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

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

doi:10.1088/1367-2630/ab71bd

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

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

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

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.

Original language	English (US)
Article number	033004
Journal	New Journal of Physics
Volume	22
Issue number	3
DOIs	https://doi.org/10.1088/1367-2630/ab71bd
State	Published - 2020

All Science Journal Classification (ASJC) codes

General Physics and Astronomy

Access to Document

10.1088/1367-2630/ab71bd

Cite this

Esfahani, A. A., Böser, S., Buzinsky, N., Cervantes, R., Claessens, C., De Viveiros, L., 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., ... Zayas, E. (2020). Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8. New Journal of Physics, 22(3), Article 033004. https://doi.org/10.1088/1367-2630/ab71bd

@article{337b7eb3de024ace8cac9dae17d90d55,

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 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.",

author = "Esfahani, {A. Ashtari} and S. B{\"o}ser and N. Buzinsky and R. Cervantes and C. Claessens and {De Viveiros}, L. and M. Fertl and Formaggio, {J. A.} and L. Gladstone and M. Guigue and Heeger, {K. M.} and J. Johnston and Jones, {A. M.} and K. Kazkaz and Laroque, {B. H.} and A. Lindman and E. Machado and B. Monreal and Morrison, {E. C.} and Nikkel, {J. A.} and E. Novitski and Oblath, {N. S.} and W. Pettus and Robertson, {R. G.H.} and G. Rybka and L. Saldaña and V. Sibille and M. Schram and Slocum, {P. L.} and Sun, {Y. H.} and T. Th{\"u}mmler and Vandevender, {B. A.} and Weiss, {T. E.} and T. Wendler and E. Zayas",

note = "Publisher Copyright: {\textcopyright} 2020 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft.",

year = "2020",

doi = "10.1088/1367-2630/ab71bd",

language = "English (US)",

volume = "22",

journal = "New Journal of Physics",

issn = "1367-2630",

publisher = "Institute of Physics",

number = "3",

}

Esfahani, AA, Böser, S, Buzinsky, N, Cervantes, R, Claessens, C, De Viveiros, L, Fertl, M, Formaggio, JA, Gladstone, L, Guigue, M, Heeger, KM, Johnston, J, Jones, AM, Kazkaz, K, Laroque, BH, Lindman, A, Machado, E, Monreal, B, Morrison, EC, Nikkel, JA, Novitski, E, Oblath, NS, Pettus, W, Robertson, RGH, Rybka, G, Saldaña, L, Sibille, V, Schram, M, Slocum, PL, Sun, YH, Thümmler, T, Vandevender, BA, Weiss, TE, Wendler, T & Zayas, E 2020, 'Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8', New Journal of Physics, vol. 22, no. 3, 033004. https://doi.org/10.1088/1367-2630/ab71bd

TY - JOUR

T1 - Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

AU - Esfahani, A. Ashtari

AU - Böser, S.

AU - Buzinsky, N.

AU - Cervantes, R.

AU - Claessens, C.

AU - De Viveiros, L.

AU - Fertl, M.

AU - Formaggio, J. A.

AU - Gladstone, L.

AU - Guigue, M.

AU - Heeger, K. M.

AU - Johnston, J.

AU - Jones, A. M.

AU - Kazkaz, K.

AU - Laroque, B. H.

AU - Lindman, A.

AU - Machado, E.

AU - Monreal, B.

AU - Morrison, E. C.

AU - Nikkel, J. A.

AU - Novitski, E.

AU - Oblath, N. S.

AU - Pettus, W.

AU - Robertson, R. G.H.

AU - Rybka, G.

AU - Saldaña, L.

AU - Sibille, V.

AU - Schram, M.

AU - Slocum, P. L.

AU - Sun, Y. H.

AU - Thümmler, T.

AU - Vandevender, B. A.

AU - Weiss, T. E.

AU - Wendler, T.

AU - Zayas, E.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85082399595&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85082399595&partnerID=8YFLogxK

U2 - 10.1088/1367-2630/ab71bd

DO - 10.1088/1367-2630/ab71bd

M3 - Article

AN - SCOPUS:85082399595

SN - 1367-2630

VL - 22

JO - New Journal of Physics

JF - New Journal of Physics

IS - 3

M1 - 033004

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this