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

Tonghua Liu,Xiaolong Gong,Chenfa Zheng,Wuzheng Guo,Sixuan Zhang,Shuo Cao

doi:10.1140/epjc/s10052-021-09713-5

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 zsim 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 1sigma based on the reconstructed distances through GP in the overlapping redshift domain. Moreover, we find that ultra-compact radio quasars could provide 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}, with the validity of such distance duality relation within 2sigma confidence level.

Highlights

Cally, the cosmic distance duality relation (CDDR) indicates that DL (z) and DA(z) satisfy the relation of DL (z) = DA(z)(1 + z)2 at the same redshift [1,2]
The validity of the CDDR depends on three basic assumptions: (i) the space-time is described by a metric theory; (ii) the light travels along the null geodesics between the source and the observer; (iii) the photon number is conserved
Testing CDDR needs two types of observational data sets, i.e., the luminosity distance derived from the luminous sources with known intrinsic luminosity in the Universe like type-Ia supernova (SN Ia), and the angular diameter distance observed from Baryon Acoustic Oscillations (BAO) [6], Sunyaev–Zeldovich (SZ) effect in clusters with X-ray surface luminosity measurements [7,8,9], or strong gravitational lensing (SGL) [10,11], etc

Summary

Introduction

Testing CDDR needs two types of observational data sets, i.e., the luminosity distance derived from the luminous sources with known (or standardizable) intrinsic luminosity in the Universe like type-Ia supernova (SN Ia), and the angular diameter distance observed from Baryon Acoustic Oscillations (BAO) [6], Sunyaev–Zeldovich (SZ) effect in clusters with X-ray surface luminosity measurements [7,8,9], or strong gravitational lensing (SGL) [10,11], etc. Many authors presented a new way to constrain the CDDR with different machine learning algorithms [17,18,19], with the luminosity distance and angular diameter distance reconstructed from complementary external probes (Type Ia supernovae and gravitational wave (GW) standard sirens) [20] Their results demonstrated the effectiveness of machine learning approaches in the highprecision test of the electromagnetic and gravitational distance duality relations. We will use two non-parameterized methods, Gaussian Process (GP) and Artificial Neural Network (ANN) algorithm, to reconstruct the newest observations of HII galaxy Hubble diagram and ultra-compact structure of radio quasars, respectively. These two approaches are datadriven and have no assumptions about the data, suggesting that they are completely model-independent.

Luminosity distances from HII galaxies and extragalactic HII regions

Angular diameter distances measured from compact structure of radio quasars

Reconstructions based on Gaussian process and artificial neural network

Testing the validity of CDDR

Results and discussion

Conclusion