@Article{Dorigo_etal2023,
author="Dorigo, T. et al
and Ramos, A.
and Ruiz de Austri, R.",
title="Toward the end-to-end optimization of particle physics instruments with differentiable programming",
journal="Reviews in Physics",
year="2023",
volume="10",
pages="100085 - pp",
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, {\textquoteleft}{\textquoteleft}experience-driven{\textquoteright}{\textquoteright} 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.",
optnote="exported from refbase (https://references.ific.uv.es/refbase/show.php?record=6096), last updated on Fri, 26 Apr 2024 16:06:57 +0000",
doi="10.1016/j.revip.2023.100085",
opturl="https://arxiv.org/abs/2203.13818",
opturl="https://doi.org/10.1016/j.revip.2023.100085"
}