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Toward the End−to−End Optimization of Particle Physics Instruments with Differentiable Programming

Tommaso Dorigo‚ Andrea Giammanco‚ Pietro Vischia‚ Max Aehle‚ Mateusz Bawaj‚ Alexey Boldyrev‚ Pablo de Castro Manzano‚ Denis Derkach‚ Julien Donini‚ Auralee Edelen‚ Federica Fanzago‚ Nicolas R. Gauger‚ Christian Glaser‚ Atılım Güneş Baydin‚ Lukas Heinrich‚ Ralf Keidel‚ Jan Kieseler‚ Claudius Krause‚ Maxime Lagrange‚ Max Lamparth‚ Lukas Layer‚ Gernot Maier‚ Federico Nardi‚ Helge E.S. Pettersen‚ Alberto Ramos‚ Fedor Ratnikov‚ Dieter Röhrich‚ Roberto Ruiz de Austri‚ Pablo Martínez Ruiz del Árbol‚ Oleg Savchenko‚ Nathan Simpson‚ Giles C. Strong‚ Angela Taliercio‚ Mia Tosi‚ Andrey Ustyuzhanin and Haitham Zaraket


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.

Reviews in Physics