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The $(x, y)$ position reconstruction method used in the analysis of the complete exposure of the Large Underground Xenon (LUX) experiment is presented. The algorithm is based on a statistical test that makes use of an iterative method to recover the photomultiplier tube (PMT) light response directly from the calibration data. The light response functions make use of a two dimensional functional form to account for the photons reflected on the inner walls of the detector. To increase the resolution for small pulses, a photon counting technique was employed to describe the response of the PMTs. The reconstruction was assessed with calibration data including ${}^{mathrm{83m}}$Kr (releasing a total energy of 41.5 keV) and ${}^{3}$H ($beta^-$ with Q = 18.6 keV) decays, and a deuterium-deuterium (D-D) neutron beam (2.45 MeV). In the horizontal plane, the reconstruction has achieved an $(x, y)$ position uncertainty of $sigma$= 0.82 cm for events of only 200 electroluminescence photons and $sigma$ = 0.17 cm for 4,000 electroluminescence photons. Such signals are associated with electron recoils of energies $sim$0.25 keV and $sim$10 keV, respectively. The reconstructed position of the smallest events with a single electron emitted from the liquid surface has a horizontal $(x, y)$ uncertainty of 2.13 cm.
Dual-phase noble-gas time projection chambers (TPCs) have improved the sensitivities for dark matter direct search in past decades. The capability of TPCs to reconstruct 3-D vertexes of keV scale recoilings is one of the most advantageous features. I
This work details the development of a three-dimensional (3D) electric field model for the LUX detector. The detector took data during two periods of searching for weakly interacting massive particle (WIMP) searches. After the first period completed,
We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For th
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy
We studied the application of statistical reconstruction algorithms, namely maximum likelihood and least squares methods, to the problem of event reconstruction in a dual phase liquid xenon detector. An iterative method was developed for in-situ reco