Many different studies have shown that a wealth of cosmological information resides on small, non-linear scales. Unfortunately, there are two challenges to overcome to utilize that information. First, we do not know the optimal estimator that will allow us to retrieve the maximum information. Second, baryonic effects impact that regime significantly and in a poorly understood manner. Ideally, we would like to use an estimator that extracts the maximum cosmological information while marginalizing over baryonic effects. In this work we show that neural networks can achieve that. We made use of data where the maximum amount of cosmological information is known: power spectra and 2D Gaussian density fields. We also contaminate the data with simplified baryonic effects and train neural networks to predict the value of the cosmological parameters. For this data, we show that neural networks can 1) extract the maximum available cosmological information, 2) marginalize over baryonic effects, and 3) extract cosmological information that is buried in the regime dominated by baryonic physics. We also show that neural networks learn the priors of the data they are trained on. We conclude that a promising strategy to maximize the scientific return of cosmological experiments is to train neural networks on state-of-the-art numerical simulations with different strengths and implementations of baryonic effects.
Baryonic effects are amongst the most severe systematics to the tomographic analysis of weak lensing data which is the principal probe in many future generations of cosmological surveys like LSST, Euclid etc.. Modeling or parameterizing these effects is essential in order to extract valuable constraints on cosmological parameters. In a recent paper, Eifler et al. (2015) suggested a reduction technique for baryonic effects by conducting a principal component analysis (PCA) and removing the largest baryonic eigenmodes from the data. In this article, we conducted the investigation further and add…
We develop an extension of \textit{the Halo Model} that describes analytically the corrections to the matter power spectrum due to the physics of baryons. We extend these corrections to the weak-lensing shear angular power spectrum. Within each halo, our baryonic model accounts for: 1) a central galaxy, the major stellar component whose properties are derived from abundance matching techniques; 2) a hot plasma in hydrostatic equilibrium and 3) an adiabatically-contracted dark matter component. This analytic approach allows us to compare our model to the dark-matter-only case. Our basic assumpt…
We present a novel statistic to extract cosmological information in weak lensing data: the lensing minima. We also investigate the effect of baryons on the cosmological constraints from peak and minimum counts. Using the \texttt{MassiveNuS} simulations, we find that lensing minima are sensitive to non-Gaussian cosmological information and are complementary to the lensing power spectrum and peak counts. For an LSST-like survey, we obtain $95\%$ credible intervals from a combination of lensing minima and peaks that are significantly stronger than from the power spectrum alone, by $44\%$, $11\%$,…
We develop a model for the matter power spectrum as the sum of Zeldovich approximation and even powers of $k$, i.e., $A_0 - A_2k^2 + A_4k^4 - ...$, compensated at low $k$. With terms up to $k^4$ the model can predict the true power spectrum to a few percent accuracy up to $k\sim 0.7 h \rm{Mpc}^{-1}$, over a wide range of redshifts and models. The $A_n$ coefficients contain information about cosmology, in particular amplitude of fluctuations. We write a simple form of the covariance matrix as a sum of Gaussian part and $A_0$ variance, which reproduces the simulations remarkably well. In contras…
Fast N-body PM simulations with a small number of time steps such as FastPM or COLA have been remarkably successful in modeling the galaxy statistics, but their lack of small scale force resolution and long time steps cannot give accurate halo matter profiles or matter power spectrum. High resolution N-body simulations can improve on this, but lack baryonic effects, which can only be properly included in hydro simulations. Here we present a scheme to calibrate the fast simulations to mimic the precision of the hydrodynamic simulations or high resolution N-body simulations. The scheme is based …
Interpreting Cosmological Information from Neural Networks in the Hydrodynamic Universe
Foundation for Research and Technology—Hellas（希腊研究与技术基金会）；Department of Physics, University of Crete（克里特大学物理系）；Department of Astrophysical Sciences, Princeton University（普林斯顿大学天体物理学系）；Center for Computational Astrophysics, Flatiron Institute（平托研究所计算天体物理中心）
