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  nazgul: A statistical approach to gamma-ray burst localization - Triangulation via non stationary time series models

Burgess, J. M., Cameron, E., Svinkin, D., & Greiner, J. (2021). nazgul: A statistical approach to gamma-ray burst localization - Triangulation via non stationary time series models. Astronomy and Astrophysics, 654: A26. doi:10.1051/0004-6361/202039461.

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Burgess, J. Michael1, Author              
Cameron, Ewan, Author
Svinkin, Dmitry, Author
Greiner, Jochen1, Author              
Affiliations:
1High Energy Astrophysics, MPI for Extraterrestrial Physics, Max Planck Society, ou_159890              

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 Abstract: Context. Gamma-ray bursts (GRBs) can be located via arrival time signal triangulation using gamma-ray detectors in orbit throughout the solar system. The classical approach based on cross-correlations of binned light curves ignores the Poisson nature of the time series data, and it is unable to model the full complexity of the problem. Aims. We aim to present a statistically proper and robust GRB timing and triangulation algorithm as a modern update to the original procedures used for the interplanetary network. Methods. A hierarchical Bayesian forward model for the unknown temporal signal evolution is learned via random Fourier features and fitted to each detector’s time series data with time differences that correspond to the position GRBs on the sky via the appropriate Poisson likelihood. Results. Our novel method can robustly estimate the position of a GRB as verified via simulations. The uncertainties generated by the method are robust and in many cases more precise compared to the classical method. Thus, we have a method that can become a valuable tool for gravitational wave follow-up.

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Language(s): eng - English
 Dates: 2021-10-06
 Publication Status: Published online
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 Identifiers: DOI: 10.1051/0004-6361/202039461
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Title: Astronomy and Astrophysics
  Other : Astron. Astrophys.
Source Genre: Journal
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Publ. Info: France : EDP Sciences S A
Pages: - Volume / Issue: 654 Sequence Number: A26 Start / End Page: - Identifier: ISSN: 1432-0746
CoNE: https://pure.mpg.de/cone/journals/resource/954922828219_1