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Register a new userCyclotron Radiation Emission Spectroscopy Signal Classification with Machine Learning in Project 8
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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. Understanding and proper use of these traits will be instrumental to improve cyclotron frequency reconstruction and help Project 8 achieve world-leading sensitivity on the tritium endpoint measurement in the future.
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The most sensitive direct method to establish the absolute neutrino mass is observation of the endpoint of the tritium beta-decay spectrum. Cyclotron Radiation Emission Spectroscopy (CRES) is a precision spectrographic technique that can probe much of the unexplored neutrino mass range with $mathcal{O}({rm eV})$ resolution. A lower bound of $m( u_e) gtrsim 9(0.1), {rm meV}$ is set by observations of neutrino oscillations, while the KATRIN Experiment - the current-generation tritium beta-decay experiment that is based on Magnetic Adiabatic Collimation with an Electrostatic (MAC-E) filter - will achieve a sensitivity of $m( u_e) lesssim 0.2,{rm eV}$. The CRES technique aims to avoid the difficulties in scaling up a MAC-E filter-based experiment to achieve a lower mass sensitivity. In this paper we review the current status of the CRES technique and describe Project 8, a phased absolute neutrino mass experiment that has the potential to reach sensitivities down to $m( u_e) lesssim 40,{rm meV}$ using an atomic tritium source.
The recently developed technique of Cyclotron Radiation Emission Spectroscopy (CRES) uses frequency information from the cyclotron motion of an electron in a magnetic bottle to infer its kinetic energy. Here we derive the expected radio frequency signal from an electron in a waveguide CRES apparatus from first principles. We demonstrate that the frequency-domain signal is rich in information about the electrons kinematic parameters, and extract a set of measurables that in a suitably designed system are sufficient for disentangling the electrons kinetic energy from the rest of its kinematic features. This lays the groundwork for high-resolution energy measurements in future CRES experiments, such as the Project 8 neutrino mass measurement.
It has been understood since 1897 that accelerating charges must emit electromagnetic radiation. Cyclotron radiation, the particular form of radiation emitted by an electron orbiting in a magnetic field, was first derived in 1904. Despite the simplicity of this concept, and the enormous utility of electron spectroscopy in nuclear and particle physics, single-electron cyclotron radiation has never been observed directly. Here we demonstrate single-electron detection in a novel radiofrequency spec- trometer. We observe the cyclotron radiation emitted by individual magnetically-trapped electrons that are produced with mildly-relativistic energies by a gaseous radioactive source. The relativistic shift in the cyclotron frequency permits a precise electron energy measurement. Precise beta elec- tron spectroscopy from gaseous radiation sources is a key technique in modern efforts to measure the neutrino mass via the tritium decay endpoint, and this work demonstrates a fundamentally new approach to precision beta spectroscopy for future neutrino mass experiments.
The shape of the beta decay energy distribution is sensitive to the mass of the electron neutrino. Attempts to measure the endpoint shape of tritium decay have so far seen no distortion from the zero-mass form, thus placing an upper limit of m_nu_beta < 2.3 eV. Here we show that a new type of electron energy spectroscopy could improve future measurements of this spectrum and therefore of the neutrino mass. We propose to detect the coherent cyclotron radiation emitted by an energetic electron in a magnetic field. For mildly relativistic electrons, like those in tritium decay, the relativistic shift of the cyclotron frequency allows us to extract the electron energy from the emitted radiation. We present calculations for the energy resolution, noise limits, high-rate measurement capability, and systematic errors expected in such an experiment.
In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine learning algorithms, namely the Multi Layer Perceptron (MLP), trained respectively with the Conjugate Gradient, Scaled Conjugate Gradient and Quasi Newton learning rules, and the Support Vector Machines (SVM), to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs vs non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features we discuss also the behavior of the classifiers on finer AGN classification tasks, namely Seyfert I vs Seyfert II and Seyfert vs LINER. Furthermore we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations.
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