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Accounting for Selection Bias and Redshift Evolution in GRB Radio Afterglow Data

by 1,2,3,*,†, 4,†, 5 and 6
National Astronomical Observatory of Japan, 2-21-1 Osawa, Tokyo 181-8588, Japan
Space Science Institute, Boulder, CO 80301, USA
Department of Astronomical Science, School of Physical Sciences, The Graduate University for Advanced Studies, SOKENDAI, Shonankokusaimura, Miura District, Hayama 240-0193, Japan
Department of Astronomy, University of Maryland, College Park, MD 20742, USA
Instituto de Astronomia, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
National Centre for Radio Astrophysics, Tata Institute of Fundamental Research, Pune 411007, India
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Elena Moretti and Francesco Longo
Galaxies 2021, 9(4), 95;
Received: 30 September 2021 / Revised: 30 October 2021 / Accepted: 4 November 2021 / Published: 7 November 2021
(This article belongs to the Special Issue Gamma-Ray Burst Science in 2030)
Gamma-ray Bursts (GRBs) are highly energetic events that can be observed at extremely high redshift. However, inherent bias in GRB data due to selection effects and redshift evolution can significantly skew any subsequent analysis. We correct for important variables related to the GRB emission, such as the burst duration, T90*, the prompt isotropic energy, Eiso, the rest-frame end time of the plateau emission, Ta,radio*, and its correspondent luminosity La,radio, for radio afterglow. In particular, we use the Efron–Petrosian method presented in 1992 for the correction of our variables of interest. Specifically, we correct Eiso and T90* for 80 GRBs, and La,radio and Ta,radio* for a subsample of 18 GRBs that present a plateau-like flattening in their light curve. Upon application of this method, we find strong evolution with redshift in most variables, particularly in La,radio, with values similar to those found in past and current literature in radio, X-ray and optical wavelengths, indicating that these variables are susceptible to observational bias. This analysis emphasizes the necessity of correcting observational data for evolutionary effects to obtain the intrinsic behavior of correlations to use them as discriminators among the most plausible theoretical models and as reliable cosmological tools. View Full-Text
Keywords: GRB; radio; redshift evolution GRB; radio; redshift evolution
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MDPI and ACS Style

Dainotti, M.; Levine, D.; Fraija, N.; Chandra, P. Accounting for Selection Bias and Redshift Evolution in GRB Radio Afterglow Data. Galaxies 2021, 9, 95.

AMA Style

Dainotti M, Levine D, Fraija N, Chandra P. Accounting for Selection Bias and Redshift Evolution in GRB Radio Afterglow Data. Galaxies. 2021; 9(4):95.

Chicago/Turabian Style

Dainotti, Maria, Delina Levine, Nissim Fraija, and Poonam Chandra. 2021. "Accounting for Selection Bias and Redshift Evolution in GRB Radio Afterglow Data" Galaxies 9, no. 4: 95.

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