Data Fusion for Gamma-Ray Burst Population Studies

Statistics – Methodology

Scientific paper

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Scientific paper

Gamma-ray burst (GRB) data are heterogeneous. Survey missions such as CGRO and Swift provide basic information (e.g., direction, peak flux) for all bursts. But for a subset of bursts with counterparts at other wavelengths, other data is available from afterglow observations, e.g., host galaxy redshifts, isotropic energy, and afterglow light curves. This heterogeniety significantly complicates global (population-level) analyses. We have developed a "data fusion" methodology that can rigorously combine GRB data from various sources, making optimum use of all the information available. We build upon our earlier Bayesian/likelihood approach for analyzing GRB population data, which is ideally suited to data fusion. We are initially focusing our efforts on methods for analysis of models for the GRB spatial and luminosity distributions using burst intensity and redshift data (possibly including redshifts from "luminosity indicators"). Roughly speaking, our approach uses the supplementary data available for the subset of bursts with afterglows to approximately "calibrate" the more widely available burst intensity data. This allows more accurate modelling of the burster redshift and luminosity distributions. Our approach accounts for significant biases and distortions ignored in other current analyses.

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