Papers › Solving the inverse cosmological calibration problem of gamma-ray bursts
Solving the inverse cosmological calibration problem of gamma-ray bursts
S. I. Shirokov, R. I. Gainutdinov, N. Yu. Lovyagin, V. L. Gorokhov
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
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-E_(p,i)) and the k-correction for gravitational lensing and Malmquist bias (GLMB) effect. We used the Pantheon SN Ia catalogue and the standard ΛCDM model with a fixed Hubble constant of H₀=70 km/s/Mpc 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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections