Rubin-LSST

Abstract:

We present cosmological (30−400 Mpc) distributions of neutral hydrogen (H i) in the intergalactic medium (IGM) traced by Lyα emitters (LAEs) and QSOs at z = 2.1–2.5, selected with the data of the ongoing Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) and the eBOSS survey. Motivated by a previous study of Mukae et al., we investigate spatial correlations of LAEs and QSOs with H i tomography maps reconstructed from H i Lyα forest absorption in the spectra of background galaxies and QSOs obtained by the CLAMATO survey and this study, respectively. In the cosmological volume far from QSOs, we find that LAEs reside in regions of strong H i absorption, i.e., H i rich, which is consistent with results of previous galaxy−background QSO pair studies. Moreover, there is an anisotropy in the H i distribution plot of transverse and line-of-sight distances; on average the H i absorption peak is blueshifted by ~200 km s−1 from the LAE Lyα redshift, reproducing the known average velocity offset between the Lyα emission redshift and the galaxy systemic redshift. We have identified a ~40 Mpc scale volume of H i underdensity that is a candidate for a giant H ii bubble, where six QSOs and an LAE overdensity exist at $\left\langle z\right\rangle =2.16$. The coincidence of the QSO and LAE overdensities with the H i underdensity indicates that the ionizing photon radiation of the QSOs has created a highly ionized volume of multiple proximity zones in a matter overdensity. Our results suggest an evolutionary picture where H i gas in an overdensity of galaxies becomes highly photoionized when QSOs emerge in the galaxies.

Abstract:

We present a new method to marginalize over uncertainties in redshift distributions, N(z), within tomographic cosmological analyses applicable to current and upcoming photometric galaxy surveys. We allow for arbitrary deviations from the best-guess N(z) governed by a general covariance matrix describing the uncertainty in our knowledge of redshift distributions. In principle, this is marginalization over hundreds or thousands of new parameters describing potential deviations as a function of redshift and tomographic bin. However, by linearly expanding the theory predictions around a fiducial model, this marginalization can be performed analytically, resulting in a modified data covariance matrix that effectively downweights the modes of the data vector that are more sensitive to redshift distribution variations. We showcase this method by applying it to the galaxy clustering measurements from the Hyper Suprime-Cam first data release. We illustrate how to marginalize over sample-variance of the calibration sample and a large general systematic uncertainty in photometric estimation methods, and explore the impact of priors imposing smoothness in the redshift distributions.