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Abstract This study evaluated the effects of water stress on rice yield over Punjab and Haryana across North India by integrating Weather Research Forecasting (WRF) and Decision Support System for Agrotechnology Transfer (DSSAT) models. Indian Remote Sensing Satellite datasets were used to define land use/land cover in WRF. The accuracy of simulated rainfall and temperature over Punjab and Haryana was evaluated against Tropical Rainfall Measuring Mission and automated weather station data of Indian Space Research Organization, respectively. Data from WRF was used as weather input to DSSAT to simulate rice yield in Punjab and Haryana for 2009 and 2014. After simulated yield has been evaluated against district-level observed yield, the water balance components within the DSSAT model were used to analyze the impact of water stress on rice yield. The correlation (R 2 ) between the crop water stress factor and the rice yield anomaly at the vegetative and reproductive stage was 0.64 and 0.52 for Haryana and 0.73 and 0.68 for Punjab, respectively. Severe water stress during the flowering to maturity stage inflicted devastating effects on yield. The study concludes that the regional climate simulations can be potentially used for early water stress prediction and its impact on rice yield. 

We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment, utilizing liquid argon time projection chamber (LArTPC) detector technology. Traditional energy estimation approaches in LArTPCs, which largely rely on reconstructing and summing visible energies, often experience sizable biases and resolution smearing because of the complex nature of neutrino interactions and the detector response. The estimation of neutrino energy can be improved after considering the kinematics information of reconstructed final-state particles. Utilizing kinematic information of reconstructed particles, the deep learning-based approach shows improved resolution and reduced bias for the muon neutrino Monte Carlo simulation sample compared to the traditional approach. In order to address the common concern about the effectiveness of this method on experimental data, the RNN-based energy estimator is further examined and validated with dedicated data-simulation consistency tests using MicroBooNE data. We also assess its potential impact on a neutrino oscillation study after accounting for all statistical and systematic uncertainties and show that it enhances physics sensitivity. This method has good potential to improve the performance of other physics analyses.

Published by the American Physical Society2024 

We present a measurement of neutral pion production in charged-current interactions using data recorded with the MicroBooNE detector exposed to Fermilab’s booster neutrino beam. The signal comprises one muon, one neutral pion, any number of nucleons, and no charged pions. Studying neutral pion production in the MicroBooNE detector provides an opportunity to better understand neutrino-argon interactions, and is crucial for future accelerator-based neutrino oscillation experiments. Using a dataset corresponding to$6.86\times {10}^{20}$protons on target, we present single-differential cross sections in muon and neutral pion momenta, scattering angles with respect to the beam for the outgoing muon and neutral pion, as well as the opening angle between the muon and neutral pion. Data extracted cross sections are compared to generator predictions. We report good agreement between the data and the models for scattering angles, except for an over-prediction by generators at muon forward angles. Similarly, the agreement between data and the models as a function of momentum is good, except for an underprediction by generators in the medium momentum ranges, 200–400 MeV for muons and 100–200 MeV for pions.

Published by the American Physical Society2024 

Karyotypic diversity is critical to catalyzing change in the evolution of all plants. By resulting in meiotic incompatibility among sets of homologous chromosomes, polyploidy and aneuploidy may facilitate reproductive isolation and the potential for speciation. Across plants, karyotypic variants in the form of allopolyploids receive greater taxonomic attention relative to autopolyploids and aneuploids. In particular, the prevalence and significance of autopolyploidy and aneuploidy in bryophytes is little understood. Using Fritsch’s 1991 compendium of bryophyte karyotypes with augmentation from karyological studies published since, we have quantified the prevalence of karyotypic variants among ~1500 extant morphological species of mosses. We assessed the phylogenetic distribution of karyological data, the frequency of autopolyploidy and aneuploidy, and the methodological correlates with karyotypic diversity. At least two ploidy levels were recorded from 17% of species potentially increasing current taxonomic diversity of mosses to over 15,000 species. We find that for a given species, the number of unique karyotypes recorded is correlated with the number of populations sampled. The evidence suggests that cytological diversity likely underlies yet undescribed species diversity in mosses, and that intensive karyological sampling is a needed tool for its discovery. 

A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid argon time projection chambers in principle allow the measurement of all final state particles above some detection threshold, undetected neutrons remain a considerable source of missing energy with little to no data constraining their production rates and kinematics. We present the first demonstration of tagging neutrino-induced neutrons in liquid argon time projection chambers using secondary protons emitted from neutron-argon interactions in the MicroBooNE detector. We describe the method developed to identify neutrino-induced neutrons and demonstrate its performance using neutrons produced in muon-neutrino charged current interactions. The method is validated using a small subset of MicroBooNE’s total dataset. The selection yields a sample with$$60\%$$$60\%$of selected tracks corresponding to neutron-induced secondary protons. At this purity, the integrated efficiency is 8.4% for neutrons that produce a detectable proton.

 

We present a novel methodology to search for intranuclear neutron-antineutron transition (n⟶n̅) followed byn̅-nucleon annihilation within an⁴⁰Ar nucleus, using the MicroBooNE liquid argon time projection chamber (LArTPC) detector. A discovery of n⟶n̅transition or a new best limit on the lifetime of this process would either constitute physics beyond the Standard Model or greatly constrain theories of baryogenesis, respectively. The approach presented in this paper makes use of deep learning methods to select n⟶n̅events based on their unique features and differentiate them from cosmogenic backgrounds. The achieved signal and background efficiencies are (70.22 ± 6.04)% and (0.0020 ± 0.0003)%, respectively. A demonstration of a search is performed with a data set corresponding to an exposure of 3.32 ×10²⁶neutron-years, and where the background rate is constrained through direct measurement, assuming the presence of a negligible signal. With this approach, no excess of events over the background prediction is observed, setting a demonstrative lower bound on the n⟶n̅lifetime in⁴⁰Ar of τ_m≳ 1.1×10²⁶years, and on the free n⟶n̅transition time of τ_n⟶n̅≳ 2.6×10⁵s, each at the 90% confidence level. This analysis represents a first-ever proof-of-principle demonstration of the ability to search for this rare process in LArTPCs with high efficiency and low background.