Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance — from the data-generating processes to their reconstruction and the inference on the parameters of interest — along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism. This report illustrates the most recent advancements in the area, and outlines, for each of the discussed applications as well as for automatic differentiation itself, ongoing and future work.

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Publication Details

Journal
Reviews in Physics
Published
2025-05-02
DOI
https://doi.org/10.1016/j.revip.2025.100120
Citations
1
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
1.87

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article

Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming

Jennet Elizabeth Dickinson, F. Bury, Michael Schenk, Nicolas R. Gauger et al.
1 citations
Reviews in Physics
Particle Detector Development and Performance
1.87
article

Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming

Jennet Elizabeth Dickinson, F. Bury, Michael Schenk, Nicolas R. Gauger, Alexander Demin, M. Doro, Ryan Roussel, R. B. Barreiro, Lisa Kusch, B. S. González, Federico Nardi, S. Cebrián, Fredrik Sandin, ANDREA GIAMMANCO, G. Maier, Marcus Liwicki, Bruno Scarpa, J. Donini, P. Vischia, Max Aehle, Roberto Ruiz de Austri, Lorenzo Arsini, Fedor Ratnikov, Verena Kain, Giles C. Strong, L. Gray, Tommaso Dorigo, J. Kieseler, P. Silva, A. S. Boldyrev, A. Belias
article en
1 citations

Abstract

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance — from the data-generating processes to their reconstruction and the inference on the parameters of interest — along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism. This report illustrates the most recent advancements in the area, and outlines, for each of the discussed applications as well as for automatic differentiation itself, ongoing and future work.

Reviews in PhysicsVol. 13
Karlsruhe Institute of Technology (DE), National Research University Higher School of Economics (RU), Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT), Centre National de la Recherche Scientifique (FR), Fermi National Accelerator Laboratory (US), University of Padua (IT), University of Kaiserslautern (DE), Istituto Nazionale di Fisica Nucleare (IT), GSI Helmholtz Centre for Heavy Ion Research (DE), Luleå University of Technology (SE), Universidad de Zaragoza (ES), LIP - Laboratory of Instrumentation and Experimental Particle Physics (PT), Education and Research Network (IN), University of Bristol (GB), University of Clermont Auvergne (FR), Istituto Nazionale di Fisica Nucleare, Sezione di Padova (IT), Universal Scientific Education and Research Network (IR), Institut National de Physique Nucléaire et de Physique des Particules (FR), Clermont Université (FR), Instituto de Física de Cantabria (ES), Instituto Superior de Tecnologias Avançadas (PT), Istituto Nazionale di Fisica Nucleare, Sezione di Roma I (IT), European Organization for Nuclear Research (CH), University of Applied Sciences Kaiserslautern (DE), Eindhoven University of Technology (NL), Sapienza University of Rome (IT), UCLouvain (BE)
U.S. Department of Energy, Kempe Foundation, European Commission, Kempestiftelserna, National Research University Higher School of Economics, Office of Science, Fundação para a Ciência e a Tecnologia, Agencia Estatal de Investigación, Basic Energy Sciences, High Energy Physics
Openalex Percentile: Top 14%
Particle Detector Development and Performance
1.87
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