1.2k citations · 5.1k across the 126 of their papers we have counts for
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First measurement of quasi-elastic baryon production in muon anti-neutrino interactions in the MicroBooNE detector
MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana +183
We present the first measurement of the cross section of Cabibbo-suppressed baryon production, using data collected with the MicroBooNE detector when exposed to the neutrinos f…
The Future of High Energy Physics Software and Computing
V. Daniel Elvira, Steven Gottlieb, Oliver Gutsche +25
Software and Computing (S&C) are essential to all High Energy Physics (HEP) experiments and many theoretical studies. The size and complexity of S&C are now commensurate with that…
Implicit Neural Representation as a Differentiable Surrogate for Photon Propagation in a Monolithic Neutrino Detector
Minjie Lei, Ka Vang Tsang, Sean Gasiorowski +6
Optical photons are used as signal in a wide variety of particle detectors. Modern neutrino experiments employ hundreds to tens of thousands of photon detectors to observe signal f…
First Measurement of Differential Cross Sections for Muon Neutrino Charged Current Interactions on Argon with a Two-proton Final State in the MicroBooNE Detector
MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana +199
We present the first measurement of differential cross sections for charged-current muon neutrino interactions on argon with one muon, two protons, and no pions in the final state.…
First constraints on light sterile neutrino oscillations from combined appearance and disappearance searches with the MicroBooNE detector
MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana +184
We present a search for eV-scale sterile neutrino oscillations in the MicroBooNE liquid argon detector, simultaneously considering all possible appearance and disappearance effects…
Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
Phiala Shanahan, Kazuhiro Terao, Daniel Whiteson
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of…
An Efficient, Scalable IO Framework for Sparse Data: larcv3
Corey Adams, Kazuhiro Terao, Marco Del Tutto +1
Neutrino physics is one of the fundamental areas of research into the origins and properties of the Universe. Many experimental neutrino projects use sophisticated detectors to obs…
Differential cross section measurement of charged current interactions without final-state pions in MicroBooNE
MicroBooNE collaboration, P. Abratenko, J. Anthony +183
In this letter we present the first measurements of an exclusive electron neutrino cross section with the MicroBooNE experiment using data from the Booster Neutrino Beamline at Fer…
Data Science and Machine Learning in Education
Gabriele Benelli, Thomas Y. Chen, Javier Duarte +20
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sop…
Search for long-lived heavy neutral leptons and Higgs portal scalars decaying in the MicroBooNE detector
MicroBooNE collaboration, P. Abratenko, J. Anthony +185
We present a search for long-lived Higgs portal scalars (HPS) and heavy neutral leptons (HNL) decaying in the MicroBooNE liquid-argon time projection chamber. The measurement is pe…
Measurement of neutral current single production on argon with the MicroBooNE detector
MicroBooNE collaboration, P. Abratenko, J. Anthony +192
We report the first measurement of production in neutral current (NC) interactions on argon with average neutrino energy of ~GeV. We use data from the MicroBooNE d…
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Savannah Thais, Paolo Calafiura, Grigorios Chachamis +7
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image dat…
Solving Simulation Systematics in and with AI/ML
Brett Viren, Jin Huang, Yi Huang +5
Training an AI/ML system on simulated data while using that system to infer on data from real detectors introduces a systematic error which is difficult to estimate and in many ana…
Observation of Radon Mitigation in MicroBooNE by a Liquid Argon Filtration System
MicroBooNE collaboration, P. Abratenko, J. Anthony +190
The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative conta…
Adversarial methods to reduce simulation bias in neutrino interaction event filtering at Liquid Argon Time Projection Chambers
Marta Babicz, Saúl Alonso-Monsalve, Stephen Dolan +1
For current and future neutrino oscillation experiments using large Liquid Argon Time Projection Chambers (LAr-TPCs), a key challenge is identifying neutrino interactions from the…
The Double Chooz antineutrino detectors
Double Chooz Collaboration, H. de Kerret, Y. Abe +247
This article describes the setup and performance of the near and far detectors in the Double Chooz experiment. The electron antineutrinos of the Chooz nuclear power plant were meas…
Cosmic ray muon clustering for the MicroBooNE liquid argon time projection chamber using sMask-RCNN
MicroBooNE collaboration, P. Abratenko, R. An +188
In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and appli…