154 citations · 392 across the 9 of their papers we have counts for
6 papers · 1 filter
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
Joschka Birk, Erik Buhmann, Cedric Ewen +2
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizi…
Full Phase Space Resonant Anomaly Detection
Erik Buhmann, Cedric Ewen, Gregor Kasieczka +3
Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strateg…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…
CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Erik Buhmann, Frank Gaede, Gregor Kasieczka +4
Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) m…
CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Erik Buhmann, Sascha Diefenbacher, Engin Eren +6
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with ge…
EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
Erik Buhmann, Gregor Kasieczka, Jesse Thaler
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generati…