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Register a new userModeling the Lyman-alpha Forest in Collisionless Simulations
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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.
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Using a suite of hydrodynamical simulations with cold dark matter, baryons, and neutrinos, we present a detailed study of the effect of massive neutrinos on the 1-D and 3-D flux power spectra of the Lyman-$alpha$ (Ly$alpha$) forest. The presence of massive neutrinos in cosmology induces a scale- and time-dependent suppression of structure formation that is strongest on small scales. Measuring this suppression is a key method for inferring neutrino masses from cosmological data, and is one of the main goals of ongoing and future surveys like eBOSS, DES, LSST, Euclid or DESI. The clustering in the Ly$alpha$ forest traces the quasi-linear power at late times and on small scales. In combination with observations of the cosmic microwave background, the forest therefore provides some of the tightest constraints on the sum of the neutrino masses. However there is a well-known degeneracy between $Sigma m_{ u}$ and the amplitude of perturbations in the linear matter power spectrum. We study the corresponding degeneracy in the 1-D flux power spectrum of the Ly$alpha$ forest, and for the first time also study this degeneracy in the 3-D flux power spectrum. We show that the non-linear effects of massive neutrinos on the Ly$alpha$ forest, beyond the effect of linear power amplitude suppression, are negligible, and this degeneracy persists in the Ly$alpha$ forest observables to a high precision. We discuss the implications of this degeneracy for choosing parametrisations of the Ly$alpha$ forest for cosmological analysis.
In La Plante et al. (2017), we presented a new suite of hydrodynamic simulations with the aim of accurately capturing the process of helium II reionization. In this paper, we discuss the observational signatures present in the He II Ly$alpha$ forest. We show that the effective optical depth of the volume $tau_mathrm{eff}$ is not sufficient for capturing the ionization state of helium II, due to the large variance inherent in sightlines. However, the He II flux PDF can be used to determine the timing of helium II reionization. The amplitude of the one-dimensional flux power spectrum can also determine the ionization state of helium II. We show that even given the currently limited number of observations ($sim$50 sightlines), measurements of the flux PDF can yield information about helium II reionization. Further, measurements using the one-dimensional power spectrum can provide clear indications of the timing of reionization, as well as the relative bias of sources of ionizing radiation.
We use the Ly-$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.
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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