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Register a new userA hierarchical field-level inference approach to reconstruction from sparse Lyman-$alpha$ forest data
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We address the problem of inferring the three-dimensional matter distribution from a sparse set of one-dimensional quasar absorption spectra of the Lyman-$alpha$ forest. Using a Bayesian forward modelling approach, we focus on extending the dynamical model to a fully self-consistent hierarchical field-level prediction of redshift-space quasar absorption sightlines. Our field-level approach rests on a recently developed semiclassical analogue to Lagrangian perturbation theory (LPT), which improves over noise problems and interpolation requirements of LPT. It furthermore allows for a manifestly conservative mapping of the optical depth to redshift space. In addition, this new dynamical model naturally introduces a coarse-graining scale, which we exploited to accelerate the Markov chain Monte-Carlo (MCMC) sampler using simulated annealing. By gradually reducing the effective temperature of the forward model, we were able to allow it to first converge on large spatial scales before the sampler became sensitive to the increasingly larger space of smaller scales. We demonstrate the advantages, in terms of speed and noise properties, of this field-level approach over using LPT as a forward model, and, using mock data, we validated its performance to reconstruct three-dimensional primordial perturbations and matter distribution from sparse quasar sightlines.
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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.
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.
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 present constraints on the masses of extremely light bosons dubbed fuzzy dark matter from Lyman-$alpha$ forest data. Extremely light bosons with a De Broglie wavelength of $sim 1$ kpc have been suggested as dark matter candidates that may resolve some of the current small scale problems of the cold dark matter model. For the first time we use hydrodynamical simulations to model the Lyman-$alpha$ flux power spectrum in these models and compare with the observed flux power spectrum from two different data sets: the XQ-100 and HIRES/MIKE quasar spectra samples. After marginalization over nuisance and physical parameters and with conservative assumptions for the thermal history of the IGM that allow for jumps in the temperature of up to $5000rm,K$, XQ-100 provides a lower limit of 7.1$times 10^{-22}$ eV, HIRES/MIKE returns a stronger limit of 14.3$times 10^{-22}$ eV, while the combination of both data sets results in a limit of 20 $times 10^{-22}$ eV (2$sigma$ C.L.). The limits for the analysis of the combined data sets increases to 37.5$times 10^{-22}$ eV (2$sigma$ C.L.) when a smoother thermal history is assumed where the temperature of the IGM evolves as a power-law in redshift. Light boson masses in the range $1-10 times10^{-22}$ eV are ruled out at high significance by our analysis, casting strong doubts that FDM helps solve the small scale crisis of the cold dark matter models.
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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