523 citations · 9.3k across the 182 of their papers we have counts for
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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…
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,…
First operation of LArTPC in the stratosphere as an engineering GRAMS balloon flight (eGRAMS)
R. Nakajima, S. Arai, K. Aoyama +60
GRAMS (Gamma-Ray and AntiMatter Survey) is a next-generation balloon/satellite experiment utilizing a LArTPC (Liquid Argon Time Projection Chamber), to simultaneously target astrop…
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…