Solving the inverse cosmological calibration problem of gamma-ray bursts

• Published in: Monthly notices of the Royal Astronomical Society Vol. 527; no. 2; pp. 2214 - 2231
• Main Authors: Shirokov, S I, Gainutdinov, R I, Lovyagin, N Yu, Gorokhov, V L
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.01.2024
• Subjects: distance scale; cosmological parameters; gamma-ray bursts
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stad3361

ABSTRACT We have received a new physical characteristics fitting based on actual observational data from the Swift mission’s long-duration gamma-ray bursts (LGRBs). We considered such characteristics as the Amati parameters for linear correlation (Eiso–Ep,i) and the k-correction for gravitational lensing and Malmquist bias (GLMB) effect. We used the Pantheon SN Ia catalogue and the standard Lambda cold dark matter model with a fixed Hubble constant of H0 = 70 km s−1 Mpc−1 as the baseline for the Hubble function μ(z). In our paper, we formulated the inverse cosmological calibration problem (ICCP) in the non-parametric statistics framework. The ICCP involves fitting non-observable physical characteristics while assuming a fixed cosmological model. To solve this problem, we developed a new method that is resistant to non-Gaussian processes. This method is based on error propagation through the Monte Carlo method and the Theil–Sen method for linear regression estimate. We have demonstrated the stability and robustness of this assessment method. The parameter estimates are as follows: $a=0.92^{+0.12}_{-0.12}$, $b=50.32^{+0.33}_{-0.32}$ without considering the GLMB effect, and $a=0.63^{+0.13}_{-0.14}$, $b=50.12^{+0.33}_{-0.31}$, and $k=1.98^{+0.25}_{-0.24}$ with the effect included. The proposed method can be applied to any other calibration sample of known standard candles, a calibrated sample of LGRBs, and the Hubble function μ(z). In the future, the ICCP idea can be used as an alternative cosmological test for estimating cosmological parameters, including the GLMB effect, or even for the selection of models, providing new information about the Universe. This can be done by analysing the residual values of observational data within the Bayesian statistics paradigm.