PHINDER EIC Project
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Jan Kieseler
Latest
On the Codesign of Scientific Experiments and Industrial Systems
Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming
Toward the end-to-end optimization of the SWGO array layout
Hadron Identification Prospects with Granular Calorimeters
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
Hadron Identification Prospects with Granular Calorimeters
Neuromorphic Readout for Hadron Calorimeters
TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
Calorimetric Measurement of Multi-TeV Muons via Deep Regression
Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm
Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks
Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper
Toward Machine Learning Optimization of Experimental Design
Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph, and image data
Muon Energy Measurement from Radiative Losses in a Calorimeter for a Collider Detector
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