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The capabilities of liquid argon time projection chambers (LArTPCs) to reconstruct the spatial and calorimetric information of neutrino events have made them the detectors of choice in a number of experiments, specifically those looking to observe electron neutrino ($ u_e$) appearance. The LArTPC promises excellent background rejection capabilities, especially in this golden channel for both short and long baseline neutrino oscillation experiments. We present the first experimental observation of electron neutrinos and anti-neutrinos in the ArgoNeut LArTPC, in the energy range relevant to DUNE and the Fermilab Short Baseline Neutrino Program. We have selected 37 electron candidate events and 274 gamma candidate events, and measured an 80% purity of electrons based on a topological selection. Additionally, we present a of separation of electrons from gammas using calorimetric energy deposition, demonstrating further separation of electrons from background gammas.
MeV-scale energy depositions by low-energy photons produced in neutrino-argon interactions have been identified and reconstructed in ArgoNeuT liquid argon time projection chamber (LArTPC) data. ArgoNeuT data collected on the NuMI beam at Fermilab wer
In this paper we give a thorough description of a liquid argon time projection chamber designed, built and operated at Yale. We present results from a calibration run where cosmic rays have been observed in the detector, a first in the US.
This manuscript describes the commissioning of the Mini-CAPTAIN liquid argon detector in a neutron beam at the Los Alamos Neutron Science Center (LANSCE), which led to a first measurement of high-energy neutron interactions in argon. The Mini-CAPTAIN
Two cylindrical forward TPC detectors are described which were constructed to extend the phase space coverage of the STAR experiment to the region 2.5 < |eta| < 4.0. For optimal use of the available space and in order to cope with the high track dens
We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $gamma$, $mu^-$, $pi^pm$, and protons in a sin