17 citations · 60 across the 31 of their papers we have counts for
5 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…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
WARP Logic Neural Networks
Lino Gerlach, Thore Gerlach, Liv Våge +2
Fast and efficient AI inference is increasingly important, and recent models that directly learn low-level logic operations have achieved state-of-the-art performance. However, exi…
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…
Reconstruction of atmospheric neutrinos in DUNE's horizontal-drift far-detector module
DUNE Collaboration, S. Abbaslu, F. Abd Alrahman +1343
This paper reports on the capabilities in reconstructing and identifying atmospheric neutrino interactions in one of the Deep Underground Neutrino Experiment's (DUNE) far detector…