1 code implementation • 29 Apr 2024 • Haoxing Du, Claudius Krause, Vinicius Mikuni, Benjamin Nachman, Ian Pang, David Shih
There have been many applications of deep neural networks to detector calibrations and a growing number of studies that propose deep generative models as automated fast detector simulators.
no code implementations • 15 Dec 2023 • Hosein Hashemi, Claudius Krause
Signatures from particle physics detectors are low-level objects encoding the physics of collisions.
no code implementations • 19 May 2023 • Matthew R. Buckley, Claudius Krause, Ian Pang, David Shih
Simulating particle detector response is the single most expensive step in the Large Hadron Collider computational pipeline.
no code implementations • 25 Oct 2022 • Claudius Krause, Ian Pang, David Shih
CaloFlow is a new and promising approach to fast calorimeter simulation based on normalizing flows.
no code implementations • 15 Mar 2022 • Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
2 code implementations • 21 Oct 2021 • Claudius Krause, David Shih
Recently, we introduced CaloFlow, a high-fidelity generative model for GEANT4 calorimeter shower emulation based on normalizing flows.
2 code implementations • 9 Jun 2021 • Claudius Krause, David Shih
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows.
1 code implementation • 15 Jan 2020 • Christina Gao, Joshua Isaacson, Claudius Krause
We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows.