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Register a new userCombining information from multiple cosmological surveys: inference and modeling challenges
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The tightest and most robust cosmological results of the next decade will be achieved by bringing together multiple surveys of the Universe. This endeavor has to happen across multiple layers of the data processing and analysis, e.g., enhancements are expected from combining Euclid, Rubin, and Roman (as well as other surveys) not only at the level of joint processing and catalog combination, but also during the post-catalog parts of the analysis such as the cosmological inference process. While every experiment builds their own analysis and inference framework and creates their own set of simulations, cross-survey work that homogenizes these efforts, exchanges information from numerical simulations, and coordinates details in the modeling of astrophysical and observational systematics of the corresponding datasets is crucial.
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We present a large-scale Bayesian inference framework to constrain cosmological parameters using galaxy redshift surveys, via an application of the Alcock-Paczynski (AP) test. Our physical model of the non-linearly evolved density field, as probed by galaxy surveys, employs Lagrangian perturbation theory (LPT) to connect Gaussian initial conditions to the final density field, followed by a coordinate transformation to obtain the redshift space representation for comparison with data. We generate realizations of primordial and present-day matter fluctuations given a set of observations. This hierarchical approach encodes a novel AP test, extracting several orders of magnitude more information from the cosmological expansion compared to classical approaches, to infer cosmological parameters and jointly reconstruct the underlying 3D dark matter density field. The novelty of this AP test lies in constraining the comoving-redshift transformation to infer the appropriate cosmology which yields isotropic correlations of the galaxy density field, with the underlying assumption relying purely on the cosmological principle. Such an AP test does not rely explicitly on modelling the full statistics of the field. We verify in depth via simulations that this renders our test robust to model misspecification. This leads to another crucial advantage, namely that the cosmological parameters exhibit extremely weak dependence on the currently unresolved phenomenon of galaxy bias, thereby circumventing a potentially key limitation. This is consequently among the first methods to extract a large fraction of information from statistics other than that of direct density contrast correlations, without being sensitive to the amplitude of density fluctuations. We perform several statistical efficiency and consistency tests on a mock galaxy catalogue, using the SDSS-III survey as template.
Two sources of geometric information are encoded in the galaxy power spectrum: the sound horizon at recombination and the horizon at matter-radiation equality. Analyzing the BOSS DR12 galaxy power spectra using perturbation theory with $Omega_m$ priors from Pantheon supernovae but no priors on $Omega_b$, we obtain constraints on $H_0$ from the second scale, finding $H_0 = 65.1^{+3.0}_{-5.4},mathrm{km},mathrm{s}^{-1}mathrm{Mpc}^{-1}$; this differs from the best-fit of SH0ES at 95% confidence. Similar results are obtained if $Omega_m$ is constrained from uncalibrated BAO: $H_0 = 65.6^{+3.4}_{-5.5},mathrm{km},mathrm{s}^{-1}mathrm{Mpc}^{-1}$. Adding the analogous lensing results from Baxter & Sherwin 2020, the posterior shifts to $70.6^{+3.7}_{-5.0},mathrm{km},mathrm{s}^{-1}mathrm{Mpc}^{-1}$. Using mock data, Fisher analyses, and scale-cuts, we demonstrate that our constraints do not receive significant information from the sound horizon scale. Since many models resolve the $H_0$ controversy by adding new physics to alter the sound horizon, our measurements are a consistency test for standard cosmology before recombination. A simple forecast indicates that such constraints could reach $sigma_{H_0} simeq 1.6,mathrm{km},mathrm{s}^{-1}mathrm{Mpc}^{-1}$ in the era of Euclid.
The angular momentum of dark matter haloes controls their spin magnitude and orientation, which in turn influences the galaxies therein. However, the process by which dark matter haloes acquire angular momentum is not fully understood; in particular, it is unclear whether angular momentum growth is stochastic. To address this question, we extend the genetic modification technique to allow control over the angular momentum of any region in the initial conditions. Using this technique to produce a sequence of modified simulations, we can then investigate whether changes to the angular momentum of a specified region in the evolved universe can be accurately predicted from changes in the initial conditions alone. We find that the angular momentum in regions with modified initial conditions can be predicted between 2 and 4 times more accurately than expected from applying tidal torque theory. This result is masked when analysing the angular momentum of haloes, because particles in the outskirts of haloes dominate the angular momentum budget. We conclude that the angular momentum of Lagrangian patches is highly predictable from the initial conditions, with apparent chaotic behaviour being driven by stochastic changes to the arbitrary boundary defining the halo.
We present $it{CosmoPower}$, a suite of neural cosmological power spectrum emulators providing orders-of-magnitude acceleration for parameter estimation from two-point statistics analyses of Large-Scale Structure (LSS) and Cosmic Microwave Background (CMB) surveys. The emulators replace the computation of matter and CMB power spectra from Boltzmann codes; thus, they do not need to be re-trained for different choices of astrophysical nuisance parameters or redshift distributions. The matter power spectrum emulation error is less than $0.4%$ in the wavenumber range $k in [10^{-5}, 10] , mathrm{Mpc}^{-1}$, for redshift $z in [0, 5]$. $it{CosmoPower}$ emulates CMB temperature, polarisation and lensing potential power spectra in the $5sigma$ region of parameter space around the $it{Planck}$ best fit values with an error $lesssim 20%$ of the expected shot noise for the forthcoming Simons Observatory. $it{CosmoPower}$ is showcased on a joint cosmic shear and galaxy clustering analysis from the Kilo-Degree Survey, as well as on a Stage IV $it{Euclid}$-like simulated cosmic shear analysis. For the CMB case, $it{CosmoPower}$ is tested on a $it{Planck}$ 2018 CMB temperature and polarisation analysis. The emulators always recover the fiducial cosmological constraints with differences in the posteriors smaller than sampling noise, while providing a speed-up factor up to $O(10^4)$ to the complete inference pipeline. This acceleration allows posterior distributions to be recovered in just a few seconds, as we demonstrate in the $it{Planck}$ likelihood case. $it{CosmoPower}$ is written entirely in Python, can be interfaced with all commonly used cosmological samplers and is publicly available https://github.com/alessiospuriomancini/cosmopower .
Tomographic three-dimensional 21 cm images from the epoch of reionization contain a wealth of information about the reionization of the intergalactic medium by astrophysical sources. Conventional power spectrum analysis cannot exploit the full information in the 21 cm data because the 21 cm signal is highly non-Gaussian due to reionization patchiness. We perform a Bayesian inference of the reionization parameters where the likelihood is implicitly defined through forward simulations using density estimation likelihood-free inference (DELFI). We adopt a trained 3D Convolutional Neural Network (CNN) to compress the 3D image data into informative summaries (DELFI-3D CNN). We show that this method recovers accurate posterior distributions for the reionization parameters. Our approach outperforms earlier analysis based on two-dimensional 21 cm images. In contrast, an MCMC analysis of the 3D lightcone-based 21 cm power spectrum alone and using a standard explicit likelihood approximation results in inaccurate credible parameter regions both in terms of the location and shape of the contours. Our proof-of-concept study implies that the DELFI-3D CNN can effectively exploit more information in the 3D 21 cm images than a 2D CNN or power spectrum analysis. This technique can be readily extended to include realistic effects and is therefore a promising approach for the scientific interpretation of future 21 cm observation data.
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