Speaker
Description
Gamma-Ray Bursts (GRBs), the most luminous explosions in the cosmos, are promising tools for cosmology due to their potential as standardizable candles. Among the empirical correlations proposed for this purpose, the Yonetoku relation, which connects the intrinsic peak energy ($E_{i, p})$ to isotropic peak luminosity ($L_{iso}$), provides a means to probe distances beyond the range of Type Ia supernovae (SNe Ia). The Yonetoku correlation is calibrated and analyzed using both GRBs with measured redshifts and a large sample with machine learning, derived pseudo-redshifts. This analysis focuses on estimating the distance modulus and constraining cosmological parameters using this relation. A joint Markov Chain Monte Carlo (MCMC) analysis is applied to simultaneously determine the Yonetoku parameters ($k, m$) and cosmological parameters ($H_0, \Omega_{\Lambda}$). This method is applied across both the full redshift range and within specific redshift bins, assuming a flat universe within the Lambda Cold Dark Matter ($\Lambda$CDM) model. This unified fitting strategy avoids the circularity problem in GRB cosmology, in which adopting a fixed cosmological model for calibration can bias subsequent parameter inference, by allowing the data to self-consistently constrain both the GRB correlation and cosmology within a single statistical framework. 116 Fermi-GBM GRBs with known redshifts are utilized in combination with the pseudo-Redshift GRB sample, a combination with SNe Ia datasets from U2.1, Dark Energy Survey (DES), and Pantheon+SHOES is employed. This combined approach yields a consistent value for $H_{0}$ and $\Omega_{\Lambda}$, indicating that GRBs with well-modeled pseudo-redshifts can serve as effective high-Redshift cosmological probes.
