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We use the 21 cm emission line data from the DINGO-VLA project to study the atomic hydrogen gas H,{textsc i} of the Universe at redshifts $z<0.1$. Results are obtained using a stacking analysis, combining the H,{textsc i} signals from 3622 galaxies extracted from 267 VLA pointings in the G09 field of the Galaxy and Mass Assembly Survey (GAMA). Rather than using a traditional one-dimensional spectral stacking method, a three-dimensional cubelet stacking method is used to enable deconvolution and the accurate recovery of average galaxy fluxes from this high-resolution interferometric dataset. By probing down to galactic scales, this experiment also overcomes confusion corrections that have been necessary to include in previous single dish studies. After stacking and deconvolution, we obtain a $30sigma$ H,{textsc i} mass measurement from the stacked spectrum, indicating an average H,{textsc i} mass of $M_{rm H,{textsc i}}=(1.674pm 0.183)times 10^{9}~{Msun}$. The corresponding cosmic density of neutral atomic hydrogen is $Omega_{rm H,{textsc i}}=(0.377pm 0.042)times 10^{-3}$ at redshift of $z=0.051$. These values are in good agreement with earlier results, implying there is no significant evolution of $Omega_{rm H,{textsc i}}$ at lower redshifts.
Using a spectral stacking technique, we measure the neutral hydrogen (HI) properties of a sample of galaxies at $z < 0.11$ across 35 pointings of the Westerbork Synthesis Radio Telescope (WSRT). The radio data contains 1,895 galaxies with redshifts a
We use spectral stacking to measure the contribution of galaxies of different masses and in different hierarchies to the cosmic atomic hydrogen (HI) mass density in the local Universe. Our sample includes 1793 galaxies at $z < 0.11$ observed with the
Measuring the HI-halo mass scaling relation (HIHM) is fundamental to understanding the role of HI in galaxy formation and its connection to structure formation. While direct measurements of the HI mass in haloes are possible using HI-spectral stackin
We use high-resolution cosmological zoom-in simulations from the FIRE project to make predictions for the covering fractions of neutral hydrogen around galaxies at z=2-4. These simulations resolve the interstellar medium of galaxies and explicitly im
We develop a machine learning-based framework to predict the HI content of galaxies using more straightforwardly observable quantities such as optical photometry and environmental parameters. We train the algorithm on z=0-2 outputs from the Mufasa co