MODE2025
8-13 June 2025
Kolymbari, Crete, Greece
Kolymbari, Crete, Greece
This is the fifth installment of a series of workshops where we bring together physicists from particle, astroparticle, and nuclear physics, computer science, and mathematics to develop new methods for experiment design and optimal information extraction from data, powered by differentiable programming.
This initiative stems from the activities of the MODE Collaboration. MODE stands for "Machine-learning Optimized Design of Experiments".
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Gradient-descent-based reconstruction for muon tomography based on automatic differentiation in PyTorch
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Design of an Imaging Air Cherenkov Telescope array layout with differential programming
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Differentiating a HEP Analysis Pipeline within the Scikit-HEP Software Ecosystem
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A Multiple Readout Ultra-High Segmentation Detector Concept For Future Colliders
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Imaging Techniques in Muon Tomography
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Bias Reduction Using Expectation Maximization in the Optimization of an AI-Assisted Muon Tomography System
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Design optimization of hadronic calorimeters for future colliders
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Bringing Automatic Differentiation to CUDA with Compiler-Based Source Transformations
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Optimization pipeline for in-ice radio neutrino detectors
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From Light to Muons: Towards a Unified Framework for Physics-based 3D Scene Reconstruction
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Partial Observability and Domain Randomization in RL-Based Strategy for Optical Cavity Locking Optimization
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When the link to the pdf file is not available, the contribution in question has not yet been accepted for publication.