Kavli Affiliate: George Ricker
| First 5 Authors: Manan Agarwal, Jay Alameda, Jeroen Audenaert, Will Benoit, Damon Beveridge
| Summary:
Modern large-scale physics experiments create datasets with sizes and
streaming rates that can exceed those from industry leaders such as Google
Cloud and Netflix. Fully processing these datasets requires both sufficient
compute power and efficient workflows. Recent advances in Machine Learning (ML)
and Artificial Intelligence (AI) can either improve or replace existing
domain-specific algorithms to increase workflow efficiency. Not only can these
algorithms improve the physics performance of current algorithms, but they can
often be executed more quickly, especially when run on coprocessors such as
GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with
ML at MIT workshop, which brought together researchers from gravitational-wave
physics, multi-messenger astrophysics, and particle physics to discuss and
share current efforts to integrate ML tools into their workflows. The following
white paper highlights examples of algorithms and computing frameworks
discussed during this workshop and summarizes the expected computing needs for
the immediate future of the involved fields.
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