skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

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.

Authors:
 [1];  [2];  [3];  [1];  [2];  [4];  [5]; ORCiD logo [3];  [6];  [7];  [8];  [3];  [9];  [10];  [9];  [2];  [1];  [6];  [9];  [8] more »; ORCiD logo [1];  [9];  [1];  [1]; ORCiD logo [1];  [8];  [3];  [9]; ORCiD logo [8];  [6];  [11];  [9];  [3];  [4];  [3] « less
  1. Univ. of Washington, Seattle, WA (United States). Dept. of Physics
  2. Univ. of Mainz (Germany)
  3. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  4. Pennsylvania State Univ., University Park, PA (United States)
  5. Univ. of Washington, Seattle, WA (United States). Dept. of Physics; Univ. of Mainz (Germany)
  6. Case Western Reserve Univ., Cleveland, OH (United States)
  7. Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sorbonne Univ., Paris (France)
  8. Yale Univ., New Haven, CT (United States)
  9. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  10. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  11. Karlsruhe Inst. of Technology (KIT) (Germany)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Nuclear Physics (NP); National Science Foundation (NSF); German Research Foundation (DFG)
OSTI Identifier:
1616702
Report Number(s):
PNNL-SA-146046
Journal ID: ISSN 1367-2630
Grant/Contract Number:  
AC05-76RL01830; SC0011091; SC0019088; FG02-97ER41020; SC0012654; 1205100; 1505678; AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
New Journal of Physics
Additional Journal Information:
Journal Volume: 22; Journal Issue: 3; Journal ID: ISSN 1367-2630
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; neutrino mass; cyclotron radiation; machine learning; support vector machine

Citation Formats

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., and Zayas, E. Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8. United States: N. p., 2020. Web. doi:10.1088/1367-2630/ab71bd.
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. Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8. United States. doi:https://doi.org/10.1088/1367-2630/ab71bd
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., and Zayas, E. Sun . "Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8". United States. doi:https://doi.org/10.1088/1367-2630/ab71bd. https://www.osti.gov/servlets/purl/1616702.
@article{osti_1616702,
title = {Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8},
author = {Esfahani, A. Ashtari and Böser, S. and Buzinsky, N. and Cervantes, R. and Claessens, C. and Viveiros, L. de and Fertl, M. and Formaggio, J. A. and Gladstone, L. and Guigue, M. and Heeger, K. M. and Johnston, J. and Jones, A. M. and Kazkaz, K. and LaRoque, B. H. and Lindman, A. and Machado, E. and Monreal, B. and Morrison, E. C. and Nikkel, J. A. and Novitski, E. and Oblath, N. S. and Pettus, W. and Robertson, R. G. H. and Rybka, G. and Saldaña, L. and Sibille, V. and Schram, M. and Slocum, P. L. and Sun, Y-H and Thümmler, T. and VanDevender, B. A. and Weiss, T. E. and Wendler, T. and Zayas, E.},
abstractNote = {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.},
doi = {10.1088/1367-2630/ab71bd},
journal = {New Journal of Physics},
number = 3,
volume = 22,
place = {United States},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share:

Works referenced in this record:

Electron radiated power in cyclotron radiation emission spectroscopy experiments
journal, May 2019


Support-vector networks
journal, September 1995

  • Cortes, Corinna; Vapnik, Vladimir
  • Machine Learning, Vol. 20, Issue 3
  • DOI: 10.1007/BF00994018

Stan : A Probabilistic Programming Language
journal, January 2017

  • Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.
  • Journal of Statistical Software, Vol. 76, Issue 1
  • DOI: 10.18637/jss.v076.i01

Precision measurement of the conversion electron spectrum of83m Kr with a solenoid retarding spectrometer
journal, March 1992

  • Picard, A.; Backe, H.; Bonn, J.
  • Zeitschrift f�r Physik A Hadrons and Nuclei, Vol. 342, Issue 1
  • DOI: 10.1007/BF01294491

Use of the Hough transformation to detect lines and curves in pictures
journal, January 1972

  • Duda, Richard O.; Hart, Peter E.
  • Communications of the ACM, Vol. 15, Issue 1
  • DOI: 10.1145/361237.361242

LIBSVM: A library for support vector machines
journal, April 2011

  • Chang, Chih-Chung; Lin, Chih-Jen
  • ACM Transactions on Intelligent Systems and Technology, Vol. 2, Issue 3
  • DOI: 10.1145/1961189.1961199

Correspondence of electron spectra from photoionization and nuclear internal conversion
journal, October 1991


ROOT — An object oriented data analysis framework
journal, April 1997

  • Brun, Rene; Rademakers, Fons
  • Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 389, Issue 1-2
  • DOI: 10.1016/S0168-9002(97)00048-X

Single-Electron Detection and Spectroscopy via Relativistic Cyclotron Radiation
journal, April 2015