Speaker
Description
Gamma-ray bursts (GRBs) can extend cosmological studies beyond the redshift range of Type Ia supernovae (SNe Ia), but their calibration is affected by the circularity problem. We apply LADDER, a deep-learning reconstruction trained on Pantheon+ SNe Ia, to obtain a model-independent distance-redshift relation. Building a self-contained calibration pipeline applied directly to raw Fermi-GBM catalog data, we calibrate both the Amati relation ($E_{\text{p, i}}$-$E_{\text{iso}}$) and Yonetoku relation ($E_{\text{p, i}}$-$L_{\text{iso}}$) without assuming a cosmological model. We then use these calibrated relations to estimate $E_{\text{iso}}$ and $L_{\text{iso}}$ for the Fermi-GBM sample. We further investigate whether the calibrated relations can be inverted to infer GRB pseudo-redshifts, and report on the accuracy, coverage, and current limitations of this inversion, including the intrinsic scatter and extrapolation constraints that bound its reliability at the highest redshifts.