6 citations · 12 across the 14 of their papers we have counts for
3 papers · 1 filter
Chopping and distilling variational autoencoders for real-time anomaly detection in high energy physics
Max Cohen, Rajat Gupta, Sterre Hoogendoorn +3
Anomaly detection (AD) has recently emerged as an exciting alternative to conventional search strategies in high energy physics using artificial intelligence (AI) and machine learn…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jennifer Ott, Jenni Ott +129
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers
Deniz Yilmaz, Liangyu Wu, Julia Gonski +2
Drift chambers have long been central to collider tracking, but future machines like a Higgs factory motivate higher granularity and cluster counting for particle ID, posing new da…