دورية أكاديمية
Deep learning based event reconstruction for cyclotron radiation emission spectroscopy
| العنوان: | Deep learning based event reconstruction for cyclotron radiation emission spectroscopy |
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| المؤلفون: | A Ashtari Esfahani, S Böser, N Buzinsky, M C Carmona-Benitez, R Cervantes, C Claessens, L de Viveiros, M Fertl, J A Formaggio, J K Gaison, L Gladstone, M Grando, M Guigue, J Hartse, K M Heeger, X Huyan, A M Jones, K Kazkaz, M Li, A Lindman, A Marsteller, C Matthé, R Mohiuddin, B Monreal, E C Morrison, R Mueller, J A Nikkel, E Novitski, N S Oblath, J I Peña, W Pettus, R Reimann, R G H Robertson, L Saldaña, M Schram, P L Slocum, J Stachurska, Y-H Sun, P T Surukuchi, A B Telles, F Thomas, M Thomas, L A Thorne, T Thümmler, L Tvrznikova, W Van De Pontseele, B A VanDevender, T E Weiss, T Wendler, E Zayas, A Ziegler |
| المصدر: | Machine Learning: Science and Technology, Vol 5, Iss 2, p 025026 (2024) |
| بيانات النشر: | IOP Publishing, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer engineering. Computer hardware LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | neutrino mass, cyclotron radiation, Project 8, machine learning, deep learning, convolutional neural network, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2632-2153 |
| Relation: | https://doaj.org/toc/2632-2153 |
| DOI: | 10.1088/2632-2153/ad3ee3 |
| URL الوصول: | https://doaj.org/article/0c18907a11204332a75d95e50634ce09 |
| رقم الأكسشن: | edsdoj.0c18907a11204332a75d95e50634ce09 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26322153 |
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| DOI: | 10.1088/2632-2153/ad3ee3 |