A3D3 at the APS April Meeting

By Angela Tran
May 2024

A3D3 recently engaged in one of the largest U.S. national physics conferences—the American Physical Society’s April meeting in Sacramento. A variety of A3D3 students presented their talks on institute-related activities.

A3D3 member Ethan Marx from The Massachusetts Institute of Technology brought his passion for data’s potential to bring new discoveries. His APS talk was about applying machine learning (ML) techniques to search for gravitational waves in LIGO-Virgo-Kagra (LVK) data. He says, “Specifically, we validated our algorithm on archival data from the third LVK observing run. The end goal of this work is to deploy this algorithm in real-time in order to issue alerts for electromagnetic astronomers to follow-up. This work is a direct implementation of the institute’s aim for real time processing of large datasets.” In his research, he enjoys the challenge of applying new cutting edge statistical/ML techniques. After being part of A3D3 for about three years, he says he likes that the Institute emphasizes the similarities between seemingly distinct fields.

A3D3 member Will Benoit from The University of Minnesota contributed his enjoyment of how his field probes the behavior of the most extreme universe matter. His APS talk was about demonstrating the production-readiness of a custom AI algorithm for the real-time detection of gravitational waves. He says, “Gravitational waves are one of the messengers in multi-messenger astronomy, and the ability to detect them more quickly will allow other instruments to follow-up more quickly.” In his two and a half years with A3D3, he has especially appreciated the interdisciplinary collaboration with fellow students working on similar technical problems, and seeing how results from different fields can apply to one another.A3D3 member Jared Burleson from The University of Illinois at Urbana-Champaign works in Experimental High Energy Particle Physics to answer the big questions about the fundamental laws of the universe. His APS talk was about the use of machine learning and artificial intelligence for track reconstruction for upgrades to the Large Hadron Collider, which he says “will see a drastic increase in the amount of real-time data gathered. My work aims to utilize AI for processing large data in real time with a focus on discovery in high energy physics.” After about a year with A3D3, he says, “I really enjoy being able to connect with other people in my field and outside my field who are interested in data-driven computing solutions to problems.”

Links to some of the talks presented at APS by A3D3 members are listed below.

• D14.1 Jared Burleson, Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs with detector segmentation and regional processing.
• G13.1 Will Benoit, A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
• D03.2 Ethan Marx, A search for binary mergers in archival LIGO data using aframe, a machine learning detection pipeline
• DD03 Yuan-Tang Chou, NSF HDR ML Anomaly Detection Challenge
• DD03 Haoran Zhao, Graph Neural Network-based Track finding as a Service with ACTS