New directions for surrogate models and differentiable programming for High Energy Physics detector simulation

Autor:	Adelmann, Andreas, Hopkins, Walter, Kourlitis, Evangelos, Kagan, Michael, Kasieczka, Gregor, Krause, Claudius, Shih, David, Mikuni, Vinicius, Nachman, Benjamin, Pedro, Kevin, Winklehner, Daniel
Rok vydání:	2022
Předmět:	High Energy Physics - Phenomenology Computer Science - Machine Learning High Energy Physics - Experiment Physics - Computational Physics Physics - Instrumentation and Detectors
Druh dokumentu:	Working Paper
Popis:	The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass'). Comment: contribution to Snowmass 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.08806 Zobrazit plný text záznamu View this record from Arxiv