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Register a new userThe broadening of Lyman-alpha forest absorption lines
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We provide an analytical description of the line broadening of HI absorbers in the Lyman-alpha forest resulting from Doppler broadening and Jeans smoothing. We demonstrate that our relation captures the dependence of the line-width on column density for narrow lines in z~3 mock spectra remarkably well. Broad lines at a given column density arise when the underlying density structure is more complex, and such clustering is not captured by our model. Our understanding of the line broadening opens the way to a new method to characterise the thermal state of the intergalactic medium and to determine the sizes of the absorbing structures.
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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 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.
In recent years, the autocorrelation of the hydrogen Lyman-{alpha} forest has been used to observe the baryon acoustic peak at redshift 2 < z < 3.5 using tens of thousands of QSO spectra from the BOSS survey. However, the interstellar medium of the Milky-Way introduces absorption lines into the spectrum of any extragalactic source. These lines, while weak and undetectable in a single BOSS spectrum, could potentially bias the cosmological signal. In order to examine this, we generate absorption line maps by stacking over a million spectra of galaxies and QSOs. We find that the systematics introduced are too small to affect the current accuracy of the baryon acoustic peak, but might be relevant to future surveys such as the Dark Energy Spectroscopic Instrument (DESI). We outline a method to account for this with future datasets.
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 lya forest at high redshifts is a powerful probe of reionization. Modeling and observing this imprint comes with significant technical challenges: inhomogeneous reionization must be taken into account while simultaneously being able to resolve the web-like small-scale structure prior to reionization. In this work we quantify the impact of inhomogeneous reionization on the lya forest at lower redshifts ($2 < z < 4$), where upcoming surveys such as DESI will enable precision measurements of the flux power spectrum. We use both small box simulations capable of handling the small-scale structure of the lya forest and semi-numerical large box simulations capable of representing the effects of inhomogeneous reionization. We find that inhomogeneous reionization could produce a measurable effect on the lya forest power spectrum. The deviation in the 3D power spectrum at $z_{rm obs} = 4$ and $k = 0.14 rm{Mpc}^{-1}$ ranges from $19 - 36%$, with a larger effect for later reionization. The corrections decrease to $2.0 - 4.1%$ by $z_{rm obs} = 2$. The impact on the 1D power spectrum is smaller, and ranges from $3.3 - 6.5%$ at $z_{rm obs}=4$ to $0.35 - 0.75%$ at $z_{rm obs}=2$, values which are comparable to the statistical uncertainties in current and upcoming surveys. Furthermore, we study how can this systematic be constrained with the help of the quadrupole of the 21 cm power spectrum.
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