The Information Content of Projected Galaxy Fields

Abstract

The power spectrum of the nonlinearly evolved large-scale mass distribution recovers only a minority of the information available on the mass fluctuation amplitude. We investigate the recovery of this information in 2D "slabs" of the mass distribution averaged over $\approx 100$~$h^{-1}$Mpc along the line of sight, as might be obtained from photometric redshift surveys. We demonstrate a Hamiltonian Monte Carlo (HMC) method to reconstruct the non-Gaussian mass distribution in slabs, under the assumption that the projected field is a point-transformed Gaussian random field, Poisson-sampled by galaxies. When applied to the \textit{Quijote} $N$-body suite at $z=0.5$ and at a transverse resolution of 2~$h^{-1}$Mpc, the method recovers $\sim 30$ times more information than the 2D power spectrum in the well-sampled limit, recovering the Gaussian limit on information. At a more realistic galaxy sampling density of $0.01$~$h^3$Mpc${}^{-3}$, shot noise reduces the information gain to a factor of 5 improvement over the power spectrum at resolutions of 4~$h^{-1}$Mpc or smaller.

13 pages, 11 figures. Matches published version

References in corpus (10)

• Array Programming with NumPy
• Why your model parameter confidences might be too optimistic -- unbiased estimation of the inverse covariance matrix
• ELUCID - Exploring the Local Universe with reConstructed Initial Density field I: Hamiltonian Markov Chain Monte Carlo Method with Particle Mesh Dynamics
• Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization
• Rejuvenating Power Spectra II: the Gaussianized galaxy density field
• Lifting weak lensing degeneracies with a field-based likelihood
• Re-capturing cosmic information
• Information Content in the Galaxy Angular Power Spectrum from the Sloan Digital Sky Survey and Its Implication on Weak Lensing Analysis
• Impacts of the physical data model on the forward inference of initial conditions from biased tracers
• On the accuracy and precision of correlation functions and field-level inference in cosmology

Cited by in corpus (4)

• The CAMELS project: public data release
• Quantification of high dimensional non-Gaussianities and its implication to Fisher analysis in cosmology
• Almanac: MCMC-based signal extraction of power spectra and maps on the sphere
• On the Connection between Field-Level Inference and $n$-point Correlation Functions