English

A Model-independent Method to Determine $H_0$ Using Time-Delay Lensing, Quasars and Type Ia Supernovae

Cosmology and Nongalactic Astrophysics 2024-01-29 v2

Abstract

Absolute distances from strong lensing can anchor Type Ia Supernovae (SNe Ia) at cosmological distances giving a model-independent inference of the Hubble constant (H0H_0). Future observations could provide strong lensing time-delay distances with source redshifts up to z4z\,\simeq\,4, which are much higher than the maximum redshift of SNe Ia observed so far. In order to make full use of time-delay distances measured at higher redshifts, we use quasars as a complementary cosmic probe to measure cosmological distances at redshifts beyond those of SNe Ia and provide a model-independent method to determine H0H_0. In this work, we demonstrate a model-independent, joint constraint of SNe Ia, quasars, and time-delay distances from strong lensed quasars. We first generate mock data sets of SNe Ia, quasar, and time-delay distances based on a fiducial cosmological model. Then, we calibrate the quasar parameters model independently using Gaussian process (GP) regression with mock SNe Ia data. Finally, we determine the value of H0H_0 model-independently using GP regression from mock quasars and time-delay distances from strong lensing systems. As a comparison, we also show the H0H_0 results obtained from mock SNe Ia in combination with time-delay lensing systems whose redshifts overlap with SNe Ia. Our results show that quasars at higher redshifts show great potential to extend the redshift coverage of SNe Ia and thus enable the full use of strong lens time-delay distance measurements from ongoing cosmic surveys and improve the accuracy of the estimation of H0H_0 from 2.1%2.1\% to 1.3%1.3\% when the uncertainties of the time-delay distances are 5%5\% of the distance values.

Keywords

Cite

@article{arxiv.2308.06951,
  title  = {A Model-independent Method to Determine $H_0$ Using Time-Delay Lensing, Quasars and Type Ia Supernovae},
  author = {Xiaolei Li and Ryan E. Keeley and Arman Shafieloo and Kai Liao},
  journal= {arXiv preprint arXiv:2308.06951},
  year   = {2024}
}

Comments

10 pages, 5 figures, 2 tables, accepted for publication in ApJ