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Abstract Scattering of high energy particles from nucleons probes their structure, as was done in the experiments that established the non-zero size of the proton using electron beams 1 . The use of charged leptons as scattering probes enables measuring the distribution of electric charges, which is encoded in the vector form factors of the nucleon 2 . Scattering weakly interacting neutrinos gives the opportunity to measure both vector and axial vector form factors of the nucleon, providing an additional, complementary probe of their structure. The nucleon transition axial form factor, F A , can be measured from neutrino scattering from free nucleons, ν μ n  →  μ − p and $${\bar{\nu }}_{\mu }p\to {\mu }^{+}n$$ ν ¯ μ p → μ + n , as a function of the negative four-momentum transfer squared ( Q 2 ). Up to now, F A ( Q 2 ) has been extracted from the bound nucleons in neutrino–deuterium scattering 3–9 , which requires uncertain nuclear corrections 10 . Here we report the first high-statistics measurement, to our knowledge, of the $${\bar{\nu }}_{\mu }\,p\to {\mu }^{+}n$$ ν ¯ μ p → μ + n cross-section from the hydrogen atom, using the plastic scintillator target of the MINERvA 11 experiment, extracting F A from free proton targets and measuring the nucleon axial charge radius, r A , to be 0.73 ± 0.17 fm. The antineutrino–hydrogen scattering presented here can access the axial form factor without the need for nuclear theory corrections, and enables direct comparisons with the increasingly precise lattice quantum chromodynamics computations 12–15 . Finally, the tools developed for this analysis and the result presented are substantial advancements in our capabilities to understand the nucleon structure in the weak sector, and also help the current and future neutrino oscillation experiments 16–20 to better constrain neutrino interaction models. 

Abstract We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time. 

Recent GPS studies show that the Indo-Burma subduction system is locked with the implication of a potential large-magnitude earthquake. To inform better seismic hazard models in the region, we need an improved understanding of the crustal structure and the dynamics of the Indo-Burma subduction system. The Bangladesh-India-Myanmar (BIMA) tripartite project deployed 60 broadband seismometers across the subduction system and have been continuously recording data for ~2 years. In this study, we computed receiver functions from 30 high-quality earthquakes (M≥5.9) with epicentral distances between 30º and 90º recorded by the array. The algorithm utilized ensures the uniqueness of the seismic model and provides an uncertainty estimate of every converted wave amplitude. We stacked all the receiver functions produced at each station along the entire transect to generate a cross-sectional model of the average crustal structure. The level of detail in the image is improved by computing higher frequency receiver functions up to 4 Hz. The results represent some of the strongest constraints on crustal structure across the subduction system. Beneath the Neogene accretionary prism's outer belt, we observe a primary conversion associated with the Ganges Brahmaputra Delta that ranges in depth from ~10 km near the deformation front up to ~12 km at the eastern boundary. From the eastern end of the Neogene accretionary prism to the Sagaing Fault, we image the Indian subducting slab and the Central Myanmar basin. The depth-extent of seismicity associated with the Wadati-Benioff zone is consistent with the locations of primary conversions from the subducting plate. We further verify the converted phases of the slab by analyzing azimuthal moveout variations. The Central Myanmar basin is roughly bowl-shaped in cross-section with a maximum thickness of ~15 km about halfway between the Kabaw and Sagaing faults. The average crustal thickness beneath the Ganges-Brahmaputra delta is ~20 km, most likely representing a transitional crust formed from thinning of the continental crust intruded and underplated by igneous rocks. In contrast, the average thickness of the continental crust beneath the Central Myanmar basin is ~40 km. Our results provide a baseline model for future geophysical investigations of the Indo-Burma subduction zone.