arXiv subjects

P. Detje

Publications and source records attributed to P. Detje.

58 records · Page 4Linked to original sources

arXiv2022

Electromagnetic Shower Reconstruction and Energy Validation with Michel Electrons and $π^0$ Samples for the Deep-Learning-Based Analyses in MicroBooNE

This article presents the reconstruction of the electromagnetic activity from electrons and photons (showers) used in the MicroBooNE deep learning-based low energy electron search. The reconstruction algorithm uses a combination of traditional and deep learning-based techniques to estimate shower energies. We validate these predictions using two $ν_μ$-sourced data samples: charged/neutral current interactions with final state neutral pions and charged current interactions in which the muon stops and decays within the detector producing a Michel electron. Both the neutral pion sample and Michel electron sample demonstrate agreement between data and simulation. Further, the absolute shower energy scale is shown to be consistent with the relevant physical constant of each sample: the neutral pion mass peak and the Michel energy cutoff.

hep-ex↗

arXiv2022

Search for an anomalous excess of charged-current $ν_e$ interactions without pions in the final state with the MicroBooNE experiment

This article presents a measurement of $ν_e$ interactions without pions in the final state using the MicroBooNE experiment and an investigation into the excess of low-energy electromagnetic events observed by the MiniBooNE collaboration. The measurement is performed in exclusive channels with (1$e$N$p$0$π$) and without (1$e$0$p$0$π$) visible final-state protons using 6.86$\times 10^{20}$ protons on target of data collected from the Booster Neutrino Beam at Fermilab. Events are reconstructed with the Pandora pattern recognition toolkit and selected using additional topological information from the MicroBooNE liquid argon time projection chamber. Using a goodness-of-fit test the data are found to be consistent with the predicted number of events with nominal flux and interaction models with a $p$-value of 0.098 in the two channels combined. A model based on the low-energy excess observed in MiniBooNE is introduced to quantify the strength of a possible $ν_e$ excess. The analysis suggests that if an excess is present, it is not consistent with a simple scaling of the $ν_e$ contribution to the flux. Combined, the 1$e$N$p$0$π$ and 1$e$0$p$0$π$ channels do not give a conclusive indication about the tested model, but separately they both disfavor the low-energy excess model at $>$90% CL. The observation in the most sensitive 1$e$N$p$0$π$ channel is below the prediction and consistent with no excess. In the less sensitive 1$e$0$p$0$π$ channel the observation at low energy is above the prediction, while overall there is agreement over the full energy spectrum.

hep-ex↗

arXiv2022

First Measurement of Inclusive Electron-Neutrino and Antineutrino Charged Current Differential Cross Sections in Charged Lepton Energy on Argon in MicroBooNE

We present the first measurement of the single-differential $ν_e + \barν_e$ charged-current inclusive cross sections on argon in electron or positron energy and in electron or positron scattering cosine over the full angular range. Data were collected using the MicroBooNE liquid argon time projection chamber located off-axis from the Fermilab Neutrinos at the Main Injector beam over an exposure of $2.0\times10^{20}$ protons on target. The signal definition includes a 60 MeV threshold on the $ν_e$ or $\barν_e$ energy and a 120 MeV threshold on the electron or positron energy. The measured total and differential cross sections are found to be in agreement with the GENIE, NuWro, and GiBUU neutrino generators.

hep-ex↗

arXiv2021

Wire-Cell 3D Pattern Recognition Techniques for Neutrino Event Reconstruction in Large LArTPCs: Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation

Wire-Cell is a 3D event reconstruction package for liquid argon time projection chambers. Through geometry, time, and drifted charge from multiple readout wire planes, 3D space points with associated charge are reconstructed prior to the pattern recognition stage. Pattern recognition techniques, including track trajectory and $dQ/dx$ (ionization charge per unit length) fitting, 3D neutrino vertex fitting, track and shower separation, particle-level clustering, and particle identification are then applied on these 3D space points as well as the original 2D projection measurements. A deep neural network is developed to enhance the reconstruction of the neutrino interaction vertex. Compared to traditional algorithms, the deep neural network boosts the vertex efficiency by a relative 30\% for charged-current $ν_e$ interactions. This pattern recognition achieves 80-90\% reconstruction efficiencies for primary leptons, after a 65.8\% (72.9\%) vertex efficiency for charged-current $ν_e$ ($ν_μ$) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20\% energy reconstruction resolutions for charged-current neutrino interactions.

physics.ins-det↗