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We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
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
Finding unambiguous evidence of dark matter interactions in a particle detector is a main objective of physics research. The liquid argon time projection chamber technique for the detection of Weakly Interacting Massive Particles (WIMP) allows sensit
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.
We present a new technology for the shaping of the electric field in Time Projection Chambers (TPCs) using a carbon-loaded polyimide foil. This technology allows for the minimisation of passive material near the active volume of the TPC and thus is c
The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber with an active volume of $7.2times 6.0times 6.9$ m$^3$. It is installed at the CERN Neutrino Platform in a specially-constructed beam that delivers charged pions, kaons,