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Register a new userFirst Observation of Low Energy Electron Neutrinos in a Liquid Argon Time Projection Chamber
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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.
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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 were analyzed to select isolated low-energy depositions in the TPC volume. The total number, reconstructed energies and positions of these depositions have been compared to those from simulations of neutrino-argon interactions using the FLUKA Monte Carlo generator. Measured features are consistent with energy depositions from photons produced by de-excitation of the neutrinos target nucleus and by inelastic scattering of primary neutrons produced by neutrino-argon interactions. This study represents a successful reconstruction of physics at the MeV-scale in a LArTPC, a capability of crucial importance for detection and reconstruction of supernova and solar neutrino interactions in future large LArTPCs.
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 detector consists of a Time Projection Chamber (TPC) with an accompanying photomultiplier tube (PMT) array sealed inside a liquid-argon-filled cryostat. The liquid argon is constantly purified and recirculated in a closed-loop cycle during operation. The specifications and assembly of the detector subsystems and an overview of their performance in a neutron beam are reported.
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 density of central Au+Au collisions at RHIC, a novel design was developed using radial drift in a low diffusion gas. From prototype measurements a 2-track resolution of 1-2 mm is expected.
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 single liquid argon time projection chamber (LArTPC) readout plane. The network extends the single particle identification network previously developed by MicroBooNE. MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNEs deep learning based $ u_e$ search analysis. In this paper, we present the networks design, training, and performance on simulation and data from the MicroBooNE detector.
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