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Register a new userThe kinetic SZ tomography with spectroscopic redshift surveys
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The kinetic Sunyaev Zeldovich effect (kSZ) effect is a potentially powerful probe to the missing baryons. However, the kSZ signal is overwhelmed by various contaminations and the cosmological application is hampered by loss of redshift information due to the projection effect. We propose a kSZ tomography method to alleviate these problems, with the aid of galaxy spectroscopic redshift surveys. We propose to estimate the large scale peculiar velocity through the 3D galaxy distribution, weigh it by the 3D galaxy density and adopt the product projected along the line of sight with a proper weighting as an estimator of the true kSZ temperature fluctuation $Theta$. We thus propose to measure the kSZ signal through the $Hat{Theta}$-$Theta$ cross correlation. This approach has a number of advantages (see details in the abstract of the paper). We test the proposed kSZ tomography against non-adiabatic and adiabatic hydrodynamical simulations. We confirm that $hat{Theta}$ is indeed tightly correlated with $Theta$ at $kla 1h/$Mpc, although nonlinearities in the density and velocity fields and nonlinear redshift distortion do weaken the tightness of the $hat{Theta}$-$Theta$ correlation. We further quantify the reconstruction noise in $Hat{Theta}$ from galaxy distribution shot noise. Based on these results, we quantify the applicability of the proposed kSZ tomography for future surveys. We find that, in combination with the BigBOSS-N spectroscopic redshift survey, the PLANCK CMB experiment will be able to detect the kSZ with an overall significance of $sim 50sigma$ and further measure its redshift distribution at many redshift bins over $0<z<2$.
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The thermal Sunyaev-Zeldovich (tSZ) effect directly measures the thermal pressure of free electrons integrated along the line of sight and thus contains valuable information on the thermal history of the universe. However, the redshift information is entangled in the projection along the line of sight. This projection effect severely degrades the power of the tSZ effect to reconstruct the thermal history. We investigate the tSZ tomography technique to recover this otherwise lost redshift information by cross correlating the tSZ effect with galaxies of known redshifts, or alternatively with matter distribution reconstructed from weak lensing tomography. We investigate in detail the 3D distribution of the gas thermal pressure and its relation with the matter distribution, through our adiabatic hydrodynamic simulation and the one with additional gastrophysics including radiative cooling, star formation and supernova feedback. (1) We find a strong correlation between the gas pressure and matter distribution, with a typical cross correlation coefficient r ~ 0.7 at k . 3h/Mpc and z < 2. This tight correlation will enable robust cross correlation measurement between SZ surveys such as Planck, ACT and SPT and lensing surveys such as DES and LSST, at ~20-100{sigma} level. (2) We propose a tomography technique to convert the measured cross correlation into the contribution from gas in each redshift bin to the tSZ power spectrum. Uncertainties in gastrophysics may affect the reconstruction at ~ 2% level, due to the ~ 1% impact of gastrophysics on r, found in our simulations. However, we find that the same gastrophysics affects the tSZ power spectrum at ~ 40% level, so it is robust to infer the gastrophysics from the reconstructed redshift resolved contribution.
Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. Photometric redshifts can be efficiently combined with the cosmic web (CW) extracted from overlapping spectroscopic surveys to improve their accuracy. We apply a similar method using a new generation of photometric redshifts based on a convolution neural network (CNN). The CNN is trained on the SDSS images with the main galaxy sample (SDSS-MGS, $r leq 17.8$) and the GAMA spectroscopic redshifts up tor $sim 19.8$. The mapping of the CW is obtained with 680,000 spectroscopic redshifts from the MGS and BOSS surveys. The redshift probability distribution functions (PDF), which are well calibrated (unbiased and narrow, $leq 120$ Mpc), intercept a few CW structure along the line of sight. Combining these PDFs with the density field distribution provides new photometric redshifts, $z_{web}$, whose accuracy is improved by a factor of two (i.e.,${sigma} sim 0.004(1+z)$) for galaxies with $r leq 17.8$. For half of them, the distance accuracy is better than 10 cMpc. The narrower the original PDF, the larger the boost in accuracy. No gain is observed for original PDFs wider than 0.03. The final $z_{web}$ PDFs also appear well calibrated. The method performs slightly better for passive galaxies than star-forming ones, and for galaxies in massive groups since these populations better trace the underlying large-scale structure. Reducing the spectroscopic sampling by a factor of 8 still improves the photometric redshift accuracy by 25%. Extending the method to galaxies fainter than the MGS limit still improves the redshift estimates for 70% of the galaxies, with a gain in accuracy of 20% at low $z$ where the resolution of the CW is the highest.
Weak gravitational lensing is becoming a mature technique for constraining cosmological parameters, and future surveys will be able to constrain the dark energy equation of state $w$. When analyzing galaxy surveys, redshift information has proven to be a valuable addition to angular shear correlations. We forecast parameter constraints on the triplet $(Omega_m,w,sigma_8)$ for an LSST-like photometric galaxy survey, using tomography of the shear-shear power spectrum, convergence peak counts and higher convergence moments. We find that redshift tomography with the power spectrum reduces the area of the $1sigma$ confidence interval in $(Omega_m,w)$ space by a factor of 8 with respect to the case of the single highest redshift bin. We also find that adding non-Gaussian information from the peak counts and higher-order moments of the convergence field and its spatial derivatives further reduces the constrained area in $(Omega_m,w)$ by a factor of 3 and 4, respectively. When we add cosmic microwave background parameter priors from Planck to our analysis, tomography improves power spectrum constraints by a factor of 3. Adding moments yields an improvement by an additional factor of 2, and adding both moments and peaks improves by almost a factor of 3, over power spectrum tomography alone. We evaluate the effect of uncorrected systematic photometric redshift errors on the parameter constraints. We find that different statistics lead to different bias directions in parameter space, suggesting the possibility of eliminating this bias via self-calibration.
Galaxy redshift surveys are one of the pillars of the current standard cosmological model and remain a key tool in the experimental effort to understand the origin of cosmic acceleration. To this end, the next generation of surveys aim at achieving sub-percent precision in the measurement of the equation of state of dark energy $w(z)$ and the growth rate of structure $f(z)$. This however requires comparable control over systematic errors, stressing the need for improved modelling methods. In this contribution we review at the introductory level some highlights of the work done in this direction by the {it Darklight} project. Supported by an ERC Advanced Grant, {it Darklight} developed novel techniques for clustering analysis, which were tested through numerical simulations before being finally applied to galaxy data as in particular those of the recently completed VIPERS redshift survey. We focus in particular on: (a) advances on estimating the growth rate of structure from redshift-space distortions; (b) parameter estimation through global Bayesian reconstruction of the density field from survey data; (c) impact of massive neutrinos on large-scale structure measurements. Overall, {it Darklight} has contributed to paving the way for forthcoming high-precision experiments, such as {it Euclid}, the next ESA cosmological mission.
Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., Vsurvey is roughly 10 Gpc3) to be covered, and thus tends to be expensive. A sparse sampling method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, Vsurvey, we observe only a fraction of the volume. The distribution of observed regions should be chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of Vsurvey (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by Vsurvey (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys.
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