Deep Neural Networks for Estimation of Gamma-Ray Burst Redshifts

Understand

While the available set of Gamma-ray Burst (GRB) data with known redshift is currently limited, a much larger set of GRB data without redshift is available from different instruments.

• This data includes well-measured prompt gamma-ray flux and spectral information.
• We estimate the redshift of a selection of these GRBs detected by Fermi-GBM and Konus-Wind using Machine Learning techniques that are based on spectral parameters.
• We find that Deep Neural Networks with Random Forest models employing non-linear relations among input parameters can reasonably reproduce the pseudo-redshift distribution of GRBs, mimicking the distribution of GRBs with spectroscopic redshift.