154 citations · 929 across the 94 of their papers we have counts for
13 papers · 1 filter
Search for an Anomalous Production of Charged-Current Interactions Without Visible Pions Across Multiple Kinematic Observables in MicroBooNE
MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana +177
This Letter presents an investigation of low-energy electron-neutrino interactions in the Fermilab Booster Neutrino Beam by the MicroBooNE experiment, motivated by the excess of el…
Data-driven model validation for neutrino-nucleus cross section measurements
MicroBooNE collaboration, P. Abratenko, O. Alterkait +184
Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation paramet…
First Measurement of Near- and Sub-Threshold Photoproduction off Nuclei
J. R. Pybus, L. Ehinger, T. Kolar +65
We report on the first measurement of photoproduction from nuclei in the photon energy range of to GeV, extending above and below the photoproduction threshold in…
Demonstration of new MeV-scale capabilities in large neutrino LArTPCs using ambient radiogenic and cosmogenic activity in MicroBooNE
MicroBooNE collaboration, P. Abratenko, O. Alterkait +184
Large neutrino liquid argon time projection chamber (LArTPC) experiments can broaden their physics reach by reconstructing and interpreting MeV-scale energy depositions, or blips,…
Demonstration of neutron identification in neutrino interactions in the MicroBooNE liquid argon time projection chamber
MicroBooNE collaboration, P. Abratenko, O. Alterkait +187
A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid ar…
Improving neutrino energy estimation of charged-current interaction events with recurrent neural networks in MicroBooNE
MicroBooNE collaboration, P. Abratenko, O. Alterkait +186
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 f…