Revisiting the cosmic distance duality relation with machine learning reconstruction methods: the combination of HII galaxies and ultra-compact radio quasars

Abstract

In this paper, we carry out an assessment of cosmic distance duality relation (CDDR) based on the latest observations of HII galaxies acting as standard candles and ultra-compact structure in radio quasars acting as standard rulers. Particularly, two machine learning reconstruction methods [Gaussian Process (GP) and Artificial Neural Network (ANN)] are applied to reconstruct the Hubble diagrams from observational data. We show that both approaches are capable of reconstructing the current constraints on possible deviations from the CDDR in the redshift range $$z\sim 2.3$$ z ∼ 2.3 . Considering four different parametric methods of CDDR, which quantify deviations from the CDDR and the standard cosmological model, we compare the results of the two different machine learning approaches. It is observed that the validity of CDDR is in well agreement with the current observational data within $$1\sigma $$ 1 σ based on the reconstructed distances through GP in the overlapping redshift domain. Moreover, we find that ultra-compact radio quasars could provide $$10^{-3}$$ 10 - 3 -level constraints on the violation parameter at high redshifts, when combined with the observations of HII galaxies. In the framework of ANN, one could derive robust constraints on the violation parameter at a precision of $$10^{-2}$$ 10 - 2 , with the validity of such distance duality relation within $$2\sigma $$ 2 σ confidence level.

Revisiting the Hubble Constant, Spatial Curvature, and Cosmography with Strongly Lensed Quasar and Hubble Parameter Observations

“…On the other hand, the approach of cosmic chronometers makes use of differential ages of passively evolving galaxies and thus is independent of any specific cosmological model. Hence the resulting H(z) measurements are cosmological-model-independent (Liu et al 2020a(Liu et al , 2021a. Therefore, in our work we consider only the cosmic chronometer data to reconstruct the H(z) function and then derive dimensionless comoving distances (d l and d s ) within the redshift range 0 < z < 2.3, which covers the redshift of six SGL systems very well.…”

Cosmological-model-independent tests of cosmic distance duality relation with Type Ia supernovae and radio quasars

“…Another method used the python package GaPP to reconstruct QSO angular size as a function of redshift from the binned data, similar to the method applied in [26]. The python package used to reconstruct the Q-SO data was based on Gaussian Processes (GP) [46] and depended on the mean function and the covariance function k(x, x ).…”

“…It is rewarding to turn to objects covering a wide redshift range with both angular diameter and luminosity distances measurement. Many efforts have been made to study the possibility of using quasars with multiple measurements for such purpose [24][25][26]. For instance, Reference [24] has applied it to derive luminosity distances to quasars from the non-linear relation between the UV and X-ray emission [27] and angular diameter distances from the angular size-redshift relation of compact radio quasars [28].…”

Deep learning method for testing the cosmic distance duality relation*

“…In each layer, hundreds of nonlinear neurons process the data information. The deep learning method has been widely employed in various fields including cosmological research [12,23,[25][26][27][28]. In this work, we test DDR with the combined data of SNe Ia and SGL, while the luminosity distance is reconstructed from SNe Ia using deep learning.…”