On the cosmic distance duality relation and strong gravitational lens power law density profile

Lima, F. S.; Holanda, R. F. L.; Pereira, S. H.; Silva, W.J.C. da

doi:10.1088/1475-7516/2021/08/035

Cited by 5 publications

(8 citation statements)

References 65 publications

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“…The magnitudes at the redshifts of lens and source in SGL systems can be determined with the reconstructed m(z) relation one-to-one. Compared with previous works [5][6][7]13], we reconstructed data without any assumption on the cosmological model or the specific parametric form. Compared with GP method [17,39], our method can reconstruct the data up to higher redshift range but with a lower uncertainty.…”

Section: Discussionmentioning

confidence: 99%

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Deep learning method for testing the cosmic distance duality relation*

Tang¹,

Lin²,

Liu³

2023

Chinese Phys. C

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The cosmic distance duality relation (DDR) is constrained from the combination of type-Ia supernovae (SNe Ia) and strong gravitational lensing (SGL) systems using deep learning method. To make use of the full SGL data, we reconstruct the luminosity distance from SNe Ia up to the highest redshift of SGL using deep learning, then it is compared with the angular diameter distance obtained from SGL. Considering the influence of lens mass profile, we constrain the possible violation of DDR in three lens mass models. Results show that in the SIS model and EPL model, DDR is violated at high confidence level, with the violation parameter $\eta_0=-0.193^{+0.021}_{-0.019}$ and $\eta_0=-0.247^{+0.014}_{-0.013}$, respectively. In the PL model, however, DDR is verified within 1$\sigma$ confidence level, with the violation parameter $\eta_0=-0.014^{+0.053}_{-0.045}$. Our results demonstrate that the constraints on DDR strongly depend on the lens mass models. Given a specific lens mass model, DDR can be constrained at a precision of $\textit{O}(10^{-2})$ using deep learning.

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Section: Discussionmentioning

confidence: 99%

“…While the angular diameter distance is usually obtained from various observations. For example, the angular diameter distance obtained from galaxy clusters can be combined with SNe Ia to test DDR [5][6][7]13]. Li et al [9] tested DDR using the combination of SNe Ia and ultra-compact radio sources.…”

Section: Introductionmentioning

confidence: 99%

Deep learning method for testing the cosmic distance duality relation*

Tang¹,

Lin²,

Liu³

2023

Chinese Phys. C

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“…This sample has been widely used in the cosmological analyses. For example, it has been used to test the General Relativity [14,15], the cosmic distance duality relation [16,17,18,19,20] and the invariance of the speed of light [21,22], as well as to estimate the cosmic curvature and other cosmological parameters [23,24].…”

Section: Introductionmentioning

confidence: 99%

Cosmological application of the lens-redshift probability distribution with improved galaxy-scale gravitational lensing sample

Chen

2023

Physics of the Dark Universe

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“…, where the CDDR relates luminosity distances (D L ) to angular diameter distances (D A ; ADDs; Liao et al 2016;Lima et al 2021;Zhou et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Exploring the Possible Evolution of the Mass Density Power-law Index of Strong Gravitational Lenses with a Model-independent Method

2023

ApJ

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In this work, we adopt a cosmological model-independent approach for the first time to test the question of whether the mass density power-law index (γ) of strong gravitational lensing systems (SGLSs) evolves with redshift, and the joint light-curve Type Ia supernova (SNe Ia) sample and the quasar sample from Risaliti & Lusso are used to provide the luminosity distances to be calibrated. Our work is based on the flat universe assumption and the cosmic distance duality relation. A reliable data-matching method is used to pair SGLS–SNe and SGLS–quasars. By using the maximum likelihood method to constrain the luminosity distance and γ index, we obtain the likelihood function values for the evolved and nonevolved cases, and then use the Akaike weights and the Bayesian Information Criterion (BIC) selection weights to compare the advantages and disadvantages of these two cases. We find that the γ index is slightly more likely to be a nonevolutionary model for γ = 2 in the case of the currently used samples with low redshift (z l < ∼ 0.66). With Akaike weights, the relative probabilities are 66.3% versus 33.7% and 69.9% versus 30.1% for the SGLS + SNe Ia sample and SGLS + quasar sample, respectively, and with the BIC selection weights, the relative probabilities are 87.4% versus 12.6% and 52.0% versus 48.0% for the two samples. In the evolving case for the relatively low-redshift lenses (SGLS + SNe Ia), with redshift 0.0625–0.659, γ = 2.058 − 0.040 + 0.041 – 0.136 − 0.165 + 0.163 z . At high redshift (SGLS + quasar), with redshift 0.0625–1.004, γ = 2.051 − 0.077 + 0.076 – 0.171 − 0.196 + 0.214 z . Although not the more likely model, this evolved γ case also fits the data well, with a negative and mild evolution for both low- and high-redshift samples.

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On the cosmic distance duality relation and strong gravitational lens power law density profile

Cited by 5 publications

References 65 publications

Deep learning method for testing the cosmic distance duality relation*

Deep learning method for testing the cosmic distance duality relation*

Cosmological application of the lens-redshift probability distribution with improved galaxy-scale gravitational lensing sample

Exploring the Possible Evolution of the Mass Density Power-law Index of Strong Gravitational Lenses with a Model-independent Method

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