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Register a new userThe Lyman-alpha forest of the QSO in the Hubble Deep Field South
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S. Savaglio
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The quasar in the Hubble Deep Field South (HDFS), J2233-606 (z=2.23) has been exhaustively observed by ground based telescopes and by the STIS spectrograph on board the Hubble Space Telescope at low, medium and high resolution in the spectral interval from 1120 A to 10000 A. This very large base-line represents a unique opportunity to study in detail the distribution of clouds associated with emitting structures in the field of the quasar and in nearby fields already observed as part of the HDFS campaign. Here we report the main properties of the Lyman-alpha clouds in the intermediate redshift range 1.20-2.20, where our present knowledge has been complicated by the difficulty in producing good data. The number density is shown to be higher than what is expected by extrapolating the results from both lower and higher redshifts: 63pm8 lines with log N_{HI}geq14.0 are found (including metal systems) at <z>=1.7, to be compared with ~40 lines predicted by extrapolating from previous studies. The redshift distribution of the Lyman-alpha clouds shows a region spanning z=1.383-1.460 (comoving size of 94 h^{-1}_{65} Mpc, Omega_o=1) with a low density of absorption lines; we detect 5 lines in this region, compared with the 16 expected from an average density along the line of sight. The two point correlation function shows a positive signal up to scales of about 3 h^{-1}_{65} Mpc and an amplitude that is larger for larger HI column densities. The average Doppler parameter is about 27 km/s, comparable to the mean value found at z > 3, thus casting doubts on the temperature evolution of the Lyman-alpha clouds.
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We explore the use of Deep Learning to infer physical quantities from the observable transmitted flux in the Lyman-alpha forest. We train a Neural Network using redshift z=3 outputs from cosmological hydrodynamic simulations and mock datasets constructed from them. We evaluate how well the trained network is able to reconstruct the optical depth for Lyman-alpha forest absorption from noisy and often saturated transmitted flux data. The Neural Network outperforms an alternative reconstruction method involving log inversion and spline interpolation by approximately a factor of 2 in the optical depth root mean square error. We find no significant dependence in the improvement on input data signal to noise, although the gain is greatest in high optical depth regions. The Lyman-alpha forest optical depth studied here serves as a simple, one dimensional, example but the use of Deep Learning and simulations to approach the inverse problem in cosmology could be extended to other physical quantities and higher dimensional data.
The Hubble Deep Field-South observations targeted a high-galactic-latitude field near QSO J2233-606. We present WFPC2 observations of the field in four wide bandpasses centered at roughly 300, 450, 606, and 814 nm. Observations, data reduction procedures, and noise properties of the final images are discussed in detail. A catalog of sources is presented, and the number counts and color distributions of the galaxies are compared to a new catalog of the HDF-N that has been constructed in an identical manner. The two fields are qualitatively similar, with the galaxy number counts for the two fields agreeing to within 20%. The HDF-S has more candidate Lyman-break galaxies at z > 2 than the HDF-N. The star-formation rate per unit volume computed from the HDF-S, based on the UV luminosity of high-redshift candidates, is a factor of 1.9 higher than from the HDF-N at z ~ 2.7, and a factor of 1.3 higher at z ~ 4.
We use hydrodynamic simulations to predict correlations between Lya forest absorption and galaxies at redshift z~3. The probability distribution function (PDF) of Lya flux decrements shifts systematically towards higher values in the vicinity of galaxies, reflecting the overdense environments in which these galaxies reside. The predicted signal remains strong in spectra smoothed over 50-200 km/s, allowing tests with moderate resolution quasar spectra. The strong bias of high redshift galaxies towards high density regions imprints a clear signature on the flux PDF, but the predictions are not sensitive to galaxy baryon mass or star formation rate, and they are similar for galaxies and for dark matter halos. The dependence of the flux PDF on galaxy proximity is sensitive to redshift determination errors, with rms errors of 150-300 km/s substantially weakening the predicted trends. On larger scales, the mean galaxy overdensity in a cube of 5 or 10 Mpc/h (comoving) is strongly correlated with the mean Lya flux decrement on a line of sight through the cube center. The slope of the correlation is ~3 times steeper for galaxies than for dark matter as a result of galaxy bias. The predicted large scale correlation is in qualitative agreement with recently reported observational results. However, observations also show a drop in absorption in the immediate vicinity of galaxies, which our models do not predict even if we allow the galaxies or AGNs within them to be ionizing sources. This decreased absorption could be a signature of galaxy feedback on the surrounding IGM, perhaps via galactic winds. Peculiar velocities often allow gas at comoving distances ~1.5 Mpc/h to produce saturated absorption at the galaxy redshift, so any feedback mechanism must suppress neutral hydrogen out to these radii to match the data. (Abridged)
The angular positions of quasars are deflected by the gravitational lensing effect of foreground matter. The Lyman-alpha forest seen in the spectra of these quasars is therefore also lensed. We propose that the signature of weak gravitational lensing of the forest could be measured using similar techniques that have been applied to the lensed Cosmic Microwave Background, and which have also been proposed for application to spectral data from 21cm radio telescopes. As with 21cm data, the forest has the advantage of spectral information, potentially yielding many lensed slices at different redshifts. We perform an illustrative idealized test, generating a high resolution angular grid of quasars (of order arcminute separation), and lensing the Lyman-alphaforest spectra at redshifts z=2-3 using a foreground density field. We find that standard quadratic estimators can be used to reconstruct images of the foreground mass distribution at z~1. There currently exists a wealth of Lya forest data from quasar and galaxy spectral surveys, with smaller sightline separations expected in the future. Lyman-alpha forest lensing is sensitive to the foreground mass distribution at redshifts intermediate between CMB lensing and galaxy shear, and avoids the difficulties of shape measurement associated with the latter. With further refinement and application of mass reconstruction techniques, weak gravitational lensing of the high redshift Lya forest may become a useful new cosmological probe.
We describe a robust Bayesian statistical method for determining Lyman alpha forest cloud sizes in spherical and in thin disk geometries, using absorption in adjacent sightlines toward closely separated QSO pairs and groups, apply this method to the available data, and discuss implications of our results for models of Ly alpha clouds. Under the assumption of a population of uniform- size and unclustered clouds, the data from Q1343+2640A/B give a 99% confidence lower and upper bounds 61<R<533 kpc/h on the radius of spherical clouds at z about 1.8, with a median value of 149 kpc/h [$(Omega_0, Lambda_0) =(1,0)$]. The baryonic mass of such large clouds is comparable to that of dwarf irregular galaxies. Their cosmic overdensity is close to the turn-around density but generally below the virialization density, suggesting a population of gravi- tationally bound but unvirialized protogalactic objects at z about 2. Their comoving volume density is similar to that of the faint blue galaxies (FBGs) at the limiting magnitude B of 26-27. The dynamical collapsing timescale of over- densities like these clouds is also comparable with the cosmic time difference between z of 2 to 1. Both populations of objects show similar weak clustering in space. All this evidence suggests a possible identification of Ly alpha clouds as the collapsing progenitors of the FBGs at z about 1. We also investigate the other QSO pairs: Q0307-1931/1932, Q0107-0232/0235, and the triplet of Q1623+268. Imposing an uniform W_0 > 0.4 A threshold on all linelists, we find a trend of larger inferred cloud radius with larger proper separation of QSO pairs, significant at the 3.4 sigma level. This indicates that the idealization of unclustered, uniform-sized clouds does not accurately describe the Ly alpha cloud population.
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