Informace o publikaci

The simulation framework of the timing-based localization for future all-sky gamma-ray observations with a fleet of CubeSats

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OHNO Masanori WERNER Norbert PÁL András MÉSZÁROS László ICHINOHE Yuto ŘÍPA Jakub TOPINKA Martin MÜNZ Filip GALGÓCZI Gabór FUKAZAWA Yasushi MIZUNO Tsunefumi TAKAHASHI Hiromitsu UCHIDA Nagomi TORIGOE Kento HIRADE Naoyoshi HIROSE Kengo MATAKE Hiroto NAKAZAWA Kazuhiro HISADOMI Syohei ODAKA Hirokazu ENOTO Teruaki HUDEC Ján KAPUS Jakub KOLEDA Martin LASZLO Robert

Rok publikování 2020
Druh Článek ve sborníku
Konference Proc. SPIE 11454, X-Ray, Optical, and Infrared Detectors for Astronomy IX, 114541Z
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Citace
www https://doi.org/10.1117/12.2562253
Doi http://dx.doi.org/10.1117/12.2562253
Klíčová slova GRBs; CubeSats; Machine-Learning
Popis The timing-based localization, which utilize the triangulation principle with the different arrival time of gammaray photons, with a fleet of Cubesats is a unique and powerful solution for the future all-sky gamma-ray observation, which is a key for identification of the electromagnetic counterpart of the gravitational wave sources. The Cubesats Applied for MEasuring and Localising Transients (CAMELOT) mission is now being promoted by the Hungarian and Japanese collaboration with a basic concept of the nine Cubesats constellations in low earth orbit. The simulation framework for estimation of the localization capability has been developed including orbital parameters, an algorithm to estimate the expected observed profile of gamma-ray photons, finding the peak of the cross-correlation function, and a statistical method to find a best-fit position and its uncertainty. It is revealed that a degree-scale localization uncertainty can be achieved by the CAMELOT mission concept for bright short gamma-ray bursts, which could be covered by future large field of view ground-based telescopes. The new approach utilizing machine-learning approach is also investigated to make the procedure automated for the future large scale constellations. The trained neural network with 106 simulated light curves generated by the artificial short burst templates successfully predicts the time-delay of the real light curve and achieves a comparable performance to the cross-correlation algorithm with full automated procedures.
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