105 citations · 367 across the 31 of their papers we have counts for
12 papers · 1 filter
Machine-learned particle flow as a foundation model for collider physics
Farouk Mokhtar, Joosep Pata, Michael Kagan +1
The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representat…
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…
Neural Scaling Laws for Boosted Jet Tagging
Matthias Vigl, Nicole Hartman, Michael Kagan +1
The success of Large Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and dataset size, is the primary driver of performance i…
The Linear Collider Facility (LCF) at CERN
H. Abramowicz, E. Adli, F. Alharthi +406
In this paper we outline a proposal for a Linear Collider Facility as the next flagship project for CERN. It offers the opportunity for a timely, cost-effective and staged construc…
A Linear Collider Vision for the Future of Particle Physics
H. Abramowicz, E. Adli, F. Alharthi +446
In this paper we review the physics opportunities at linear colliders with a special focus on high centre-of-mass energies and beam polarisation, take a fresh look at the…
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
Farouk Mokhtar, Joosep Pata, Dolores Garcia +4
We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross…