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1. Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

   https://doi.org/10.1088/2632-2153/ad3ee3  

   Ashtari_Esfahani, A; Böser, S; Buzinsky, N; Carmona-Benitez, M C; Cervantes, R; Claessens, C; de_Viveiros, L; Fertl, M; Formaggio, J A; Gaison, J K; et al (May 2024, Machine Learning: Science and Technology)

   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. 

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2. Real-time signal detection for Cyclotron Radiation Emission Spectroscopy measurements using antenna arrays

   https://doi.org/10.1088/1748-0221/19/05/P05073  

   Ashtari_Esfahani, A; Böser, S; Buzinsky, N; Carmona-Benitez, MC; Claessens, C; de_Viveiros, L; Fertl, M; Formaggio, JA; Foust, BT; Gaison, JK; et al (May 2024, Journal of Instrumentation)

   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. 

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3. First Observation of Cyclotron Radiation from MeV-Scale $e^{\pm }$ following Nuclear $\beta$ Decay

   https://doi.org/10.1103/PhysRevLett.131.082502  

   Byron, W; Harrington, H; Taylor, R J; DeGraw, W; Buzinsky, N; Dodson, B; Fertl, M; García, A; Garvey, G; Graner, B; et al (August 2023, Physical Review Letters)

   We present an apparatus for detection of cyclotron radiation yielding a frequency-based β  kinetic energy determination in the 5 keV to 2.1 MeV range, characteristic of nuclear β decays. The cyclotron frequency of the radiating β particles in a magnetic field is used to determine the β energy precisely. Our work establishes the foundation to apply the cyclotron radiation emission spectroscopy (CRES) technique, developed by the Project 8 Collaboration, far beyond the 18-keV tritium endpoint region. We report initial measurements of β−’s from 6He and βþ’s from 19Ne decays to demonstrate the broadband response of our detection system and assess potential systematic uncertainties for β spectroscopy over the full (MeV) energy range. To our knowledge, this is the first direct observation of cyclotron radiation from individual highly relativistic β’s in a waveguide. This work establishes the application of CRES to a variety of nuclei, opening its reach to searches for new physics beyond the TeV scale via precision β-decay measurements. 

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4. Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform

   https://doi.org/10.3390/s23031742  

   Komeylian, Somayeh; Paolini, Christopher (February 2023, Sensors)

   To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%. 

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5. Demonstration of Spatial Multiplexing by Digital Beamforming in 5G Fiber-Wireless Integrated Network

   https://doi.org/10.1364/OFC.2021.Th5E.6  

   S. Su, C. Hsu (July 2021, 2021 Optical Fiber Communication Conference (OFC))

   Multiple data streams sharing the same time-frequency resource block through digital beamforming technique are received at different angles of arrival by a 1-by-4 antenna array and decoded simultaneously in 5G fiber-wireless integrated access network. 

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