Dr Kirsty Duffy

Abstract:

Obtaining a high-quality interaction model with associated uncertainties is essential for neutrino experiments studying oscillations, nuclear scattering processes, or both. As a primary input to the MicroBooNE experiment’s next generation of neutrino cross section measurements and its flagship investigation of the MiniBooNE low-energy excess, we present a new tune of the charged-current pionless (CC0π) interaction cross section via the two major contributing processes—charged-current quasielastic and multinucleon interaction models—within version 3.0.6 of the GENIE neutrino event generator. Parameters in these models are tuned to muon neutrino CC0π cross section data obtained by the T2K experiment, which provides an independent set of neutrino interactions with a neutrino flux in a similar energy range to MicroBooNE’s neutrino beam. Although the fit is to muon neutrino data, the information carries over to electron neutrino simulation because the same underlying models are used in GENIE. A number of novel fit parameters were developed for this work, and the optimal parameters were chosen from existing and new sets. We choose to fit four parameters that have not previously been constrained by theory or data. Thus, this will be called a theory-driven tune. The result is an improved match to the T2K CC0π data with more well-motivated uncertainties based on the fit.

Abstract:

We present the first measurement of the single-differential νe + ¯νe charged-current inclusive cross sections on argon in electron or positron energy and in electron or positron scattering angle over the full 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 × 1020 protons on target. The signal definition includes a 60 MeV threshold on the νe or ¯νe 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.

Abstract:

We report results from a search for neutrino-induced neutral current (NC) resonant Δ(1232) baryon production followed by Δ radiative decay, with a ⟨0.8⟩  GeV neutrino beam. Data corresponding to MicroBooNE’s first three years of operations (6.80×1020 protons on target) are used to select single-photon events with one or zero protons and without charged leptons in the final state (1γ1p and 1γ0p, respectively). The background is constrained via an in situ high-purity measurement of NC π0 events, made possible via dedicated 2γ1p and 2γ0p selections. A total of 16 and 153 events are observed for the 1γ1p and 1γ0p selections, respectively, compared to a constrained background prediction of 20.5±3.65(syst) and 145.1±13.8(syst) events. The data lead to a bound on an anomalous enhancement of the normalization of NC Δ radiative decay of less than 2.3 times the predicted nominal rate for this process at the 90% confidence level (C.L.). The measurement disfavors a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8% C.L., in favor of the nominal prediction, and represents a greater than 50-fold improvement over the world’s best limit on single-photon production in NC interactions in the sub-GeV neutrino energy range.

Abstract:

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 푑푄/푑푥 (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 휈푒 interactions. This pattern recognition achieves 80-90% reconstruction efficiencies for primary leptons, after a 65.8% (72.9%) vertex efficiency for charged-current 휈푒 (휈휇) interactions. Based on the resulting reconstructed particles and their kinematics, we also achieve 15-20% energy reconstruction resolutions for charged-current neutrino interactions.