Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy
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. Glads… 展开作者
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
备注：
submitted to Machine Learning: Science and Technology
发表信息：
Machine Learning: Science and Technology, 5 (2024) 025026
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.
Cyclotron Radiation Signal Characterization in Resonant Cavities for the Project 8 Neutrino Mass Experiment
TRIUMF（特里厄姆夫实验室）；University of Washington（华盛顿大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Johannes Gutenberg University Mainz（美因茨约翰内斯古腾堡大学）；Indiana University（印第安纳大学）
A facility for radiation hardness studies based on a medical cyclotron
高辐射环境下运行仪器的开发是各个研究领域的一项挑战，特别是在粒子物理实验和空间任务中，并推动了对专门用于辐射硬度研究的辐照设施的日益增长的需求。根据不同的应用，在粒子类型、能量和剂量率方面会出现不同的需求。在这篇文章中，我们提出了一个多功能的安装位于伯尔尼大学医院（Inselspital），这是作为一个受控的18 MeV质子源的医用回旋加速器的基础上。该加速器用于医用放射性同位素的日常生产以及多学科研究，这要归功于一条6.5米长的束流传输线，该传输线终止于一个独立的掩体，仅用于科学活动。该设施提供了广泛的质子通量，由于可调束流从约10 pA到微安范围，以及一系列的转向和聚焦磁铁沿束线，使束斑被聚焦到几毫米^2。束线可以配备各种束流监测探测器、准直器和束流测量装置，以精确控制辐照条件。该设施还拥有一个设备齐全的实验室，专门用于辐照后样品的表征。本文报道了质子通量从$5\times10^9$ cm $^{-2}$ s $^{-1}$到$4\times10^{11}$ cm $^{-2}$ s $^{-1}$的辐照装置的实验验证。
