131 citations · 174 across the 15 of their papers we have counts for
3 papers · 1 filter
Reconfigurable On-Chip AI for Particle Physics Detectors with Logic Neural Networks
Mila Bileska, Lino Gerlach, Jennet Dickinson +4
On-device machine learning is increasingly important in applications where extreme data rates or resource constraints make centralized processing infeasible. Logic neural networks…
On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
Daniel Abadjiev, Eliza Howard, Tsz Ngong You +35
A 10 TeV Muon Collider is a compelling candidate for a future energy-frontier facility, offering unprecedented opportunities to explore the fundamental laws of particle physics. Mu…
Smartpixels: Towards on-sensor inference of charged particle track parameters and uncertainties
Jennet Dickinson, Rachel Kovach-Fuentes, Lindsey Gray +20
The combinatorics of track seeding has long been a computational bottleneck for triggering and offline computing in High Energy Physics (HEP), and remains so for the HL-LHC. Next-g…