to both gravitational wave and electromagnetic data for near real-time inference of counterparts. Examine topics such as low-latency data products, physically motivated models, search biases, and supervised classification methods. Gain insights into new techniques, pre-merger alerts, alert brokers, and the importance of contextual information and sky localization in advancing the field of multi-messenger astronomy.

Introduction
Timeline
Highlights
GW17017
Applications of ML
Has remnant
Online searches
Supervised classification
New techniques
Questions
Premerger alerts
Alert brokers
Contextual information
Sky localization