We present an optimized variant of the halo model, designed to produce accurate matter power spectra well into the non-linear regime for a wide range of cosmological models. To do this, we introduce physically motivated free parameters into the halo-model formalism and fit these to data from high-resolution $N$-body simulations. For a variety of $Λ$CDM and $w$CDM models the halo-model power is accurate to $\simeq 5$ per cent for $k\leq 10h\,\mathrm{Mpc}$ and $z\leq 2$. An advantage of our new halo model is that it can be adapted to account for the effects of baryonic feedback on the power spec…
We present measurements of the spatial mapping between (hot) baryons and the total matter in the Universe, via the cross-correlation between the thermal Sunyaev-Zeldovich (tSZ) map from Planck and the weak gravitational lensing maps from the Red Sequence Cluster Survey (RCSLenS). The cross-correlations are performed on the map level where all the sources (including diffuse intergalactic gas) contribute to the signal. We consider two configuration-space correlation function estimators, $ξ^{ y-κ}$ and $ξ^ {y-γ_{t}}$, and a Fourier space estimator, $C_{\ell}^{y-κ}$, in our analysis. We detect a s…
One of the main unsolved problems of cosmology is how to maximize the extraction of information from nonlinear data. If the data are nonlinear the usual approach is to employ a sequence of statistics (N-point statistics, counting statistics of clusters, density peaks or voids etc.), along with the corresponding covariance matrices. However, this approach is computationally prohibitive and has not been shown to be exhaustive in terms of information content. Here we instead develop a Bayesian approach, expanding the likelihood around the maximum posterior of linear modes, which we solve for usin…
We quantify the information content of the non-linear matter power spectrum, the halo mass function, and the void size function, using the Quijote $N$-body simulations. We find that these three statistics exhibit very different degeneracies amongst the cosmological parameters, and thus the combination of all three probes enables the breaking of degeneracies, in turn yielding remarkably tight constraints. We perform a Fisher analysis using the full covariance matrix, including all auto- and cross-correlations, finding that this increases the information content for neutrino mass compared to a c…
The kinematic Sunyaev-Zel'dovich (kSZ) effect --- the Doppler boosting of cosmic microwave background (CMB) photons due to Compton-scattering off free electrons with non-zero bulk velocity --- probes the abundance and distribution of baryons in the Universe. All kSZ measurements to date have explicitly required spectroscopic redshifts. Here, we implement a novel estimator for the kSZ -- large-scale structure cross-correlation based on projected fields: it does not require redshift estimates for individual objects, allowing kSZ measurements from large-scale imaging surveys. We apply this estima…
Recent CMB observations have resulted in very precise observational data. A robust and reliable CMB reconstruction technique can lead to efficient estimation of the cosmological parameters. We demonstrate the performance of our methodology using simulated temperature and polarization observations using cosmic variance limited future generation PRISM satellite mission. We generate samples from the joint distribution by implementing the CMB inverse covariance weighted internal-linear-combination (ILC) with the Gibbs sampling technique. We use the Python Sky Model (PySM), d4f1s1 to generate the r…
The results of joint analyses of available cosmological data have motivated an important debate about a possible detection of a non-zero spatial curvature. If confirmed, such a result would imply a change in our present understanding of cosmic evolution with important theoretical and observational consequences. In this paper we discuss the legitimacy of carrying out joint analyses with the currently available data sets and explore their implications for a non-flat universe and extensions of the standard cosmological model. We use a robust tension estimator to perform a quantitative analysis of…
Cosmological inference from the eBOSS QSO full-shape analysis with optimal redshift weights
National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）；School of Astronomy and Space Sciences, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学天文与天体物理前沿研究院）
We derive cosmological constraints on the matter density, \om, and the amplitude of fluctuations, \sig, using $\mathtt{GalWCat19}$, a catalog of 1800 galaxy clusters we identified in the Sloan Digital Sky Survey-DR13 spectroscopic data set using our GalWeight technique to determine cluster membership \citep{Abdullah18,Abdullah19}. By analyzing a subsample of 756 clusters in a redshift range of $0.045\leq z \leq 0.125$ and virial masses of $M\geq 0.8\times10^{14}$ \hm ~with mean redshift of $z = 0.085$, we obtain \om ~$=0.310^{+0.023}_{-0.027} \pm 0.041$ (systematic) and \sig ~$=0.810^{+0.031}_…
