94 citations · 290 across the 23 of their papers we have counts for
4 papers · 1 filter
Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
Jonathan Renusch, Benjamin Huth, Daniel Murnane +9
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task. Kalman filters and linea…
Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Nilotpal Kakati, Daniel Murnane, Baran Hashemi +5
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one model can serve many of those step…
The Landscape of Composite Higgs Models
Daniel Murnane
While the Standard Model (SM) of particle physics contains the most precise set of predictions ever devised by humanity, that precision comes at a cost. The strange nature of the H…
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…