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DOE OSTI · 2565215

AI-Driven Detector Design for the EIC (Final Technical Report)

Arratia, Miguel
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Abstract

We developed an optimization workflow based on DNN-based fast-simulation and reconstruction algorithms. We used these methods to advance the design of calorimeter systems for the Electron-Ion Collider (EIC). This DNN-driven optimization provides a blueprint for integrating gradient-based methods into detector-design workflows. All software pipelines and methods have been released publicly and incorporated into the EIC collaboration’s physics studies, broadening their impact. Three journal articles detailing the methods developed here serve as a reference for the design and optimal use of next generation high-granularity calorimeter systems in nuclear and particle physics.

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73 NUCLEAR PHYSICS AND RADIATION PHYSICS

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BibTeXRIS

Arratia, Miguel. 2024-05-01. AI-Driven Detector Design for the EIC (Final Technical Report). https://doi.org/10.2172/2565215

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