The Hubble Diagram: Jump from Supernovae to Gamma-ray Bursts

Lovyagin, N. Yu.; Gainutdinov, R. I.; Shirokov, S. I.; Gorokhov, V. L.

doi:10.3390/universe8070344

Cited by 3 publications

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Testing the cosmological principle with the Pantheon+ sample and the region-fitting method

Hu,

Wang,

et al. 2024

A&A

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The cosmological principle is fundamental to the standard cosmological model. It assumes that the Universe is homogeneous and isotropic on very large scales. As the basic assumption, it must stand the test of various observations. In this work, we investigated the properties of the Pantheon+ sample, including redshift distribution and position distribution, and we give its constraint on the flat Lambda CDM model: $ m $ = 0.36pm 0.02 and $H_ $ = 72.83pm 0.23 km s$^ $ Mpc$^ $. Then, using the region fitting (RF) method, we mapped the all-sky distribution of cosmological parameters ($ m $ and $H_ $) and find that the distribution significantly deviates from isotropy. A local matter underdensity region exists toward ($ circ circ $) as well as a preferred direction of the cosmic anisotropy ($ circ circ $) in galactic coordinates. Similar directions may imply that local matter density might be responsible for the anisotropy of the accelerated expansion of the Universe. Results of statistical isotropy analyses including Isotropy and Isotropy with real-data positions (RP) show high confidence levels. For the local matter underdensity, the statistical significances are 2.78sigma (isotropy) and 2.34sigma (isotropy RP). For the cosmic anisotropy, the statistical significances are 3.96sigma (isotropy) and 3.15sigma (isotropy RP). The comparison of these two kinds of statistical isotropy analyses suggests that inhomogeneous spatial distribution of real sample can increase the deviation from isotropy. The similar results and findings are also found from reanalyses of the low-redshift sample (lp+) and the lower screening angle ( = 60degr ), but with a slight decrease in statistical significance. Overall, our results provide clear indications for a possible cosmic anisotropy. This possibility must be taken seriously. Further testing is needed to better understand this signal.

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Testing the cosmological principle with the Pantheon+ sample and the region-fitting method

Hu,

Wang,

et al. 2024

A&A

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Solving the inverse cosmological calibration problem of gamma-ray bursts

Shirokov,

Gainutdinov,

Lovyagin

et al. 2023

Monthly Notices of the Royal Astronomical Society

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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.

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