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\title{New results in apply of the machine-z method
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\author{\speaker{Istvan Racz}\\
        RCAES HAS Konkoly Observatory
 \\
        E-mail: \email{racz@complex.elte.hu
}}

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\abstract{
  Gamma-ray bursts (GRBs) are the most energetic transients in the far Universe. Several thousands of GRBs have been observed so far but we could measure the distance of only a few hundreds. We studied the parameters of GRBs with available spectroscopic redshift in order to be able to estimate the redshift of those GRBs without a measured redshift. To calculate their distances we applied two machine-learning estimator methods: random forest regressor and XGBoost. For the process we used selected gamma, x-ray and ultraviolet parameters from the the Swift GRB catalog, in which 328 GRBs had measured spectroscopic redshift. We found a significantly higher correlation between the measured and estimated redshift, we have improved the correlation in multiple steps from 0.57 (published by Ukwatta et al., 2016) to 0.67. It seems that both the random forest and the XGBOOST methods give similarly high correlation and for further improvements additional redshift measurements are required.
}

\FullConference{7th Fermi Symposium 2017\\
		15-20 October 2017\\
		Garmisch-Partenkirchen, Germany}


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