489 citations · 1.9k across the 26 of their papers we have counts for
6 papers · 1 filter
Quantum Information meets High-Energy Physics: Input to the update of the European Strategy for Particle Physics
Yoav Afik, Federica Fabbri, Matthew Low +68
Some of the most astonishing and prominent properties of Quantum Mechanics, such as entanglement and Bell nonlocality, have only been studied extensively in dedicated low-energy la…
RODEM Jet Datasets
Knut Zoch, John Andrew Raine, Debajyoti Sengupta +1
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in pa…
-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
John Andrew Raine, Matthew Leigh, Knut Zoch +1
In this work we introduce -Flows, an extension of the -Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations…
Topological Reconstruction of Particle Physics Processes using Graph Neural Networks
Lukas Ehrke, John Andrew Raine, Knut Zoch +2
We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of…
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant +3
In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with tra…
ν-Flows: Conditional Neutrino Regression
Matthew Leigh, John Andrew Raine, Knut Zoch +1
We present -Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep in…