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The cyclotron radiation emission spectroscopy (CRES) technique pioneered by Project 8 measures electromagnetic radiation from individual electrons gyrating in a background magnetic field to construct a highly precise energy spectrum for beta decay studies and other applications. The detector, magnetic trap geometry and electron dynamics give rise to a multitude of complex electron signal structures which carry information about distinguishing physical traits. With machine learning models, we develop a scheme based on these traits to analyze and classify CRES signals. Proper understanding and use of these traits will be instrumental to improve cyclotron frequency reconstruction and boost the potential of Project 8 to achieve world-leading sensitivity on the tritium endpoint measurement in the future.

The Locust simulation package is a new C++ software tool developed to simulate the measurement of time-varying electromagnetic fields using RF detection techniques. Modularity and flexibility allow for arbitrary input signals, while concurrently supporting tight integration with physics-based simulations as input. External signals driven by the Kassiopeia particle tracking package are discussed, demonstrating conditional feedback between Locust and Kassiopeia during software execution. An application of the simulation to the Project 8 experiment is described. Locust is publicly available at https://github.com/project8/locust_mc.

A 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 ≈ 10 16 atoms / ( cm 2 s ) 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

The KArlsruhe TRItium Neutrino experiment (KATRIN) aims to determine the effective electron (anti)-neutrino mass with a sensitivity of 0.2eV/c 2 by precisely measuring the endpoint region of the tritium β -decay spectrum. It uses a tandem of electrostatic spectrometers working as magnetic adiabatic collimation combined with an electrostatic (MAC-E) filters. In the space between the pre-spectrometer and the main spectrometer, creating a Penning trap is unavoidable when the superconducting magnet between the two spectrometers, biased at their respective nominal potentials, is energized. The electrons accumulated in this trap can lead to discharges, which create additional background electrons and endanger the spectrometer and detector section downstream. To counteract this problem, “electron catchers” were installed in the beamline inside the magnet bore between the two spectrometers. These catchers can be moved across the magnetic-flux tube and intercept on a sub-ms time scale the stored electrons along their magnetron motion paths. In this paper, we report on the design and the successful commissioning of the electron catchers and present results on their efficiency in reducing the experimental background.

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.

The KATRIN experiment aims to measure the effective electron antineutrino mass \\[m_ _e\\] with a sensitivity of \\[0.2\\, eV/ c^2\\] using a gaseous tritium source combined with the MAC-E filter technique. A low background rate is crucial to achieving the proposed sensitivity, and dedicated measurements have been performed to study possible sources of background electrons. In this work, we test the hypothesis that gamma radiation from external radioactive sources significantly increases the rate of background events created in the main spectrometer (MS) and observed in the focal-plane detector. Using detailed simulations of the gamma flux in the experimental hall, combined with a series of experimental tests that artificially increased or decreased the local gamma flux to the MS, we set an upper limit of \\[0.006\\, count/ s\\] (90% C.L.) from this mechanism. Our results indicate the effectiveness of the electrostatic and magnetic shielding used to block secondary electrons emitted from the inner surface of the MS.

The KATRIN experiment aims to determine the effective electron neutrino mass with a sensitivity of \\[0.2 eV/c^2\\] (%90 CL) by precision measurement of the shape of the tritium \\[ \\]-spectrum in the endpoint region. The energy analysis of the decay electrons is achieved by a MAC-E filter spectrometer. A common background source in this setup is the decay of short-lived isotopes, such as \\[^ 219 Rn\\] and \\[^ 220 Rn\\], in the spectrometer volume. Active and passive countermeasures have been implemented and tested at the KATRIN main spectrometer. One of these is the magnetic pulse method, which employs the existing air coil system to reduce the magnetic guiding field in the spectrometer on a short timescale in order to remove low- and high-energy stored electrons. Here we describe the working principle of this method and present results from commissioning measurements at the main spectrometer. Simulations with the particle-tracking software Kassiopeia were carried out to gain a detailed understanding of the electron storage conditions and removal processes.

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.

Anaplasmosis and ehrlichiosis are tick-borne rickettsial diseases that cause significant economic losses in the livestock industry worldwide. Although bovine anaplasmosis is known to be endemic in the Philippines, epidemiological data is fragmented. Moreover, little is known about bovine ehrlichiosis in the country. In this study, the prevalence of Anaplasma marginale and Ehrlichia in cattle and water buffalo from provinces in the southern part of Luzon, Philippines, was investigated through PCR. Blood samples from 620 animals comprised of 512 cattle and 108 water buffalo and 195 tick samples were subjected to nested PCR targeting the groESL gene of Anaplasmataceae. Positive samples were further subjected to another nested PCR and conventional PCR to amplify the A. marginale groEL gene and the Ehrlichia dsbA gene, respectively. Selected A. marginale -positive samples were also subjected to nested PCR targeting the msp5 gene. Regardless of the animal host, the overall prevalence in blood samples obtained was 51.9% for Anaplasmataceae, 43% for A. marginale , and 1.1% for Ehrlichia . No water buffalo were positive for Ehrlichia . Meanwhile, 15.9, 6.7, and 2% of the tick samples, all morphologically identified as Rhipicephalus ( Boophilus ) microplus , were positive for Anaplasmataceae, A. marginale , and Ehrlichia , respectively. Sequence analysis of selected A. marginale msp5 amplicons showed that the isolates from the region share 94–98% identity to reported A. marginale from other countries. The phylogenetic tree showed clustering of isolates in the region and a close relationship with A. marginale isolates from other countries. Sequences of Ehrlichia amplicons from cattle and ticks were 97–100% similar to reported Ehrlichia minasensis isolates. This study showed the high prevalence of A. marginale in Luzon, Philippines, and provided the first molecular evidence of E. minasensis in the country.

We present a working concept for Phase III of the Project 8 experiment, aiming to achieve a neutrino mass sensitivity of 2 eV (90 % C.L.) using a large volume of molecular tritium and a phased antenna array. The detection system is discussed in detail.