End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters

End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters

Recent advances in machine learning have opened new avenues for optimizing detector designs in high-energy physics, where the complex interplay of geometry, materials, and physics processes has traditionally posed a significant challenge. In this work, we introduce the end-to-end. AI Detector Optimi...

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Main Authors: Kylian Schmidt, Krishna Nikhil Kota, Jan Kieseler, Andrea De Vita, Markus Klute, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Joseph Willmore, Pietro Vischia
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Particles
Subjects:
computational modeling
machine learning
diffusion model
calorimeter
particle detector
holistic optimization
Online Access:https://www.mdpi.com/2571-712X/8/2/47
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