Liquid xenon particle detectors rely on excellent light collection efficiency for their performance. This depends on the high reflectivity of polytetrafluoroethylene (PTFE) at the xenon scintillation ...wavelength of 178 nm, but the angular dependence of this reflectivity is not well-understood. IBEX is designed to directly measure the angular distribution of xenon scintillation light reflected off PTFE in liquid xenon. These measurements are fully described by a microphysical reflectivity model with few free parameters. Dependence on PTFE type, surface finish, xenon pressure, and wavelength of incident light is explored. Total internal reflection is observed, which results in the dominance of specular over diffuse reflection and a reflectivity near 100% for high angles of incidence.
Abstract
Cyclotron radiation emission spectroscopy (CRES) is a modern approach for determining charged particle energies via high-precision frequency measurements of the emitted cyclotron radiation. ...For CRES experiments with gas within the fiducial volume, signal and noise dynamics can be modelled by a hidden Markov model. We introduce a novel application of the Viterbi algorithm in order to derive informational limits on the optimal detection of cyclotron radiation signals in this class of gas-filled CRES experiments, thereby providing concrete limits from which future reconstruction algorithms, as well as detector designs, can be constrained. The validity of the resultant decision rules is confirmed using both Monte Carlo and Project 8 data.
Abstract The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged ...particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β − -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.
The LUX-ZEPLIN experiment is a dark matter detector centered on a dual-phase xenon time projection chamber operating at the Sanford Underground Research Facility in Lead, South Dakota, USA. This ...Letter reports results from LUX-ZEPLIN's first search for weakly interacting massive particles (WIMPs) with an exposure of 60 live days using a fiducial mass of 5.5 t. A profile-likelihood ratio analysis shows the data to be consistent with a background-only hypothesis, setting new limits on spin-independent WIMP-nucleon, spin-dependent WIMP-neutron, and spin-dependent WIMP-proton cross sections for WIMP masses above 9 GeV/c^{2}. The most stringent limit is set for spin-independent scattering at 36 GeV/c^{2}, rejecting cross sections above 9.2×10^{-48} cm at the 90% confidence level.
PDF
