Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

Esfahani, A. Ashtari; Böser, S.; Buzinsky, N.; Cervantes, R.; Claessens, C.; Viveiros, L. de; Fertl, M.; Formaggio, J. A.; Gladstone, L.; Guigue, M.; Heeger, K. M.; Johnston, J.; Jones, A. M.; Kazkaz, K.; LaRoque, B. H.; Lindman, A.; Machado, E.; Monreal, B.; Morrison, E. C.; Nikkel, J. A.; Novitski, E.; Oblath, N. S.; Pettus, W.; Robertson, R. G. H.; Rybka, G.; Saldaña, L.; Sibille, V.; Schram, M.; Slocum, P. L.; Sun, Y-H; Thümmler, T.; VanDevender, B. A.; Weiss, T. E.; Wendler, T.; Zayas, E.

doi:10.1088/1367-2630/ab71bd

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DOE OSTI · 1616702

Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

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Abstract

The Cyclotron Radiation Emission Spectroscopy (CRES) technique pioneered by Project 8 measures cyclotron radiation from individual electrons 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. Here, we develop machine learning models to classify CRES signals with high accuracy based on these traits, improve the resultant frequency spectrum, and offer the potential for a sophisticated analysis which will help Project 8 achieve tritium endpoint measurement in the future.

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73 NUCLEAR PHYSICS AND RADIATION PHYSICS cyclotron radiation machine learning neutrino mass support vector machine

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Esfahani, A. Ashtari, Böser, S., Buzinsky, N., Cervantes, R., Claessens, C., Viveiros, L. de, Fertl, M., Formaggio, J. A., Gladstone, L., Guigue, M., Heeger, K. M., Johnston, J., Jones, A. M., Kazkaz, K., LaRoque, B. H., Lindman, A., Machado, E., Monreal, B., Morrison, E. C., Nikkel, J. A., Novitski, E., Oblath, N. S., Pettus, W., Robertson, R. G. H., Rybka, G., Saldaña, L., Sibille, V., Schram, M., Slocum, P. L., Sun, Y-H, Thümmler, T., VanDevender, B. A., Weiss, T. E., Wendler, T., Zayas, E.. 2020-03-01. Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8. https://doi.org/10.1088/1367-2630%2Fab71bd

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