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Found 4 papers, 3 papers with code
Improved selective background Monte Carlo simulation at Belle II with graph attention networks and weighted events
When measuring rare processes at Belle II, a huge luminosity is required, which means a large number of simulations are necessary to determine signal efficiencies and background contributions.
Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
Simulating high-resolution detector responses is a storage-costly and computationally intensive process that has long been challenging in particle physics.
PE-GAN: Prior Embedding GAN for PXD images at Belle II
However, data from the fine-grained PXD requires a substantial amount of storage.
Shared Data and Algorithms for Deep Learning in Fundamental Physics
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.
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