10 citations · 27 across the 10 of their papers we have counts for
7 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…
Machine-learning based particle-flow algorithm in CMS
Farouk Mokhtar
The particle-flow (PF) algorithm provides a global event description by reconstructing final-state particles and is central to event reconstruction in CMS. Recently, end-to-end mac…
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…
Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics
Zihan Zhao, Farouk Mokhtar, Raghav Kansal +2
This study introduces an innovative approach to analyzing unlabeled data in high-energy physics (HEP) through the application of self-supervised learning (SSL). Faced with the incr…
FAIR AI Models in High Energy Physics
Javier Duarte, Haoyang Li, Avik Roy +14
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientifi…