Recovering Density Fields inside Quasar Proximity Zones at $z\sim 6$

Abstract

The matter density field at $z\sim 6$ is very challenging to probe. One of the traditional probes of the low density IGM that works successfully at lower redshifts is the Lyman-alpha forest in quasar spectra. However, at the end of reionization, the residual neutral hydrogen usually creates saturated absorption, thus much of the information about the gas density is lost. Luckily, in a quasar proximity zone, the ionizing radiation is exceptionally intense, thus creating a large region with non-zero transmitted flux. In this study we use the synthetic spectra from simulations to investigate how to recover the density fluctuations inside the quasar proximity zones. We show that, under ideal conditions, the density can be recovered accurately with a small scatter. We also discuss how systematics such as the quasar continuum fitting and reionization models affect the results. This study shows that by analyzing the absorption features inside quasar proximity zones we can potentially constrain quasar properties and the environments they reside in.

12 pages, 12 figures, comments welcome

References in corpus (5)

• The Final SDSS High-Redshift Quasar Sample of 52 Quasars at z>5.7
• The first ultraviolet quasar stacked spectrum at z=2.4 from WFC3
• The nature and evolution of the highly ionized near-zones in the absorption spectra of z~6 quasars
• Minor Contribution of Quasars to Ionizing Photon Budget at z~6: Update on Quasar Luminosity Function at the Faint-end with Subaru/Suprime-Cam
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Cited by in corpus (6)

• XQR-30: the ultimate XSHOOTER quasar sample at the reionization epoch
• Measuring the Density Fields around Bright Quasars at $z\sim 6$ with XQR-30 Spectra
• Galaxy Formation and Symbiotic Evolution with the Inter-Galactic Medium in the Age of ELT-ANDES
• Approximating Density Probability Distribution Functions Across Cosmologies
• Reconstructing Large-scale Temperature Profiles around $z\sim 6$ Quasars
• Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning