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相对论质子谱仪:传感器性能、应用及科学综述

The Relativistic Proton Spectrometer: A Review of Sensor Performance, Applications, and Science.

作者信息

Mazur J E, O'Brien T P, Looper M D

机构信息

14745 Lee Road, Chantilly, VA 20151 USA.

出版信息

Space Sci Rev. 2023;219(3):26. doi: 10.1007/s11214-023-00962-2. Epub 2023 Apr 5.

DOI:10.1007/s11214-023-00962-2
PMID:37034006
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10076393/
Abstract

The Relativistic Proton Spectrometer (RPS) on the Van Allen Probes spacecraft was a particle spectrometer designed to measure the flux, angular distribution, and energy spectrum of protons from to . RPS provided new information about the inner Van Allen belt: a nearby region of space that had been relatively unexplored because of the difficulties of making charged particle measurements there and the associated hazards to satellite operations. We met the primary mission objective of providing accurate data for the AP9 radiation specification model at the high energies where there were little to no data prior to the Van Allen Probes mission. Along the way, we were able to demonstrate the long-term stability of parts of the Inner Belt by comparison with short-lived space science missions that operated decades prior to Van Allen Probes. The most significant surprises were the agreement between RPS and some of those historical measurements and the discovery of a trapped population of leptons at the outer edge of the inner belt. This end-of-mission paper summarizes the instrument performance, calibration, data products, and specific science and engineering results, and includes suggestions for future investigations of intense radiation fields like those found within the inner belt.

摘要

范艾伦探测器航天器上的相对论质子谱仪(RPS)是一种粒子谱仪,旨在测量能量从[具体能量区间1]到[具体能量区间2]的质子的通量、角分布和能谱。RPS提供了有关内范艾伦带的新信息:内范艾伦带是一个附近的空间区域,由于在那里进行带电粒子测量存在困难以及对卫星运行有相关危害,此前相对未被充分探索。我们实现了主要任务目标,即在高能区为AP9辐射规范模型提供准确数据,在范艾伦探测器任务之前,该高能区几乎没有数据。在此过程中,通过与在范艾伦探测器之前几十年运行的短期空间科学任务进行比较,我们能够证明内带部分区域的长期稳定性。最显著的意外发现是RPS与一些历史测量结果之间的一致性,以及在内带外边缘发现了一群被俘获的轻子。这篇任务总结论文概述了仪器性能、校准、数据产品以及具体的科学和工程成果,并包括了对未来研究内带这样的强辐射场的建议。

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Machine learning at the energy and intensity frontiers of particle physics.机器学习在粒子物理的能量和强度前沿。
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