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In this paper, we carry out an assessment of cosmic distance duality relation(CDDR) based on the latest observations of HII galaxies acting as standardcandles 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 Hubblediagrams from observational data. We show that both approaches are capable ofreconstructing the current constraints on possible deviations from the CDDR inthe redshift range $z\sim 2.3$. Considering four different parametric methodsof CDDR, which quantify deviations from the CDDR and the standard cosmologicalmodel, 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 currentobservational data within $1\sigma$ based on the reconstructed distancesthrough GP in the overlapping redshift domain. Moreover, we find thatultra-compact radio quasars could provide $10^{-3}$-level constraints on theviolation parameter at high redshifts, when combined with the observations ofHII galaxies. In the framework of ANN, one could derive robust constraints onthe violation parameter at a precision of $10^{-2}$, with the validity of suchdistance duality relation within $2\sigma$ confidence level.

european physical journal. c, particles and fields

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