Open Access
Open access
Reviews in Physics, volume 10, pages 100085

Toward the end-to-end optimization of particle physics instruments with differentiable programming

T. Dorigo 1, 2
A. Giammanco 2, 3
P. Vischia 3, 4
Max Aehle 5
M. Bawaj 6
Pablo de Castro Manzano 1
D Derkach 7
Julien Donini 2, 8
Auralee Edelen 9
F. Fanzago 1
Nicolas R. Gauger 5
C. Glaser 10
Atılım G. Baydin 11
R Keidel 13
J. Kieseler 14
Claudius Krause 15
Maxime Lagrange 3
Max Lamparth 12
Lukas Layer 12
Show full list: 21 authors
Publication typeJournal Article
Publication date2023-06-01
scimago Q1
SJR1.879
CiteScore21.3
Impact factor
ISSN24054283
General Physics and Astronomy
Abstract
The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, “experience-driven” layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this white paper, we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications.

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