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Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up toredshift 10, and thus have the potential to be vital cosmological probes ofearly processes in the universe. Fulfilling this potential requires a largesample of GRBs with known redshifts, but due to observational limitations, only11\% have known redshifts ($z$). There have been numerous attempts to estimateredshifts via correlation studies, most of which have led to inaccuratepredictions. To overcome this, we estimated GRB redshift via an ensemblesupervised machine learning model that uses X-ray afterglows of long-durationGRBs observed by the Neil Gehrels Swift Observatory. The estimated redshiftsare strongly correlated (a Pearson coefficient of 0.93) and have a root meansquare error, namely the square root of the average squared error$\langle\Delta z^2\rangle$, of 0.46 with the observed redshifts showing thereliability of this method. The addition of GRB afterglow parameters improvesthe predictions considerably by 63\% compared to previous results inpeer-reviewed literature. Finally, we use our machine learning model to inferthe redshifts of 154 GRBs, which increase the known redshifts of long GRBs withplateaus by 94\%, a significant milestone for enhancing GRB population studiesthat require large samples with redshift.

Inferring the redshift of more than 150 GRBs with a Machine Learning Ensemble model