Machine learning-based inversion of nuclear responses

Understand

A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the correct interpretation of neutrino oscillation experiments.

• Nuclear quantum Monte Carlo methods infer the nuclear electroweak response functions from their Laplace transforms.
• Inverting the Laplace transform is a notoriously ill-posed problem; and Bayesian techniques, such as maximum entropy, are typically used to reconstruct the original response functions in the quasielastic region.
• In this work, we present a physics-informed artificial neural network architecture suitable for approximating the inverse of the Laplace transform.