Deep learning based event reconstruction for cyclotron radiation emission spectroscopy
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- Univ. of Washington, Seattle, WA (United States)
- Johannes Gutenberg Univ., Mainz (Germany)
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Pennsylvania State Univ., University Park, PA (United States)
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Case Western Reserve Univ., Cleveland, OH (United States)
- Yale Univ., New Haven, CT (United States)
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Indiana Univ., Bloomington, IN (United States)
- Karlsruhe Inst. of Technology (KIT) (Germany)
- Univ. of Washington, Seattle, WA (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β--decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Nuclear Physics (NP); USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF)
- Grant/Contract Number:
- AC52-07NA27344; SC0020433; SC0011091; AC05-76RL01830; FG02-97ER41020; SC0012654
- OSTI ID:
- 2396437
- Report Number(s):
- LLNL--JRNL-840805; 1055466
- Journal Information:
- Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 2 Vol. 5; ISSN 2632-2153
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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