We present a detailed study of a continuum random-phase approximation approach to quasielastic electron-nucleus and neutrino-nucleus scattering. We compare the $\left(e,e\prime \right)$ cross-section predictions with electron scattering data for the nuclear targets ${}^{12}\mathrm{C}$, ${}^{16}\mathrm{O}$, and ${}^{40}\mathrm{Ca}$, in the kinematic region where quasielastic scattering is expected to dominate. We examine the longitudinal and transverse contributions to ${}^{12}\mathrm{C}\left(e,e\prime \right)$ and compare them with the available data. We find an overall satisfactory description of the $\left(e,e\prime \right)$ data. Further, we study the ${}^{12}\mathrm{C}\left({\nu }_{\mu },{\mu }^{-}\right)$ cross sections relevant for accelerator-based neutrino-oscillation experiments. We pay special attention to low-energy excitations which can account for non-negligible contributions in measurements, and require a beyond-Fermi-gas formalism.

We calculate coherent elastic neutrino-nucleus scattering cross sections on spin-0 nuclei (e.g. ⁴⁰Ar and ²⁸Si) at energies below 100 MeV within the Standard Model and account for all effects of permille size. We provide a complete error budget including uncertainties at nuclear, nucleon, hadronic, and quark levels separately as well as perturbative error. Our calculation starts from the four-fermion effective field theory to explicitly separate heavy-particle mediated corrections (which are absorbed by Wilson coefficients) from light-particle contributions. Electrons and muons running in loops introduce a non- trivial dependence on the momentum transfer due to their relatively light masses. These same loops, and those mediated by tau leptons, break the flavor universality because of mass-dependent electromagnetic radiative corrections. Nuclear physics uncertainties significantly cancel in flavor asymmetries resulting in subpercent relative errors. We find that for low neutrino energies, the cross section can be predicted with a relative precision that is competitive with neutrino-electron scattering. We highlight potentially useful applications of such a precise cross section prediction ranging from precision tests of the Standard Model, to searches for new physics and to the monitoring of nuclear reactors.

We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network’s validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a ${\nu }_{\mu }$ charged-current neutral pion data samples.