Nuclear weak decays provide important probes to fundamental symmetries in nature. Implementations are also provided for our proposed method and all reference algorithms.
#Product hunt buckley hyper 60m product code
To simplify adaptation for various problems, we provide easy-to-follow instructions on how graph-based representations of data structures, relevant for fundamental physics, can be constructed and provide code implementations for several of them. We show that our approach reaches performance close to dedicated methods on all datasets. As showcase application, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks. We discuss the design and structure and line out how additional datasets can be submitted for inclusion. 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. The datasets contain hadronic top quarks, cosmic-ray-induced air showers, phase transitions in hadronic matter, and generator-level histories. We introduce a PYTHON package that provides simple 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.
0 kommentar(er)
