Article-Journal

Bias Reduction Using Expectation Maximization in the Optimization of an AI-Assisted Muon Tomography System
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Bringing Automatic Differentiation to CUDA with Compiler-Based Source Transformations
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Design optimization of hadronic calorimeters for future colliders
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Development and Explainability of Models for Machine-Learning-Based Reconstruction of Signals in Particle Detectors
Machine learning methods are being introduced at all stages of data reconstruction and analysis in various high-energy physics …
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Differentiable Deep Learning Surrogate Models Applied to the Optimization of the IFMIF-DONES Facility
One of the primary challenges for future nuclear fusion power plants is understanding how neutron irradiation affects reactor …
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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 …
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From Light to Muons: Towards a Unified Framework for Physics-based 3D Scene Reconstruction
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Gradient-descent-based reconstruction for muon tomography based on automatic differentiation in PyTorch
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Hadron Identification Prospects with Granular Calorimeters
In this work we consider the problem of determining the identity of hadrons at high energies based on the topology of their energy …
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Information Field Theory for Two Applications in Astroparticle Physics
Information field theory (IFT) provides a powerful framework for reconstructing continuous fields from noisy and sparse data. Based on …
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