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Trapped Proton Fluxes Estimation Inside the South Atlantic Anomaly Using the NASA AE9/AP9/SPM Radiation Models along the China Seismo-Electromagnetic Satellite Orbit

Authors :
Matteo Martucci
Roberta Sparvoli
Simona Bartocci
Roberto Battiston
William Jerome Burger
Donatella Campana
Luca Carfora
Guido Castellini
Livio Conti
Andrea Contin
Cinzia De Donato
Cristian De Santis
Francesco Maria Follega
Roberto Iuppa
Ignazio Lazzizzera
Nadir Marcelli
Giuseppe Masciantonio
Matteo Mergé
Alberto Oliva
Giuseppe Osteria
Francesco Palma
Federico Palmonari
Beatrice Panico
Alexandra Parmentier
Francesco Perfetto
Piergiorgio Picozza
Mirko Piersanti
Michele Pozzato
Ester Ricci
Marco Ricci
Sergio Bruno Ricciarini
Zouleikha Sahnoun
Valentina Scotti
Alessandro Sotgiu
Vincenzo Vitale
Simona Zoffoli
Paolo Zuccon
Source :
Applied Sciences, Vol 11, Iss 8, p 3465 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

The radiation belts in the Earth’s magnetosphere pose a hazard to satellite systems and spacecraft missions (both manned and unmanned), heavily affecting payload design and resources, thus resulting in an impact on the overall mission performance and final costs. The NASA AE9/AP9/SPM radiation models for energetic electrons, protons, and plasma provide useful information on the near-Earth environment, but they are still incomplete as to some features and, for some energy ranges, their predictions are not based on a statistically sufficient sample of direct measurements. Therefore, it is of the upmost importance to provide new data and direct measurements to improve their output. In this work, the AP9 model is applied to the China Seismo-Electromagnetic Satellite (CSES-01) orbit to estimate the flux of energetic protons over the South Atlantic Anomaly during a short testing period of one day, 1 January 2021. Moreover, a preliminary comparison with proton data obtained from the High-Energy Particle Detector (HEPD) on board CSES-01 is carried out. This estimation will serve as the starting ground for a forthcoming complete data analysis, enabling extensive testing and validation of current theoretical and empirical models.

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.467c73f78ea949a888b2f6df5447f883
Document Type :
article
Full Text :
https://doi.org/10.3390/app11083465