California Institute of Technology

 

The detection of the electromagnetic counterparts to both gravitational waves and neutrinos in 2017 have heralded the era of multi-messenger astronomy. These discoveries were only possible due to data in all messengers being broadcast worldwide in real-time and cyberinfrastructure/algorithms for unifying and characterizing them. However, current protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. With increasing sensitivity of detectors in all messengers and the Rubin Observatory's Legacy Survey of Space and Time (LSST) on the horizon, it is critical to replace existing human-centered infrastructure with autonomous systems strategizing and coordinating follow-up to maximize designated science objectives.

In this talk, I describe my efforts to design novel intelligent systems for strategizing optimal resource allocation in real-time. I demonstrate their utility for challenging and exciting use cases, including classification and parameter estimation for supernovae and kilonovae, and preparedness with Zwicky Transient Facility, a pathfinder to Rubin. Finally, I assert that such solutions provide the leading edge necessary to secure access to limited resources, by yielding hard numbers to expected returns, and demonstrating the necessary agility for maximizing returns for difficult and novel science cases.