Differentiable programming for particle physics simulations
Computational Physics
2022-05-11 v1 Data Analysis, Statistics and Probability
Abstract
We describe how to apply adjoint sensitivity methods to backward Monte-Carlo schemes arising from simulations of particles passing through matter. Relying on this, we demonstrate derivative based techniques for solving inverse problems for such systems without approximations to underlying transport dynamics. We are implementing those algorithms for various scenarios within a general purpose differentiable programming C++17 library NOA (github.com/grinisrit/noa).
Cite
@article{arxiv.2108.10245,
title = {Differentiable programming for particle physics simulations},
author = {Roland Grinis},
journal= {arXiv preprint arXiv:2108.10245},
year = {2022}
}
Comments
12 pages, 3 figures, presented at QUARKS online workshops 2021, initial version, comments welcome