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{\sc SimBIG}: Cosmological Constraints using Simulation-Based Inference of Galaxy Clustering with Marked Power Spectra
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abstract
We present the first $\Lambda$CDM cosmological analysis performed on a galaxy survey using marked power spectra. The marked power spectrum is the two-point function of a marked field, where galaxies are weighted by a function that depends on their local density. The presence of the mark leads these statistics to contain higher-order information of the original galaxy field, making them a good candidate to exploit the non-Gaussian information of a galaxy catalog. In this work we make use of \simbig, a forward modeling framework for galaxy clustering analyses, and perform simulation-based inference using normalizing flows to infer the posterior distribution of the $\Lambda$CDM cosmological parameters. We consider different mark configurations (ways to weight the galaxy field) and deploy them in the \simbig~pipeline to analyze the corresponding marked power spectra measured from a subset of the BOSS galaxy sample. We analyze the redshift-space mark power spectra decomposed in $\ell = 0, 2, 4$ multipoles and include scales up to the non-linear regime. Among the various mark configurations considered, the ones that give the most stringent cosmological constraints produce posterior median and $68\%$ confidence limits on the growth of structure parameters equal to $\Omega_m=0.273^{+0.040}_{-0.030}$ and $\sigma_8=0.777^{+0.077}_{-0.071}$. Compared to a perturbation theory analysis using the power spectrum of the same dataset, the \simbig~marked power spectra constraints on $\sigma_8$ are up to $1.2\times$ tighter, while no improvement is seen for the other cosmological parameters.
Forward citations
Cited by 9 Pith papers
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First Constraints from Marked Angular Power Spectra with Subaru Hyper Suprime-Cam Survey First-Year Data
Marked angular power spectra applied to HSC-Y1 weak lensing data yield S8 = 0.807 ± 0.024, about 43 percent tighter than standard power spectra.
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Analytic Model for Covariance Matrices of the 2-, 3-, and 4-Point Correlation Functions in the Gaussian Random Field Approximation
Closed-form analytic covariance templates for the 2, 3, and 4 point galaxy correlation functions, built from a 1/k power-law power spectrum, reproduce Boltzmann-code results at percent level and trace sparsity to clos...
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Weighted Webs: Morphology-Informed Marked Fields
Morphology-based marks (tidal shear and local fractal dimension) add a modest but complementary ~10% Fisher-information gain over density-only marked power spectra for cosmological parameters.
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Enhancing Cosmological Constraints by Two-dimensional $\beta$-cosmic-web Weighted Angular Correlation Functions
Weighting 2D angular correlation functions by inverse mean neighbor distance in the β-skeleton cosmic web increases the Ωm discrimination measured by Δχ² by a factor of 2-3 relative to standard 2PACF.
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Cosmological Constraints with Void Lensing I: the Simulation-Based Inference Framework
A simulation-based inference pipeline recovers unbiased Omega_m and S8 posteriors from noise-free mock void lensing signals, though realistic shape noise currently erases most of the constraining power.
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The constraining power of the Marked Power Spectrum: an analytical study
An analytic one-loop model of the marked power spectrum with non-local primordial non-Gaussianity is validated against Quijote-PNG simulations and, in Fisher forecasts, does not outperform power spectrum plus bispectr...
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Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI
Jointly training a graph neural network with a normalizing flow yields low-dimensional summary statistics from simulated galaxy catalogs that support likelihood-free inference of Omega_m, and can be interpreted via co...
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Mitigating Model Misspecification in Simulation-Based Inference for Galaxy Clustering
A two-step method (coefficient pruning plus learned robust transformation) fixes model misspecification in the SimBIG wavelet-scattering analysis of BOSS galaxy clustering and produces tight Lambda-CDM constraints.
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Implicit Likelihood Inference of the Neutrino Mass Hierarchy from Cosmological Data
A simulation-based neural-likelihood analysis of Planck 2018 and DESI DR2 data reports a weak preference (tilde_Delta = 0.12, 68% CL interval spanning both signs) for the normal neutrino mass hierarchy.
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