6533b820fe1ef96bd127a455
RESEARCH PRODUCT
Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8
M. GuigueM. GuigueSebastian BöserJoseph A. FormaggioA. M. JonesKareem KazkazB. H. LaroqueJames NikkelN. S. OblathBenjamin MonrealE. MachadoT. E. WeissE. C. MorrisonP. L. SlocumThomas ThümmlerK. M. HeegerT. WendlerE. ZayasE. NovitskiC. ClaessensMartin FertlMartin FertlWalter C. PettusA. LindmanB.a. VandevenderN. BuzinskyL. GladstoneR. G. H. RobertsonV. SibilleGray RybkaL. SaldañaJ. JohnstonR. CervantesMalachi SchramY. H. SunL. De ViveirosA. Ashtari Esfahanisubject
CyclotronGeneral Physics and AstronomyFOS: Physical sciencesElectronMachine learningcomputer.software_genre01 natural sciencesSignalElectromagnetic radiation010305 fluids & plasmaslaw.inventionHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)lawMagnetic trap0103 physical sciencesddc:530Emission spectrumCyclotron radiationNuclear Experiment (nucl-ex)010306 general physicsNuclear ExperimentPhysicsbusiness.industryPhysicsDetector3. Good healthArtificial intelligencebusinesscomputerdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-09-17 |