DeepAI AI Chat
Log In Sign Up

Shared Data and Algorithms for Deep Learning in Fundamental Physics

by   Lisa Benato, et al.

We introduce a collection of datasets from fundamental physics research – including particle physics, astroparticle physics, and hadron- and nuclear physics – for supervised machine learning studies. These datasets, containing hadronic top quarks, cosmic-ray induced air showers, phase transitions in hadronic matter, and generator-level histories, are made public to simplify future work on cross-disciplinary machine learning and transfer learning in fundamental physics. Based on these data, we present a simple yet flexible graph-based neural network architecture that can easily be applied to a wide range of supervised learning tasks in these domains. We show that our approach reaches performance close to state-of-the-art dedicated methods on all datasets. 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. Implementations are also provided for our proposed method and all reference algorithms.


page 8

page 10


TensorNetwork: A Library for Physics and Machine Learning

TensorNetwork is an open source library for implementing tensor network ...

TUDataset: A collection of benchmark datasets for learning with graphs

Recently, there has been an increasing interest in (supervised) learning...

Sim-to-Real Domain Adaptation For High Energy Physics

Particle physics or High Energy Physics (HEP) studies the elementary con...

A Class of Models with the Potential to Represent Fundamental Physics

A class of models intended to be as minimal and structureless as possibl...

Lorentz Group Equivariant Neural Network for Particle Physics

We present a neural network architecture that is fully equivariant with ...

Learning new physics efficiently with nonparametric methods

We present a machine learning approach for model-independent new physics...

A transfer learning enhanced the physics-informed neural network model for vortex-induced vibration

Vortex-induced vibration (VIV) is a typical nonlinear fluid-structure in...