43 citations · 49 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Knowledge is Overrated: A zero-knowledge machine learning and cryptographic hashing-based framework for verifiable, low latency inference at the LHC
Pratik Jawahar, Caterina Doglioni, Maurizio Pierini
Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline per…
Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Laura Boggia, Carlos Cocha, Fotis Giasemis +11
The field of high energy physics (HEP) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionis…
The SMARTHEP European Training Network
James Andrew Gooding, Leon Bozianu, Carlos Cocha Toapaxi +2
Synergies between MAchine learning, Real-Time analysis and Hybrid architectures for efficient Event Processing and decision-making (SMARTHEP) is a European Training Network, traini…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…