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dc.contributor.authorDorigo, Tommaso
dc.contributor.authorGiammanco, Andrea
dc.contributor.authorVischia, Pietro
dc.contributor.authorAehle, Max
dc.contributor.authorBawaj, Mateusz
dc.contributor.authorBoldyrev, Alexey
dc.contributor.authorde Castro Manzano, Pablo
dc.contributor.authorDerkach, Denis
dc.contributor.authorDonini, Julien
dc.contributor.authorEdelen, Auralee
dc.contributor.authorFanzago, Federica
dc.contributor.authorGauger, Nicolas R.
dc.contributor.authorGlaser, Christian
dc.contributor.authorBaydin, Atılım G.
dc.contributor.authorHeinrich, Lukas
dc.contributor.authorKeidel, Ralf
dc.contributor.authorKieseler, Jan
dc.contributor.authorKrause, Claudius
dc.contributor.authorLagrange, Maxime
dc.contributor.authorLamparth, Max
dc.contributor.authorLayer, Lukas
dc.contributor.authorMaier, Gernot
dc.contributor.authorNardi, Federico
dc.contributor.authorPettersen, Helge Egil Seime
dc.contributor.authorRamos, Alberto
dc.contributor.authorRatnikov, Fedor
dc.contributor.authorRöhrich, Dieter
dc.contributor.authorde Austri, Roberto Ruiz
dc.contributor.authordel Árbol, Pablo Martínez Ruiz
dc.contributor.authorSavchenko, Oleg
dc.contributor.authorSimpson, Nathan
dc.contributor.authorStrong, Giles C.
dc.contributor.authorTaliercio, Angela
dc.contributor.authorTosi, Mia
dc.contributor.authorUstyuzhanin, Andrey
dc.contributor.authorZaraket, Haitham
dc.description.abstractThe 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.en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleToward the end-to-end optimization of particle physics instruments with differentiable programmingen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.source.journalReviews in Physicsen_US
dc.relation.projectNorges forskningsråd: 250858en_US
dc.relation.projectNorges forskningsråd: 310713en_US
dc.identifier.citationReviews in Physics. 2023, 10, 100085.en_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal