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A significant challenge in measurements of neutrino oscillations is reconstructing the incoming neutrino energies. While modern fully-active tracking calorimeters such as liquid argon time projection chambers in principle allow the measurement of all final state particles above some detection threshold, undetected neutrons remain a considerable source of missing energy with little to no data constraining their production rates and kinematics. We present the first demonstration of tagging neutrino-induced neutrons in liquid argon time projection chambers using secondary protons emitted from neutron-argon interactions in the MicroBooNE detector. We describe the method developed to identify neutrino-induced neutrons and demonstrate its performance using neutrons produced in muon-neutrino charged current interactions. The method is validated using a small subset of MicroBooNE’s total dataset. The selection yields a sample with 60 % of selected tracks corresponding to neutron-induced secondary protons. At this purity, the integrated efficiency is 8.4% for neutrons that produce a detectable proton.

Methane and carbon dioxide hydrate crystals were formed on nearly spherical water droplets at 274.6 K and 2,150 kPa or 1,000 kPa above the corresponding three‐phase hydrate equilibrium pressure. Each experiment was performed with two droplets 5 mm and 2.5 mm in diameter or three droplets with a diameter of 2.5 mm. At the higher pressure the water droplets quickly became jagged and exhibited many needlelike or hairlike crystals extruding from the droplet, whereas at the lower pressure the surface was smooth. In almost all experiments, a depression or collapse of the hydrate layer was observed to occur. This collapse was interpreted as evidence of a continuing hydrate formation after the droplet surface was covered by the hydrate layer. The type of hydrate‐forming gas and the size of the droplet was observed not to influence the macroscopic hydrate crystal morphology. The decomposition of the methane and carbon dioxide hydrate layers was also observed. Reformation was also experimented, and the effect of memory on the morphology of hydrate crystal growth was determined.

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations.

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements and provide comparisons to detector simulations.

The adsorption of polyethylene oxide (PEO) on chemi‐thermo‐mechanical pulp (CTMP), clay, and chalk suspensions was investigated at 303 and 343 K. These conditions corresponded to points below and above the critical solution temperature (CST) of a 0.005 mass % polyethylene oxide solution in the presence of 2 mol/L of KCl. The PEO adsorption on CTMP and clay particles was also studied at different initial PEO concentrations up to 50 mg/L. PEO adsorbed onto papermaking furnish particles at both temperatures, but the adsorbed amount was found to be larger above the CST in all systems. At 303 K it increased with the increase in PEO added to the system until it reached a plateau, but at 343 K it increased with the increase in PEO added in all the range of PEO concentrations up to 50 mg/L. The state of aggregation of PEO‐clay and PEO‐chalk suspensions was also studied by monitoring fluctuations in the intensity of light transmitted through the suspension. These measurements indicated a strongly temperature‐dependent aggregation. It was concluded that the entropically driven phase separation leads to enhanced aggregation, which in turn favors the retention of fiber fines and clay filler.

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mis-modeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

We present flux-averaged charged-current \\(\\nu_\\mu\\) cross-section measurements on argon for final states containing exactly one \\(\\pi^\\pm\\) and no other hadrons except nucleons. The analysis uses data from the MicroBooNE experiment in the Booster Neutrino Beam, corresponding to \\(1.11 \\times 10^{21}\\) protons on target. Total and single-differential cross-section measurements are provided within a phase space restricted to muon momenta above 150 MeV, pion momenta above 100 MeV, and muon-pion opening angles smaller than 2.65 rad. Differential cross sections are reported with respect to the scattering angles of the muon and pion relative to the beam direction, their momenta, and their combined opening angle. The differential cross section with respect to muon momentum is based on a subset of selected events with the muon track fully contained in the detector, whereas the cross section with respect to pion momentum is based on a subset of selected events rich in pions that have not hadronically scattered on the argon before coming to rest. The latter has not been measured on argon before. The total cross section is measured as \\((3.75~\\pm~0.07~\\textrm{(stat.)}~\\pm~0.80~\\textrm{(syst.)}) \\times 10^{-38} \\, \\text{cm}^2/\\text{Ar}\\) at a mean energy of approximately 0.8 GeV. Comparisons of the measured cross sections with predictions from multiple neutrino-nucleus interaction generators show good overall agreement, except at very forward muon angles.