Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system.

Still under debate is the question of whether machine learning is capable of going beyond black-box modeling for complex physical systems. We investigate the generalizing and interpretability properties of learning algorithms. To this end, we use supervised and unsupervised learning to infer the phase boundaries of the active Ising model, starting from an ensemble of configurations of the system. We illustrate that unsupervised learning techniques are powerful at identifying the phase boundaries in the control parameter space, even in situations of phase coexistence. It is demonstrated that supervised learning with neural networks is capable of learning the characteristics of the phase diagram, such that the knowledge obtained at a limited set of control variables can be used to determine the phase boundaries across the phase diagram. In this way, we show that properly designed supervised learning provides predictive power to regions in the phase diagram that are not included in the training phase of the algorithm. We stress the importance of introducing interpretability methods in order to perform a physically relevant classification of the phases with deep learning.

Identifying neutrinos and antineutrinos in neutral-current scattering reactions.

We study neutrino-induced nucleon knockout from nuclei. Expressions for the induced polarization are derived within the framework of the independent-nucleon model and the nonrelativistic plane-wave approximation. Large dissimilarities in the nucleon polarization asymmetries are observed between neutrino- and antineutrino-induced processes. These asymmetries represent a potential way to distinguish between neutrinos and antineutrinos in neutral-current neutrino scattering on nuclei. We discuss astrophysical applications of these polarization asymmetries. Our findings are illustrated for neutrino scattering on 16O and 208P b.