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Register a new userReconstructing the Gravitational Lensing Potential from the Lyman-$alpha$ Forest
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We demonstrate a method for reconstructing the weak lensing potential from the Lyman-$alpha$ forest data. We derive an optimal estimator for the lensing potential on the sky based on the correlation between pixels in real space. This method effectively deals with irregularly spaced data, holes in the survey, missing data and inhomogeneous noise. We demonstrate an implementation of the method with simulated spectra and weak lensing. It is shown that with a source density of $>sim 0.5$ per square arcminutes and $sim 200$ pixels in each spectrum ($lambda / Deltalambda = 1300$) the lensing potential can be reconstructed with high fidelity if the relative absorption in the spectral pixels is signal dominated. When noise dominates the measurement of the absorption in each pixel the noise in the lensing potential is higher, but for reasonable numbers of sources and noise levels and a high fidelity map the lensing potential is obtainable. The lensing estimator could also be applied to lensing of the Cosmic Microwave Background (CMB), 21 cm intensity mapping (IM) or any case in which the correlation function of the source can be accurately estimated.
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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 have proposed a method for measuring weak lensing using the Lyman-alpha forest. Here we estimate the noise expected in weak lensing maps and power spectra for different sets of observational parameters. We find that surveys of the size and quality of the ones being done today and ones planned for the future will be able to measure the lensing power spectrum at a source redshift of z~2.5 with high precision and even be able to image the distribution of foreground matter with high fidelity on degree scales. For example, we predict that Lyman-alpha forest lensing measurement from the Dark Energy Spectroscopic Instrument survey should yield the mass fluctuation amplitude with statistical errors of 1.5%. By dividing the redshift range into multiple bins some tomographic lensing information should be accessible as well. This would allow for cosmological lensing measurements at higher redshift than are accessible with galaxy shear surveys and correspondingly better constraints on the evolution of dark energy at relatively early times.
We derive joint constraints on the warm dark matter (WDM) half-mode scale by combining the analyses of a selection of astrophysical probes: strong gravitational lensing with extended sources, the Lyman-$alpha$ forest, and the number of luminous satellites in the Milky Way. We derive an upper limit of $lambda_{rm hm}=0.089{rm~Mpc~h^{-1} }$ at the 95 per cent confidence level, which we show to be stable for a broad range of prior choices. Assuming a Planck cosmology and that WDM particles are thermal relics, this corresponds to an upper limit on the half-mode mass of $M_{rm hm }< 3 times 10^{7} {rm~M_{odot}~h^{-1}}$, and a lower limit on the particle mass of $m_{rm th }> 6.048 {rm~keV}$, both at the 95 per cent confidence level. We find that models with $lambda_{rm hm}> 0.223 {rm~Mpc~h^{-1} }$ (corresponding to $m_{rm th }> 2.552 {rm~keV}$ and $M_{rm hm }< 4.8 times 10^{8} {rm~M_{odot}~h^{-1}}$) are ruled out with respect to the maximum likelihood model by a factor $leq 1/20$. For lepton asymmetries $L_6>10$, we rule out the $7.1 {rm~keV}$ sterile neutrino dark matter model, which presents a possible explanation to the unidentified $3.55 {rm~keV}$ line in the Milky Way and clusters of galaxies. The inferred 95 percentiles suggest that we further rule out the ETHOS-4 model of self-interacting DM. Our results highlight the importance of extending the current constraints to lower half-mode scales. We address important sources of systematic errors and provide prospects for how the constraints of these probes can be improved upon in the future.
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.
We present the BOSS Lyman-alpha (Lya) Forest Sample from SDSS Data Release 9, comprising 54,468 quasar spectra with zqso > 2.15 suitable for Lya forest analysis. This data set probes the intergalactic medium with absorption redshifts 2.0 < z_alpha < 5.7 over an area of 3275 square degrees, and encompasses an approximate comoving volume of 20 h^-3 Gpc^3. With each spectrum, we have included several products designed to aid in Lya forest analysis: improved sky masks that flag pixels where data may be unreliable, corrections for known biases in the pipeline estimated noise, masks for the cores of damped Lya systems and corrections for their wings, and estimates of the unabsorbed continua so that the observed flux can be converted to a fractional transmission. The continua are derived using a principal component fit to the quasar spectrum redwards of restframe Lya (lambda > 1216 Ang), extrapolated into the forest region and normalized by a linear function to fit the expected evolution of the Lya forest mean-flux. The estimated continuum errors are ~5% rms. We also discuss possible systematics arising from uncertain spectrophotometry and artifacts in the flux calibration; global corrections for the latter are provided. Our sample provides a convenient starting point for users to analyze clustering in BOSS Lya forest data, and it provides a fiducial data set that can be used to compare results from different analyses of baryon acoustic oscillations in the Lya forest. The full data set is available from the SDSS-III DR9 web site.
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