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Simulations of expected signal and background of gamma-ray sources by large field-of-view detectors aboard CubeSats

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 Added by Jakub Ripa
 Publication date 2021
  fields Physics
and research's language is English




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In recent years the number of CubeSats (U-class spacecrafts) launched into space has increased exponentially marking the dawn of the nanosatellite technology. In general these satellites have a much smaller mass budget compared to conventional scientific satellites which limits shielding of scientific instruments against direct and indirect radiation in space. In this paper we present a simulation framework to quantify the signal in large field-of-view gamma-ray scintillation detectors of satellites induced by X-ray/gamma-ray transients, by taking into account the response of the detector. Furthermore, we quantify the signal induced by X-ray and particle background sources at a Low-Earth Orbit outside South Atlantic Anomaly and polar regions. Finally, we calculate the signal-to-noise ratio taking into account different energy threshold levels. Our simulation can be used to optimize material composition and predict detectability of various astrophysical sources by CubeSats. We apply the developed simulation to a satellite belonging to a planned CAMELOT CubeSat constellation. This project mainly aims to detect short and long gamma-ray bursts (GRBs) and as a secondary science objective, to detect soft gamma-ray repeaters (SGRs) and terrestrial gamma-ray flashes (TGFs). The simulation includes a detailed computer-aided design (CAD) model of the satellite to take into account the interaction of particles with the material of the satellite as accurately as possible. Results of our simulations predict that CubeSats can complement the large space observatories in high-energy astrophysics for observations of GRBs, SGRs and TGFs. For the detectors planned to be on board of the CAMELOT CubeSats the simulations show that detections with signal-to-noise ratio of at least 9 for median GRB and SGR fluxes are achievable.



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