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

Publication type:
Journal article
Metadata:
Autoren
  • A Ashtari Esfahani
  • S Boeser
  • N Buzinsky
  • R Cervantes
  • C Claessens
  • L de Viveiros
  • M Fertl
  • JA Formaggio
  • L Gladstone
  • M Guigue
  • KM Heeger
  • J Johnston
  • AM Jones
  • K Kazkaz
  • BH LaRoque
  • A Lindman
  • E Machado
  • B Monreal
  • EC Morrison
  • JA Nikkel
  • E Novitski
  • NS Oblath
  • W Pettus
  • RGH Robertson
  • G Rybka
  • L Saldana
  • V Sibille
  • M Schram
  • PL Slocum
  • Y-H Sun
  • T Thuemmler
  • BA VanDevender
  • TE Weiss
  • T Wendler
  • E Zayas
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000518988900001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1088/1367-2630/ab71bd
Externe Identifier
  • Clarivate Analytics Document Solution ID: KT4MM
ISSN
1367-2630
Ausgabe der Veröffentlichung
3
Zeitschrift
NEW JOURNAL OF PHYSICS
Schlüsselwörter
  • neutrino mass
  • cyclotron radiation
  • machine learning
  • support vector machine
Artikelnummer
ARTN 033004
Datum der Veröffentlichung
2020
Status
Published
Titel
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
Sub types
  • Article
Ausgabe der Zeitschrift
22

Data source: Web of Science (Lite)

Other metadata sources:
Abstract
<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>
Autoren
  • A Ashtari Esfahani
  • S Böser
  • N Buzinsky
  • R Cervantes
  • C Claessens
  • L de Viveiros
  • M Fertl
  • JA Formaggio
  • L Gladstone
  • M Guigue
  • KM Heeger
  • J Johnston
  • AM Jones
  • K Kazkaz
  • BH LaRoque
  • A Lindman
  • E Machado
  • B Monreal
  • EC Morrison
  • JA Nikkel
  • E Novitski
  • NS Oblath
  • W Pettus
  • RGH Robertson
  • G Rybka
  • L Saldaña
  • V Sibille
  • M Schram
  • PL Slocum
  • Y-H Sun
  • T Thümmler
  • BA VanDevender
  • TE Weiss
  • T Wendler
  • E Zayas
DOI
10.1088/1367-2630/ab71bd
eISSN
1367-2630
Ausgabe der Veröffentlichung
3
Zeitschrift
New Journal of Physics
Online publication date
2020
Paginierung
033004 - 033004
Datum der Veröffentlichung
2020
Status
Published
Herausgeber
IOP Publishing
Herausgeber URL
http://dx.doi.org/10.1088/1367-2630/ab71bd
Datum der Datenerfassung
2021
Titel
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
Ausgabe der Zeitschrift
22

Data source: Crossref

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. 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.
Autoren
  • A Ashtari Esfahani
  • S Boser
  • N Buzinsky
  • R Cervantes
  • C Claessens
  • L de Viveiros
  • M Fertl
  • JA Formaggio
  • L Gladstone
  • M Guigue
  • KM Heeger
  • J Johnston
  • AM Jones
  • K Kazkaz
  • BH LaRoque
  • A Lindman
  • E Machado
  • B Monreal
  • EC Morrison
  • JA Nikkel
  • E Novitski
  • NS Oblath
  • W Pettus
  • RGH Robertson
  • G Rybka
  • L Saldana
  • V Sibille
  • M Schram
  • PL Slocum
  • YH Sun
  • T Thummler
  • BA VanDevender
  • TE Weiss
  • T Wendler
  • E Zayas
Autoren-URL
http://arxiv.org/abs/1909.08115v2
Zeitschrift
New Journal of Physics, Volume 22, March 2020
Schlüsselwörter
  • nucl-ex
  • nucl-ex
  • hep-ex
Notes
30 pages, 16 figures
Datum der Veröffentlichung
2019
Herausgeber URL
http://dx.doi.org/10.1088/1367-2630/ab71bd
Datum der Datenerfassung
2019
Datum, an dem der Datensatz öffentlich gemacht wurde
2019
Titel
Cyclotron Radiation Emission Spectroscopy Signal Classification with Machine Learning in Project 8

Files

1909.08115v2.pdf

Data source: arXiv

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