Beschreibung
Testing the expansion history beyond the redshift range densely sampled by Type Ia supernovae requires independent distance probes with distinct astrophysical systematics. Wide-field time-domain surveys now deliver light curves for hundreds of thousands of quasars, making AGN variability a viable survey-scale distance indicator via the optical/UV variability–luminosity anti-correlation: at fixed rest-frame wavelength and timescale, more luminous Type 1 AGN vary less.
I will present a hierarchical Bayesian framework for AGN-variability cosmology that combines Gaussian-process light-curve modelling with population-level inference of the variability–luminosity relation and cosmological distance–redshift parameters. The framework propagates light-curve uncertainties into the population-level fit, with Gaia-DR3-like mock catalogues used to validate the method for survey-scale inference under realistic selection effects.
Building on this validation, I will present first calibration results from a parent sample of over 200,000 SDSS quasars out to (z\simeq3.5), combining Gaia G and ZTF g/r light curves with multi-epoch DESI DR1 spectroscopy where available. I will show how this data set constrains the wavelength and luminosity dependence of the variability relation, and discuss what these calibrations imply for the distance precision and cosmological reach of AGN variability for tests of ΛCDM and its extensions.