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1. SYNCA: A Synthetic Cyclotron Antenna for the Project 8 Collaboration

   https://doi.org/10.1088/1748-0221/18/01/P01034  

   Ashtari Esfahani, A.; Böser, S.; Buzinsky, N.; Carmona-Benitez, M.C.; Claessens, C.; de Viveiros, L.; Fertl, M.; Formaggio, J.A.; Gladstone, L.; Grando, M.; et al (January 2023, Journal of Instrumentation)

   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. 

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2. 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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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. ABALONE𝐓𝐌 Photosensors for the IceCube experiment

   Daniel Ferenc, Andrew Chang (January 2020, Nuclear instruments and methods in physics research)

   The ABALONETM Photosensor Technology (U.S. Pat. 9,064,678) is a modern, scalable technology specifically invented for cost-effective mass production, robustness, and high performance. We present the performance of advanced fused-silica ABALONE Photosensors, developed specifically for the potential extension of the IceCube neutrino experiment, and stresstested for 120 days. The resulting performance makes a significant difference: intrinsic gain in the high 10 8 range, total afterpulsing rate of only 5x10-3 ions per photoelectron, sub-nanosecond timing resolution, single-photon sensitivity, and unique radio-purity and UV sensitivity, thanks to the fused silica components—at no additional cost to the assembly process. 

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5. Model-agnostic search for dijet resonances with anomalous jet substructure in proton–proton collisions at $\sqrt{s}$ = 13 TeV

   https://doi.org/10.1088/1361-6633/add762  

   The_CMS_Collaboration (June 2025, Reports on Progress in Physics)

   Abstract This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8–6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. Search regions are obtained by utilizing multivariate machine-learning methods to select jets with anomalous substructure. A collection of complementary anomaly detection methods—based on unsupervised, weakly supervised, and semisupervised algorithms—are used in order to maximize the sensitivity to unknown new physics signatures. These algorithms are applied to data corresponding to an integrated luminosity of 138 fb⁻¹, recorded by the CMS experiment at the LHC, at a center-of-mass energy of 13 TeV. No significant excesses above background expectations are seen. Exclusion limits are derived on the production cross section of benchmark signal models varying in resonance mass, jet mass, and jet substructure. Many of these signatures have not been previously sought, making several of the limits reported on the corresponding benchmark models the first ever. When compared to benchmark inclusive and substructure-based search strategies, the anomaly detection methods are found to significantly enhance the sensitivity to a variety of models. 

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