Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Anomaly detection with convolutional autoencoders is a popular method to search for new
physics in a model-agnostic manner. These techniques are powerful, but they are still a …

The rapidly-developing intersection of machine learning (ML) with high-energy physics
(HEP) presents both opportunities and challenges to our community. Far beyond …

This book has been written as part of my habilitation at the University of Hamburg. It is
intended to serve graduate students and researchers to get familiar with the stateof-the-art of …

A bstract We train a network to identify jets with fractional dark decay (semi-visible jets) using
the pattern of their low-level jet constituents, and explore the nature of the information used …

Choosing which properties of the data to use as input to multivariate decision algorithms—
also known as feature selection—is an important step in solving any problem with machine …

In this paper we present a novel approach to interpretable AI inspired by Quantum Field
Theory (QFT) which we call the NCoder. The NCoder is a modified autoencoder neural …

This review represents a summary of recent work in the application of artificial intelligence
(AI) and machine learning (ML) in nuclear science, covering topics in nuclear theory …

A bstract We study the effectiveness of theoretically-motivated high-level jet observables in
the extreme context of jets with a large number of hard sub-jets (up to N= 8). Previous …

A bstract Embedding symmetries in the architectures of deep neural networks can improve
classification and network convergence in the context of jet substructure. These results hint …