12 citations · 35 across the 15 of their papers we have counts for
5 papers · 1 filter
Full-event anomaly detection for new physics searches at the Electron-Ion Collider
Dimitrios Athanasakos, Sebastian Grieninger, Hongkai Liu +3
The future Electron-Ion Collider (EIC) will offer new opportunities to search for physics beyond the Standard Model. We study resonant anomaly detection using machine learning and…
Toward an event-level analysis of hadron structure using differential programming
Kevin Braga, Markus Diefenthaler, Steven Goldenberg +7
Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at…
Graph theory inspired anomaly detection at the LHC
Jack Y. Araz, Dimitrios Athanasakos, Mateusz Ploskon +1
Designing model-independent anomaly detection algorithms for analyzing LHC data remains a central challenge in the search for new physics, due to the high dimensionality of collide…
Point cloud-based diffusion models for the Electron-Ion Collider
Jack Y. Araz, Vinicius Mikuni, Felix Ringer +3
At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standar…
Diffusion model approach to simulating electron-proton scattering events
Peter Devlin, Jian-Wei Qiu, Felix Ringer +1
Generative AI is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for…