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Register a new userModeling Lyman-alpha Forest Cross-Correlations with LyMAS
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We use the Ly-$alpha$ Mass Association Scheme (LyMAS; Peirani et al. 2014) to predict cross-correlations at $z=2.5$ between dark matter halos and transmitted flux in the Ly-$alpha$ forest, and compare to cross-correlations measured for quasars and damped Ly-$alpha$ systems (DLAs) from the Baryon Oscillation Spectroscopic Survey (BOSS) by Font-Ribera et al. (2012, 2013). We calibrate LyMAS using Horizon-AGN hydrodynamical cosmological simulations of a $(100 h^{-1} mathrm{Mpc})^3$ comoving volume. We apply this calibration to a $(1 h^{-1} mathrm{Gpc})^3$ simulation realized with $2048^3$ dark matter particles. In the 100 $h^{-1}$ Mpc box, LyMAS reproduces the halo-flux correlations computed from the full hydrodynamic gas distribution very well. In the 1 $h^{-1}$ Gpc box, the amplitude of the large scale cross-correlation tracks the halo bias $b_h$ as expected. We provide empirical fitting functions that describe our numerical results. In the transverse separation bins used for the BOSS analyses, LyMAS cross-correlation predictions follow linear theory accurately down to small scales. Fitting the BOSS measurements requires inclusion of random velocity errors; we find best-fit RMS velocity errors of 399 km s$^{-1}$ and 252 km s$^{-1}$ for quasars and DLAs, respectively. We infer bias-weighted mean halo masses of $M_h/10^{12} h^{-1}M_odot=2.19^{+0.16}_{-0.15}$ and $0.69^{+0.16}_{-0.14}$ for the host halos of quasars and DLAs, with $sim 0.2$ dex systematic uncertainty associated with redshift evolution, IGM parameters, and selection of data fitting range.
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Cosmological hydrodynamic simulations can accurately predict the properties of the intergalactic medium (IGM), but only under the condition of retaining high spatial resolution necessary to resolve density fluctuations in the IGM. This resolution constraint prohibits simulating large volumes, such as those probed by BOSS and future surveys, like DESI and 4MOST. To overcome this limitation, we present Iteratively Matched Statistics (IMS), a novel method to accurately model the Lyman-alpha forest with collisionless N-body simulations, where the relevant density fluctuations are unresolved. We use a small-box, high-resolution hydrodynamic simulation to obtain the probability distribution function (PDF) and the power spectrum of the real-space Lyman-alpha forest flux. These two statistics are iteratively mapped onto a pseudo-flux field of an N-body simulation, which we construct from the matter density. We demonstrate that our method can perfectly reproduce line-of-sight observables, such as the PDF and power spectrum, and accurately reproduce the 3D flux power spectrum (5-20%). We quantify the performance of the commonly used Gaussian smoothing technique and show that it has significantly lower accuracy (20-80%), especially for N-body simulations with achievable mean inter-particle separations in large-volume simulations. In addition, we show that IMS produces reasonable and smooth spectra, making it a powerful tool for modeling the IGM in large cosmological volumes and for producing realistic mock skies for Lyman-alpha forest surveys.
The Lyman-alpha forest is the large-scale structure probe for which we appear to have modeling control to the highest wavenumbers, which makes it of great interest for constraining the warmness/fuzziness of the dark matter and the timing of reionization processes. However, the standard statistic, the Lyman-alpha forest power spectrum, is unable to strongly constrain the IGM temperature-density relation, and this inability further limits how well other high wavenumber-sensitive parameters can be constrained. With the aim of breaking these degeneracies, we measure the power spectrum of the Lyman-beta forest and its cross correlation with the coeveal Lyman-alpha forest using the one hundred spectra of z=3.5-4.5 quasars in the VLT/X-Shooter XQ-100 Legacy Survey, motivated by the Lyman-beta transitions smaller absorption cross section that makes it sensitive to somewhat higher densities relative to the Lyman-alpha transition. Our inferences from this measurement for the IGM temperature-density relation appear to latch consistently onto the recent tight lower-redshift Lyman-alpha forest constraints of arXiv:2009.00016v1 [astro-ph.CO]. The z=3.4-4.7 trends we find using the Lyman-alpha--Lyman-beta cross correlation show a flattening of the slope of the temperature-density relation with decreasing redshift. This is the trend anticipated from ongoing HeII reionization and there being sufficient time to reach the asymptotic temperature-density slope after hydrogen reionization completes. Furthermore, our measurements provide a consistency check on IGM models that explain the Lyman-alpha forest, with the cross correlation being immune to systematics that are uncorrelated between the two forests, such as metal line contamination.
We investigate the large-scale structure of Lyman-alpha emission intensity in the Universe at redshifts z=2-3.5 using cross-correlation techniques. Our Lya emission samples are spectra of BOSS Luminous Red Galaxies from Data Release 12 with the best fit model galaxies subtracted. We cross-correlate the residual flux in these spectra with BOSS quasars, and detect a positive signal on scales 1-15 Mpc/h. We identify and remove a source of contamination not previously accounted for, due to the effects of quasar clustering on cross-fibre light. Corrected, our quasar-Lya emission cross-correlation is 50 % lower than that seen by Croft et al. for DR10, but still significant. Because only 3% of space is within 15 Mpc/h of a quasar, the result does not fully explore the global large-scale structure of Lya emission. To do this, we cross-correlate with the Lya forest. We find no signal in this case. The 95% upper limit on the global Lya mean surface brightness from Lya emission-Lya forest cross-correlation is mu < 1.2x10^-22 erg/s/cm^2/A/arcsec^2 This null result rules out the scenario where the observed quasar-Lya emission cross-correlation is primarily due to the large scale structure of star forming galaxies, Taken in combination, our results suggest that Lya emitting galaxies contribute, but quasars dominate within 15 Mpc/h. A simple model for Lya emission from quasars based on hydrodynamic simulations reproduces both the observed forest-Lya emission and quasar-Lya emission signals. The latter is also consistent with extrapolation of observations of fluorescent emission from smaller scales r < 1 Mpc.
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 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.
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