Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMeasuring Cosmic Density of Neutral Hydrogen via Stacking the DINGO-VLA Data
178
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
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.
rate research
Read More
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 and positions known from the Sloan Digital Sky Survey (SDSS). We carefully quantified the effects of sample bias, aperture used to extract spectra, sidelobes and weighting technique and use our data to provide a new estimate for the cosmic HI mass density. We find a cosmic HI mass density of $Omega_{rm HI} = (4.02 pm 0.26)times 10^{-4} h_{70}^{-1}$ at $langle zrangle = 0.066$, consistent with measurements from blind HI surveys and other HI stacking experiments at low redshifts. The combination of the small interferometer beam size and the large survey volume makes our result highly robust against systematic effects due to confusion at small scales and cosmic variance at large scales. Splitting into three sub-samples with $langle zrangle$ = 0.038, 0.067 and 0.093 shows no significant evolution of the HI gas content at low redshift.
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 Westerbork Synthesis Radio Telescope, for which Sloan Digital Sky Survey spectroscopy and hierarchy information are also available. We find a cosmic HI mass density of $Omega_{rm HI} = (3.99 pm 0.54)times 10^{-4} h_{70}^{-1}$ at $langle zrangle = 0.065$. For the central and satellite galaxies, we obtain $Omega_{rm HI}$ of $(3.51 pm 0.49)times 10^{-4} h_{70}^{-1}$ and $(0.90 pm 0.16)times 10^{-4} h_{70}^{-1}$, respectively. We show that galaxies above and below stellar masses of $sim$10$^{9.3}$ M$_{odot}$ contribute in roughly equal measure to the global value of $Omega_{rm HI}$. While consistent with estimates based on targeted HI surveys, our results are in tension with previous theoretical work. We show that these differences are, at least partly, due to the empirical recipe used to set the partition between atomic and molecular hydrogen in semi-analytical models. Moreover, comparing our measurements with the cosmological semi-analytic models of galaxy formation {sc Shark} and GALFORM reveals gradual stripping of gas via ram pressure works better to fully reproduce the properties of satellite galaxies in our sample, than strangulation. Our findings highlight the power of this approach in constraining theoretical models, and confirm the non-negligible contribution of massive galaxies to the HI mass budget of the local Universe.
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 stacking, the reported shape of the relation depends on the techniques used to measure it (e.g. monotonically increasing with mass versus flat, mass-independent). Using a simulated HI and optical survey produced with the SHARK semi-analytic galaxy formation model, we investigate how well different observational techniques can recover the intrinsic, theoretically predicted, HIHM relation. We run a galaxy group finder and mimic the HI stacking procedure adopted by different surveys and find we can reproduce their observationally derived HIHM relation. However, none of the adopted techniques recover the underlying HIHM relation predicted by the simulation. We find that systematic effects in halo mass estimates of galaxy groups modify the inferred shape of the HIHM relation from the intrinsic one in the simulation, while contamination by interloping galaxies, not associated with the groups, contribute to the inferred HI mass of a halo mass bin, when using large velocity windows for stacking. The effect of contamination is maximal at Mvir~10^(12-12.5)Msol. Stacking methods based on summing the HI emission spectra to infer the mean HI mass of galaxies of different properties belonging to a group suffer minimal contamination but are strongly limited by the use of optical counterparts, which miss the contribution of dwarf galaxies. Deep spectroscopic surveys will provide significant improvements by going deeper while maintaining high spectroscopic completeness; for example, the WAVES survey will recover ~52% of the total HI mass of the groups with Mvir~10^(14)Msol compared to ~21% in GAMA.
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 implement a comprehensive set of stellar feedback mechanisms. Our simulation sample consists of 16 main halos covering the mass range M_h~10^9-6x10^12 Msun at z=2, including 12 halos in the mass range M_h~10^11-10^12 Msun corresponding to Lyman break galaxies (LBGs). We process our simulations with a ray tracing method to compute the ionization state of the gas. Galactic winds increase the HI covering fractions in galaxy halos by direct ejection of cool gas from galaxies and through interactions with gas inflowing from the intergalactic medium. Our simulations predict HI covering fractions for Lyman limit systems (LLSs) consistent with measurements around z~2-2.5 LBGs; these covering fractions are a factor ~2 higher than our previous calculations without galactic winds. The fractions of HI absorbers arising in inflows and in outflows are on average ~50% but exhibit significant time variability, ranging from ~10% to ~90%. For our most massive halos, we find a factor ~3 deficit in the LLS covering fraction relative to what is measured around quasars at z~2, suggesting that the presence of a quasar may affect the properties of halo gas on ~100 kpc scales. The predicted covering fractions, which decrease with time, peak at M_h~10^11-10^12 Msun, near the peak of the star formation efficiency in dark matter halos. In our simulations, star formation and galactic outflows are highly time dependent; HI covering fractions are also time variable but less so because they represent averages over large areas.
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 cosmological hydrodynamic simulation, which includes star formation, feedback, and a heuristic model to quench massive galaxies that yields a reasonable match to a range of survey data including HI. We employ a variety of machine learning methods (regressors), and quantify their performance using the root mean square error ({sc rmse}) and the Pearson correlation coefficient (r). Considering SDSS photometry, 3$^{rd}$ nearest neighbor environment and line of sight peculiar velocities as features, we obtain r $> 0.8$ accuracy of the HI-richness prediction, corresponding to {sc rmse}$<0.3$. Adding near-IR photometry to the features yields some improvement to the prediction. Compared to all the regressors, random forest shows the best performance, with r $>0.9$ at $z=0$, followed by a Deep Neural Network with r $>0.85$. All regressors exhibit a declining performance with increasing redshift, which limits the utility of this approach to $zla 1$, and they tend to somewhat over-predict the HI content of low-HI galaxies which might be due to Eddington bias in the training sample. We test our approach on the RESOLVE survey data. Training on a subset of RESOLVE data, we find that our machine learning method can reasonably well predict the HI-richness of the remaining RESOLVE data, with {sc rmse}$sim0.28$. When we train on mock data from Mufasa and test on RESOLVE, this increases to {sc rmse}$sim0.45$. Our method will be useful for making galaxy-by-galaxy survey predictions and incompleteness corrections for upcoming HI 21cm surveys such as the LADUMA and MIGHTEE surveys on MeerKAT, over regions where photometry is already available.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
