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- W1752601627 abstract "Two classes of GRBs, short and long, have been determined without any doubts, and are usually ascribed to different progenitors, yet these classes overlap for a variety of descriptive parameters. A subsample of 46 long and 22 short Fermi GRBs with estimated Hurst Exponents (HEs), complemented by minimum variability time-scales (MVTS) and durations (T90) is used to perform a supervised Machine Learning (ML) and Monte Carlo (MC) simulation using a Support Vector Machine (SVM) algorithm. It is found that while T90 itself performs very well in distinguishing short and long GRBs, the overall success ratio is higher when the training set is complemented by MVTS and HE. These results may allow to introduce a new (non-linear) parameter that might provide less ambiguous classification of GRBs." @default.
- W1752601627 created "2016-06-24" @default.
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- W1752601627 date "2015-07-17" @default.
- W1752601627 modified "2023-09-27" @default.
- W1752601627 title "Distinguishing short and long Fermi GRBs" @default.
- W1752601627 hasPublicationYear "2015" @default.
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