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2021

Shared Data and Algorithms for Deep Learning in Fundamental Physics

Benato, Lisa, Buhmann, Erik, Erdmann, Martin et al.

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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.

The datasets contain hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories.
While public datasets from multiple fundamental physics disciplines already exist, the common interface and provided reference models simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics.
We discuss the design and structure and line out how additional datasets can be submitted for inclusion.

Shared Data and Algorithms for Deep Learning in Fundamental Physics · Around

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