Deep learning method in testing the cosmic distance duality relation

Abstract

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 ${\eta }_0=-0.193_{-0.019}^{+0.021}$ and ${\eta }_0=-0.247_{-0.013}^{+0.014}$, respectively. In the PL model, however, DDR is verified within 1$\sigma$ confidence level, with the violation parameter ${\eta }_0=-0.014_{-0.045}^{+0.053}$. 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 $\text{O}(10^{-2})$ using deep learning.

11 pages,4 figures

References in corpus (9)

• Testing the Distance-Duality Relation with Galaxy Clusters and Type Ia Supernovae
• Cosmological-model-independent tests for the distance-duality relation from Galaxy Clusters and Type Ia Supernova
• The Impact of Cosmic Dust on Supernova Cosmology
• Variation of the Amati Relation with the Cosmological Redshift: a Selection Effect or an Evolution Effect?
• Testing the distance duality relation using type Ia supernovae and ultracompact radio sources
• Constraining the reionization history using deep learning from 21cm tomography with the Square Kilometre Array
• A Distance-Deviation Consistency and Model-Independent Method to Test the Cosmic Distance-Duality Relation
• Deep learning for Sunyaev-Zel'dovich detection in Planck
• Test of the cosmic distance duality relation for arbitrary spatial curvature