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Found 3 papers, 2 papers with code
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.
Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
The generator is constraint during the training such that the generated showers show the expected dependency on the initial energy and the impact position.
Instrumentation and Detectors
Generating and refining particle detector simulations using the Wasserstein distance in adversarial networks
We use adversarial network architectures together with the Wasserstein distance to generate or refine simulated detector data.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment
