TomOpt：以μ介子断层成像为背景，对粒子探测器的任务和约束感知设计进行差分优化

IF 6.3 2区物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Machine Learning Science and Technology Pub Date : 2024-07-02 DOI:10.1088/2632-2153/ad52e7

Giles C Strong, Maxime Lagrange, Aitor Orio, Anna Bordignon, Florian Bury, Tommaso Dorigo, Andrea Giammanco, Mariam Heikal, Jan Kieseler, Max Lamparth, Pablo Martínez Ruíz del Árbol, Federico Nardi, Pietro Vischia and Haitham Zaraket

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引用次数: 0

摘要

我们介绍了一个名为 "TomOpt "的软件包，该软件包的开发目的是优化通过宇宙射线μ介子散射进行断层扫描的探测器的几何布局和规格。该软件利用可微分编程对μ介子与探测器和扫描体积的相互作用进行建模，推断体积属性，以及执行损耗最小化的优化循环。这样，我们首次展示了粒子物理仪器端到端可微分和推理感知优化。我们研究了该软件在相关基准场景下的性能，并讨论了它的潜在应用。我们的代码可在 Github 上获取（Strong et al 2024，网址：https://github.com/GilesStrong/tomopt）。

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TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography

We describe a software package, TomOpt, developed to optimise the geometrical layout and specifications of detectors designed for tomography by scattering of cosmic-ray muons. The software exploits differentiable programming for the modeling of muon interactions with detectors and scanned volumes, the inference of volume properties, and the optimisation cycle performing the loss minimisation. In doing so, we provide the first demonstration of end-to-end-differentiable and inference-aware optimisation of particle physics instruments. We study the performance of the software on a relevant benchmark scenario and discuss its potential applications. Our code is available on Github (Strong et al 2024 available at: https://github.com/GilesStrong/tomopt).

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来源期刊

Machine Learning Science and Technology Computer Science-Artificial Intelligence

CiteScore

9.10

自引率

4.40%

发文量

审稿时长

5 weeks

期刊介绍： Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.