Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

2022/03/22 by T. Dorigo, Tommaso Dorigo, Dorigo, Tommaso +92 · 10 citations
Engineering · Materials Science · Medicine · Physics and Astronomy · #Electron and X-Ray Spectroscopy Techniques #Nuclear reactor physics and engineering #Radiation Therapy and Dosimetry #physics.ins-det

paper · pdf · doi:10.48550/arxiv.2203.13818

109 pages, 32 figures. To be submitted to Reviews in Physics

arxiv created 2022/03/22 · arxiv updated 2022/03/29

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, given 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 by means of 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 document 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.

Cited by

• Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector
• Global fits and the search for new physics: past, present and future
• Neural surrogates for designing gravitational wave detectors
• Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics
• Design of an Imaging Air Cherenkov Telescope array layout with differential programming
• Applying reinforcement learning to optical cavity locking tasks: considerations on actor-critic architectures and real-time hardware implementation
• Optimization of a cosmic muon tomography scanner for cargo border control inspection
• Approach to Finding a Robust Deep Learning Model
• Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation
• Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough

Related

• Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming
• Physics Instrument Design with Reinforcement Learning
• End-to-End Optimal Detector Design with Mutual Information Surrogates
• Machine Learning for Complex Instrument Design and Optimization
• New directions for surrogate models and differentiable programming for High Energy Physics detector simulation