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Key Points Optical, very high frequency, and low‐frequency observations are combined to analyze the transition from upward to horizontal propagation of initial in‐cloud lightning A drop in the optical blue‐to‐red ratio indicates when the dominant illumination process changes from streamers to likely stepped leader We find for in‐cloud lightning that the upward initial leader and the horizontal stepped leader could be physically different 

Accelerator based neutrino oscillation experiments seek to measure the relative number of electron and muon (anti)neutrinos at different$L/E$values. However high statistics studies of neutrino interactions are almost exclusively measured using muon (anti)neutrinos since the dominant flavor of neutrinos produced by accelerator based beams are of the muon type. This work reports new measurements of electron (anti)neutrinos interactions in hydrocarbon, obtained by strongly suppressing backgrounds initiated by muon flavor (anti)neutrinos. Double differential cross sections as a function of visible energy transfer,$E_{\text{avail}}$, and transverse momentum transfer,$p_T$, or three momentum transfer,$q_3$are presented.

Published by the American Physical Society2024 

Neutron production in antineutrino interactions can lead to bias in energy reconstruction in neutrino oscillation experiments, but these interactions have rarely been studied. MINERvA previously studied neutron production at an average antineutrino energy of ∼3 GeV in 2016 and found deficiencies in leading models. In this paper, the MINERvA 6 GeV average antineutrino energy dataset is shown to have similar disagreements. A measurement of the cross section for an antineutrino to produce two or more neutrons and have low visible energy is presented as an experiment-independent way to explore neutron production modeling. This cross section disagrees with several leading models’ predictions. Neutron modeling techniques from nuclear physics are used to quantify neutron detection uncertainties on this result. 

The organic carbon (Corg) stored in seagrass meadows is globally significant and could be relevant in strategies to mitigate increasing CO2 concentration in the atmosphere. Most of that stored Corg is in the soils that underlie the seagrasses. We explored how seagrass and soil characteristics vary among seagrass meadows across the geographic range of turtlegrass (Thalassia testudinum) with a goal of illuminating the processes controlling soil organic carbon (Corg) storage spanning 23° of latitude. Seagrass abundance (percent cover, biomass, and canopy height) varied by over an order of magnitude across sites, and we found high variability in soil characteristics, with Corg ranging from 0.08 to 12.59% dry weight. Seagrass abundance was a good predictor of the Corg stocks in surficial soils, and the relative importance of seagrass-derived soil Corg increased as abundance increased. These relationships suggest that first-order estimates of surficial soil Corg stocks can be made by measuring seagrass abundance and applying a linear transfer function. The relative availability of the nutrients N and P to support plant growth was also correlated with soil Corg stocks. Stocks were lower at N-limited sites than at P-limited ones, but the importance of seagrass-derived organic matter to soil Corg stocks was not a function of nutrient limitation status. This finding seemed at odds with our observation that labile standard substrates decomposed more slowly at N-limited than at P-limited sites, since even though decomposition rates were 55% lower at N-limited sites, less Corg was accumulating in the soils. The dependence of Corg stocks and decomposition rates on nutrient availability suggests that eutrophication is likely to exert a strong influence on carbon storage in seagrass meadows. 

We present measurements of the cross section for antineutrino charged-current quasielasticlike scattering on hydrocarbon using the medium energy NuMI wide-band neutrino beam peaking at antineutrino energy hE¯νi ∼ 6 GeV. The measurements are presented as a function of the longitudinal momentum (pjj) and transverse momentum (pT) of the final state muon. This work complements our previously reported high statistics measurement in the neutrino channel and extends the previous antineutrino measurement made in a low energy beam at hE¯νi ∼ 3.5 GeV out to pT of 2.5 GeV=c. Current theoretical models do not completely describe the data in this previously unexplored high pT region. The single differential cross section as a function of four-momentum transfer (Q2 QE) now extends to 4 GeV2 with high statistics. The cross section as a function of Q2 QE shows that the tuned simulations developed by the MINERvA Collaboration that agreed well with the low energy beam measurements do not agree as well with the medium energy beam measurements. Newer neutrino interaction models such as the GENIE v3 tunes are better able to simulate the high Q2 QE region. 

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.