New #openaccess publication #SciPost #Physics
A Lorentz-equivariant transformer for all of the LHC
Johann Brehmer, Víctor Bresó, Pim de Haan, Tilman Plehn, Huilin Qu, Jonas Spinner, Jesse Thaler
SciPost Phys. 19, 108 (2025)
https://scipost.org/SciPostPhys.19.4.108
New #openaccess publication #SciPost #Physics Core
Towards universal unfolding of detector effects in high-energy physics using denoising diffusion probabilistic models
Camila Pazos, Shuchin Aeron, Pierre-Hugues Beauchemin, Vincent Croft, Zhengyan Huan, Martin Klassen, Taritree Wongjirad
SciPost Phys. Core 8, 064 (2025)
https://scipost.org/SciPostPhysCore.8.4.064
New #openaccess publication #SciPost #Physics
Learning the simplicity of scattering amplitudes
Clifford Cheung, Aurélien Dersy, Matthew D. Schwartz
SciPost Phys. 18, 040 (2025)
https://scipost.org/SciPostPhys.18.2.040
New #openaccess publication #SciPost #Physics
Open string stub as an auxiliary string field
Harold Erbin, Atakan Hilmi Firat
SciPost Phys. 17, 044 (2024)
https://scipost.org/SciPostPhys.17.2.044
New #openaccess publication #SciPost #Physics
Goodness of fit by Neyman-Pearson testing
Gaia Grosso, Marco Letizia, Maurizio Pierini, Andrea Wulzer
SciPost Phys. 16, 123 (2024)
https://scipost.org/SciPostPhys.16.5.123
New #openaccess publication #SciPost #Physics
EPiC-GAN: Equivariant point cloud generation for particle jets
Erik Buhmann, Gregor Kasieczka, Jesse Thaler
SciPost Phys. 15, 130 (2023)
https://scipost.org/SciPostPhys.15.4.130
#UH
#CDCS
#MIT
#IAIFI
#BMBF
#DFG
#FriedrichNaumannStiftung
#NSF
#DOE
📢New paper (finally...) out today📢 'EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets' with Erik (#UniHamburg too) and Jesse (#IAIFI)
A short summary below, full paper at https://arxiv.org/abs/2301.08128
One useful way to represent data from particle physics collisions is a point cloud: each collision event is a cloud of points & each point has a position in space (the position of the specific sensor or particle) and some additional features attached (for example the energy)
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.
SciPost Physics
Ben Waber
Gregor Kasieczka