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Redshift Evolution of the X-Ray and Ultraviolet Luminosity Relation of Quasars: Calibrated Results from SNe Ia

Li et al. (2025)

Arman Shafieloo #3 Co-author
ASTROPHYSICAL JOURNAL IF 4.9 A 등급 International collaboration

Abstract

Quasars could serve as standard candles if the relation between their ultraviolet (UV) and X-ray luminosities can be accurately calibrated. Previously, we developed a model-independent method to calibrate quasar standard candles using the distance-redshift relation reconstructed from TypeIa supernovae (SNeIa) at z<2 using Gaussian process regression. Interestingly, we found that the calibrated quasar standard candle data set preferred a deviation from ΛCDM at redshifts above z > 2. One possible interpretation of these findings is that the calibration parameters of the quasar UV and X-ray luminosity relationship evolves with redshift. In order to test the redshift dependence of the quasar calibration in a model-independent manner, we divided the quasar sample whose redshift overlaps with the redshift coverage of Pantheon+ SNe Ia compilation into two subsamples: a low-redshift quasar subsample and a high-redshift quasar subsample. Assuming all the quasar samples are reliable, our results show that there is about a 4σ inconsistency between the quasar parameters inferred from the subsamples without considering evolution. This inconsistency suggests the possibility of considering redshift evolution for the relationship between the quasars’ UV and X-ray luminosities. We then test an explicit parameterization of the redshift evolution of the quasar calibration parameters via γ(z)=γ0+γ1(1+z) and β(z)=β0+β1(1+z). Combining this redshift- dependent calibration relationship with the distance-redshift relationship reconstructed from the Pantheon+ supernova compilation, we find the high-redshift subsample and low-redshift subsample become consistent at the 2σ level, which means that the parameterized form of γ(z) and β(z) works well at describing the evolution of the quasar calibration parameters.

AI Classification

Maturity Tier

Tier 1 — Classical ML

AI Role

parameter-estimation

supporting

Techniques

gaussian-processclassical-ML

Tasks

regression/parameter-estimation

Subfield

cosmology

tabular-catalog

Flags

Develops AI method
· Classification confidence: 90%