Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Nathalie Soybelman"'
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 4, p 045042 (2024)
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have proven that ML techniques can be successfully applied to the tracking pr
Externí odkaz:
https://doaj.org/article/7545228673d5468b8ef916c08169dd46
Autor:
Nathalie Soybelman, Nilotpal Kakati, Lukas Heinrich, Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Marumi Kado, Jonathan Shlomi
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045036 (2023)
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the large Hadron collider, where observational set-valued data is generated conditional on a set of incoming particles. To accel
Externí odkaz:
https://doaj.org/article/d10a6430beb64bd2b3a183547d0c0963
Publikováno v:
SciPost Physics, Vol 16, Iss 1, p 037 (2024)
While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We use symbolic regression trained on matrix-element information to extract, for instance, optimal LHC
Externí odkaz:
https://doaj.org/article/39d918aab1d548b496383bba9130275f