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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.

Experimental efforts searching for dark matter particles over the last few decades have ruled out many candidates led by the new generation of tonne-scale liquid xenon. For light dark matter, hydrogen could be a better target than xenon as it would offer a better kinematic match to the low mass particles. This article describes the HydroX concept, an idea to expand the dark matter sensitivity reach of large liquid xenon detectors by adding hydrogen to the liquid xenon. We discuss the nature of signal generation in liquid xenon to argue that the signal produced at the interaction site by a dark matter–hydrogen interaction could be significantly enhanced over the same interaction on xenon, increasing the sensitivity to the lightest particles. We discuss the technical implications of adding hydrogen to a xenon detector, as well as some background considerations. Finally, we make projections as to the potential sensitivity of a HydroX implementation and discuss next steps. Dark matter searches have faced challenges in detecting lighter particles with traditional xenon detectors. The authors propose the HydroX concept, integrating hydrogen into liquid xenon detectors, enhancing sensitivity to lighter dark matter particles and detection capabilities in the field.

The Argon Dark Matter (ArDM-1t) experiment is a ton-scale liquid argon (LAr) double-phase time projection chamber designed for direct Dark Matter searches. Such a device allows to explore the low energy frontier in LAr with a charge imaging detector. The ionization charge is extracted from the liquid into the gas phase and there amplified by the use of a Large Electron Multiplier in order to reduce the detection threshold. Direct detection of the ionization charge with fine spatial granularity, combined with a measurement of the amplitude and time evolution of the associated primary scintillation light, provide powerful tools for the identification of WIMP interactions against the background due to electrons, photons and possibly neutrons if scattering more than once. A one ton LAr detector is presently installed on surface at CERN to fully test all functionalities and it will be soon moved to an underground location. We will emphasize here the lessons learned from such a device for the design of a large LAr TPC for neutrino oscillation, proton decay and astrophysical neutrinos searches.

Observation of rotational curve of spiral galaxies shows that a large fraction (∼23%) of the mass density of the universe is unaccounted for. Such a significant percentage of missing dark matter suggests that the universe may consist of new types of elementary particles. A compelling explanation for the new particles is the existence of Weakly Interacting Massive Particles (WIMPs), which are non-baryonic particles characterized by particle physics theories beyond the Standard Model. WIMPs are believed to only interact through the weak force and gravity; hence the interaction cross section with ordinary matter is extremely small. Therefore, experimental techniques that combine low radioactivity, low energy thresholds, efficient discrimination against electronic recoil backgrounds, and scalability to large detector masses can only be performed at a deep underground environment where the interference of cosmic rays is obviated. In this paper, we report a cryogenic large liquid xenon detector for dark matter searches at Sanford Lab (Davis Cavern) in the Homestake Mine, USA. The goal of the large underground xenon (LUX) dual-phase detector is to clearly detect (or exclude) WIMPs with a spin independent cross-section per nucleon of 7 × 10−46 cm2, equivalent to ∼0.5 events/100 kg/month in an inner 100 kg fiducial volume (FV) of a 300 kg LXe detector.

LUX-ZEPLIN (LZ) is a second-generation direct dark matter experiment with spin-independent WIMP-nucleon scattering sensitivity above 1.4 × 10 - 48 cm 2 for a WIMP mass of 40 GeV / c 2 and a 1000 days exposure. LZ achieves this sensitivity through a combination of a large 5.6 t fiducial volume, active inner and outer veto systems, and radio-pure construction using materials with inherently low radioactivity content. The LZ collaboration performed an extensive radioassay campaign over a period of six years to inform material selection for construction and provide an input to the experimental background model against which any possible signal excess may be evaluated. The campaign and its results are described in this paper. We present assays of dust and radon daughters depositing on the surface of components as well as cleanliness controls necessary to maintain background expectations through detector construction and assembly. Finally, examples from the campaign to highlight fixed contaminant radioassays for the LZ photomultiplier tubes, quality control and quality assurance procedures through fabrication, radon emanation measurements of major sub-systems, and bespoke detector systems to assay scintillator are presented.

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 \\(\\beta^-\\)-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.

