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

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:

Esfahani, A. Ashtari ^[1]; Böser, S. ^[2]; Buzinsky, N. ^[3]; Cervantes, R. ^[1]; Claessens, C. ^[2]; Viveiros, L. de ^[4]; Fertl, M. ^[5];

^[3]; Gladstone, L. ^[6]; Guigue, M. ^[7]; Heeger, K. M. ^[8]; Johnston, J. ^[3]; Jones, A. M. ^[9]; Kazkaz, K. ^[10]; LaRoque, B. H. ^[9]; Lindman, A. ^[2]; Machado, E. ^[1]; Monreal, B. ^[6]; Morrison, E. C. ^[9]; Nikkel, J. A. ^[8] more »

Univ. of Washington, Seattle, WA (United States). Dept. of Physics
Univ. of Mainz (Germany)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Pennsylvania State Univ., University Park, PA (United States)
Univ. of Washington, Seattle, WA (United States). Dept. of Physics; Univ. of Mainz (Germany)
Case Western Reserve Univ., Cleveland, OH (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Sorbonne Univ., Paris (France)
Yale Univ., New Haven, CT (United States)
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Karlsruhe Inst. of Technology (KIT) (Germany)

Publication Date:: Sun Mar 01 00:00:00 EST 2020

Research Org.:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Nuclear Physics (NP); National Science Foundation (NSF); German Research Foundation (DFG)

OSTI Identifier:: 1616702

Alternate Identifier(s):: OSTI ID: 1836942

Report Number(s):: PNNL-SA-146046; LLNL-JRNL-820155
Journal ID: ISSN 1367-2630; TRN: US2106647

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.

Copy to clipboard


                    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.  https://doi.org/10.1088/1367-2630/ab71bd

Copy to clipboard


                    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.  https://doi.org/10.1088/1367-2630/ab71bd.  https://www.osti.gov/servlets/purl/1616702.

Copy to clipboard


                    
@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         = {Sun Mar 01 00:00:00 EST 2020},

  month        = {Sun Mar 01 00:00:00 EST 2020}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1088/1367-2630/ab71bd

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 5 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

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

Esfahani, A. Ashtari; Bansal, V.; Böser, S.
Physical Review C, Vol. 99, Issue 5
DOI: 10.1103/PhysRevC.99.055501

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

Wark, D.; Bartlett, R.; Bowles, T.
Physical Review Letters, Vol. 67, Issue 17
DOI: 10.1103/PhysRevLett.67.2291

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

Determining the neutrino mass with cyclotron radiation emission spectroscopy—Project 8
journal, March 2017

Esfahani, Ali Ashtari; Asner, David M.; Böser, Sebastian
Journal of Physics G: Nuclear and Particle Physics, Vol. 44, Issue 5
DOI: 10.1088/1361-6471/aa5b4f

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

Asner, D. M.; Bradley, R. F.; de Viveiros, L.
Physical Review Letters, Vol. 114, Issue 16
DOI: 10.1103/PhysRevLett.114.162501

Similar Records in DOE PAGES and OSTI.GOV collections:

Cyclotron radiation emission spectroscopy of electrons from tritium $$β$$ decay and ^83mKr internal conversion

Journal Article Ashtari Esfahani, A. ; Böser, S. ; Buzinsky, N. ; ... - Physical Review. C

Project 8 has developed a novel technique, cyclotron radiation emission spectroscopy (CRES), for direct neutrino mass measurements. A CRES-based experiment on the beta spectrum of tritium has been carried out in a small-volume apparatus. Here, we provide a detailed account of the experiment, focusing on systematic effects and analysis techniques. In a Bayesian (frequentist) analysis, we measure the tritium endpoint asmore »« less
https://doi.org/10.1103/physrevc.109.035503
Determining the neutrino mass with cyclotron radiation emission spectroscopy—Project 8

Journal Article Esfahani, Ali Ashtari ; Asner, David M. ; Böser, Sebastian ; ... - Journal of Physics. G, Nuclear and Particle Physics

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 withmore »« less
Cited by 63
https://doi.org/10.1088/1361-6471/aa5b4f

Full Text Available
Tritium Beta Spectrum Measurement and Neutrino Mass Limit from Cyclotron Radiation Emission Spectroscopy

Journal Article Ashtari Esfahani, A. ; Böser, S. ; Buzinsky, N. ; ... - Physical Review Letters

The absolute scale of the neutrino mass plays a critical role in physics at every scale, from the subatomic to the cosmological. Measurements of the tritium end-point spectrum have provided the most precise direct limit on the neutrino mass scale. In this Letter, we present advances by Project 8 to the cyclotron radiation emission spectroscopy (CRES) technique culminating in the first frequency-based neutrino mass limit. With only a cm³-scale physical detection volume, a limit of m_β < 155 eV/c² (152 eV/c²) is extracted from the background-free measurement of the continuous tritium beta spectrum in a Bayesian (frequentist) analysis. Using ^83mKrmore »« less
https://doi.org/10.1103/PhysRevLett.131.102502
Viterbi decoding of CRES signals in Project 8

Journal Article Ashtari Esfahani, A. ; Bogorad, Z. ; Böser, S. ; ... - New Journal of Physics

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 confirmedmore »« less
https://doi.org/10.1088/1367-2630/ac66f6
First Observation of Cyclotron Radiation from MeV-Scale e ± following Nuclear β Decay

Journal Article Byron, W. ; Harrington, H. ; Taylor, R. J. ; ... - 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 β⁺’s from ¹⁹Ne decays to demonstrate the broadband response of our detection system and assessmore »« less
https://doi.org/10.1103/physrevlett.131.082502

Similar Records

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

Abstract

Citation Formats

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

Stan : A Probabilistic Programming Language journal, January 2017

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

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

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

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

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

Determining the neutrino mass with cyclotron radiation emission spectroscopy—Project 8 journal, March 2017

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

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

Stan : A Probabilistic Programming Language
journal, January 2017

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

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

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

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

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

Determining the neutrino mass with cyclotron radiation emission spectroscopy—Project 8
journal, March 2017

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