Galaxy cluster strong lensing cosmography: cosmological constraints from a sample of regular galaxy clusters

Authors: G. B. Caminha, S. H. Suyu, C. Grillo, P. Rosati

A&A 657, A83 (2022)
arXiv: 2110.06232v1 - DOI (astro-ph.CO)
10 pages, 4 figures, 3 tables, submitted to A&A

Abstract: Cluster strong lensing cosmography is a promising probe of the background geometry of the Universe and several studies have emerged, thanks to the increased quality of observations using space and ground-based telescopes. For the first time, we use a sample of five cluster strong lenses to measure the values of cosmological parameters and combine them with those from classical probes. In order to assess the degeneracies and the effectiveness of strong-lensing cosmography in constraining the background geometry of the Universe, we adopt four cosmological scenarios. We find good constraining power on the total matter density of the Universe ($\Omega_{\rm m}$) and the equation of state of the dark energy parameter $w$. For a flat $w$CDM cosmology, we find $\Omega_{\rm m} = 0.30_{-0.11}^{+0.09}$ and $w=-1.12_{-0.32}^{+0.17}$ from strong lensing only. Interestingly, we show that the constraints from the Cosmic Microwave Background (CMB) are improved by factors of 2.5 and 4.0 on $\Omega_{\rm m}$ and $w$, respectively, when combined with our posterior distributions in this cosmological model. In a scenario where the equation of state of dark energy evolves with redshift, the strong lensing constraints are compatible with a cosmological constant (i.e. $w=-1$). In a curved cosmology, our strong lensing analyses can accommodate a large range of values for the curvature of the Universe of $\Omega_{\rm k}=0.28_{-0.21}^{+0.16}$. In all cosmological scenarios, we show that our strong lensing constraints are complementary and in good agreement with measurements from the CMB, baryon acoustic oscillations and Type Ia supernovae. Our results show that cluster strong lensing cosmography is a potentially powerful probe to be included in the cosmological analyses of future surveys.

Submitted to arXiv on 12 Oct. 2021

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.