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AbstractA calorimetric detector for minimally disruptive measurements of atomic hydrogen beams is described. The calorimeter measures heat released by the recombination of hydrogen atoms into molecules on a thin wire. As a demonstration, the angular distribution of a beam with a peak intensity of$$\approx 10^{16} \,{\textrm{atoms}}/{(\textrm{cm}^2 \textrm{s})}$$ $\approx {10}^{16}\text{atoms}/\left({\text{cm}}^2\text{s}\right)$is measured by translating the wire across the beam. The data agree well with an analytic model of the beam from the thermal hydrogen atom source. Using the beam shape model, the relative intensity of the beam can be determined to 5% precision or better at any angle. Graphical abstract 

Abstract 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. 

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 tritiumβ⁻-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. 

Abstract 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 β-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. 

Abstract The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60–80 t capable of probing the remaining weakly interacting massive particle-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in¹³⁶Xe using a natural-abundance xenon target. XLZD can reach a 3σdiscovery potential half-life of 5.7 × 10²⁷years (and a 90% CL exclusion of 1.3 × 10²⁸years) with 10 years of data taking, corresponding to a Majorana mass range of 7.3–31.3 meV (4.8–20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community. 

Abstract Xenon dual-phase time projections chambers (TPCs) have proven to be a successful technology in studying physical phenomena that require low-background conditions. With$$40\,\textrm{t}$$ $40\text{t}$of liquid xenon (LXe) in the TPC baseline design, DARWIN will have a high sensitivity for the detection of particle dark matter, neutrinoless double beta decay ($$0\upnu \upbeta \upbeta $$ $0\nu \beta \beta$), and axion-like particles (ALPs). Although cosmic muons are a source of background that cannot be entirely eliminated, they may be greatly diminished by placing the detector deep underground. In this study, we used Monte Carlo simulations to model the cosmogenic background expected for the DARWIN observatory at four underground laboratories: Laboratori Nazionali del Gran Sasso (LNGS), Sanford Underground Research Facility (SURF), Laboratoire Souterrain de Modane (LSM) and SNOLAB. We present here the results of simulations performed to determine the production rate of$${}^{137}$$ ${}^{137}$Xe, the most crucial isotope in the search for$$0\upnu \upbeta \upbeta $$ $0\nu \beta \beta$of$${}^{136}$$ ${}^{136}$Xe. Additionally, we explore the contribution that other muon-induced spallation products, such as other unstable xenon isotopes and tritium, may have on the cosmogenic background.