Project 8 has developed a novel technique, Cyclotron Radiation Emission Spectroscopy (CRES), for direct neutrino mass measurements. A CRES-based experiment on the beta spectrum of tritium has been carried out in a small-volume apparatus. We provide a detailed account of the experiment, focusing on systematic effects and analysis techniques. In a Bayesian (frequentist) analysis, we measure the tritium endpoint as \\(18553^{+18}_{-19}\\) (\\(18548^{+19}_{-19}\\)) eV and set upper limits of 155 (152) eV (90% C.L.) on the neutrino mass. No background events are observed beyond the endpoint in 82 days of running. We also demonstrate an energy resolution of \\(1.66\\pm0.19\\) eV in a resolution-optimized magnetic trap configuration by measuring \\(^{83\\rm m}\\)Kr 17.8-keV internal-conversion electrons. These measurements establish CRES as a low-background, high-resolution technique with the potential to advance neutrino mass sensitivity.

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for precision measurement of the energies of charged particles, which is being developed by the Project 8 Collaboration to measure the neutrino mass using tritium beta-decay spectroscopy. Project 8 seeks to use the CRES technique to measure the neutrino mass with a sensitivity of 40~meV, requiring a large supply of tritium atoms stored in a multi-cubic meter detector volume. Antenna arrays are one potential technology compatible with an experiment of this scale, but the capability of an antenna-based CRES experiment to measure the neutrino mass depends on the efficiency of the signal detection algorithms. In this paper, we develop efficiency models for three signal detection algorithms and compare them using simulations from a prototype antenna-based CRES experiment as a case-study. The algorithms include a power threshold, a matched filter template bank, and a neural network based machine learning approach, which are analyzed in terms of their average detection efficiency and relative computational cost. It is found that significant improvements in detection efficiency and, therefore, neutrino mass sensitivity are achievable, with only a moderate increase in computation cost, by utilizing either the matched filter or machine learning approach in place of a power threshold, which is the baseline signal detection algorithm used in previous CRES experiments by Project 8.

Cyclotron Radiation Emission Spectroscopy (CRES) is a technique for measuring the kinetic energy of charged particles through a precision measurement of the frequency of the cyclotron radiation generated by the particle's motion in a magnetic field. The Project 8 collaboration is developing a next-generation neutrino mass measurement experiment based on CRES. One approach is to use a phased antenna array, which surrounds a volume of tritium gas, to detect and measure the cyclotron radiation of the resulting \\(\\beta\\)-decay electrons. To validate the feasibility of this method, Project 8 has designed a test stand to benchmark the performance of an antenna array at reconstructing signals that mimic those of genuine CRES events. To generate synthetic CRES events, a novel probe antenna has been developed, which emits radiation with characteristics similar to the cyclotron radiation produced by charged particles in magnetic fields. This paper outlines the design, construction, and characterization of this Synthetic Cyclotron Antenna (SYNCA). Furthermore, we perform a series of measurements that use the SYNCA to test the position reconstruction capabilities of the digital beamforming reconstruction technique. We find that the SYNCA produces radiation with characteristics closely matching those expected for cyclotron radiation and reproduces experimentally the phenomenology of digital beamforming simulations of true CRES signals.

This report describes the experimental strategy and technologies for XLZD, the next-generation xenon observatory sensitive to dark matter and neutrino physics. In the baseline design, the detector will have an active liquid xenon target of 60 tonnes, which could be increased to 80 tonnes if the market conditions for xenon are favorable. It is based on the mature liquid xenon time projection chamber technology used in current-generation experiments, LZ and XENONnT. The report discusses the baseline design and opportunities for further optimization of the individual detector components. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3 σ evidence potential for WIMP-nucleon cross sections as low as 3 × 10 - 49 c m 2 (at 40 GeV/c 2 WIMP mass). The observatory will also have leading sensitivity to a wide range of alternative dark matter models. It is projected to have a 3 σ observation potential of neutrinoless double beta decay of 136 Xe at a half-life of up to 5.7 × 10 27  years. Additionally, it is sensitive to astrophysical neutrinos from the sun and galactic supernovae.