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Using silicon-nitride microresonators with integrated Moiré-Bragg gratings to suppress parasitic nonlinear processes, we demonstrate on-chip frequency conversion to a single idler tone with a record-high 71% efficiency using Bragg scattering four-wave-mixing. 

Cross quadrature correlations are observed from the spectral matrices of two-mode bright states generated in an integrated optical parametric oscillator. We attribute degradation of amplitude difference squeezing with increasing pump intensities to this effect. 

Aqueous electrolytes are the leading candidate to meet the surging demand for safe and low-cost storage batteries. Aqueous electrolytes facilitate more sustainable battery technologies due to the attributes of being nonflammable, environmentally benign, and cost effective. Yet, water’s narrow electrochemical stability window remains the primary bottleneck for the development of high-energy aqueous batteries with long cycle life and infallible safety. Water’s electrolysis leads to either hydrogen evolution reaction (HER) or oxygen evolution reaction (OER), which causes a series of dire consequences, including poor Coulombic efficiency, short device longevity, and safety issues. These are often showstoppers of a new aqueous battery technology besides the low energy density. Prolific progress has been made in the understanding of HER and OER from both catalysis and battery fields. Unfortunately, a systematic review on these advances from a battery chemistry standpoint is lacking. This review provides in-depth discussions on the mechanisms of water electrolysis on electrodes, where we summarize the critical influencing factors applicable for a broad spectrum of aqueous battery systems. Recent progress and existing challenges on suppressing water electrolysis are discussed, and our perspectives on the future development of this field are provided. 

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 

In review. 

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.