3 papers
hep-ex2025
Fast and Precise Track Fitting with Machine Learning
Ryan Miller, Alexander Shmakov, Kyuho Oh +5
Efficient and accurate particle tracking is crucial for measuring Standard Model parameters and searching for new physics. This task consists of two major computational steps: trac…
hep-ph2024
Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
Haoyang Li, Marko Stamenkovic, Alexander Shmakov +12
The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to b…
hep-ex2024
Fast multi-geometry calorimeter simulation with conditional self-attention variational autoencoders
Dylan Smith, Aishik Ghosh, Junze Liu +2
The simulation of detector response is a vital aspect of data analysis in particle physics, but current Monte Carlo methods are computationally expensive. Machine learning methods,…