6 papers
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…
A study of CMS analysis pipelines through the Integration Challenge
Mohamed Aly, Peter Elmer, Peter Fackeldey +1
The upcoming High-Luminosity Large Hadron Collider (HL-LHC) at CERN will deliver an unprecedented volume of data for High Energy Physics (HEP). This wealth of information offers si…
Hypothesis-awkward: Property-Based Testing Strategies for Awkward Array
Tai Sakuma, Ianna Osborne, Peter Elmer
Hypothesis-awkward is a collection of Hypothesis strategies for Awkward Array. Awkward Array can represent a wide variety of nested, variable-length, mixed-type data. Many tools th…
Bridging the Vendor Gap: Enabling AMD GPU Support for Awkward Array via ROCm/HIP for the HL-LHC Era
Ianna Osborne, Maxym Naumchyk, Tai Sakuma +2
The High-Luminosity LHC (HL-LHC) will demand order-of-magnitude gains in analysis throughput, and increasingly those gains must come from GPUs that are not made by a single vendor.…
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…
Training and Onboarding initiatives in High Energy Physics experiments
S. Hageboeck, A. Reinsvold Hall, N. Skidmore +16
In this paper we document the current analysis software training and onboarding activities in several High Energy Physics (HEP) experiments: ATLAS, CMS, LHCb, Belle II and DUNE. Fa…