Speaker
Abhishikth Mallampalli
(University of Wisconsin Madison (US))
Description
We present our approach to mitigate the Beam-Induced Background(BIB) in a muon collider, leveraging machine learning. We then utilize pruning and quantization-aware training to enable real-time data processing, and demonstrate that we can distinguish BIB energy deposits from physics processes of interest with significant accuracy using FPGAs. Our work is a first proof-of-concept of the ability to distinguish BIB from the physics processes of interest at a muon collider using machine learning.
Authors
Abhishikth Mallampalli
(University of Wisconsin Madison (US))
Sridhara Dasu
(University of Wisconsin Madison (US))
