Including parameter dependence in the data and covariance for cosmological inference

Abstract

The final step of most large-scale structure analyses involves the comparison of power spectra or correlation functions to theoretical models. It is clear that the theoretical models have parameter dependence, but frequently the measurements and the covariance matrix depend upon some of the parameters as well. We show that a very simple interpolation scheme from an unstructured mesh allows for an efficient way to include this parameter dependence self-consistently in the analysis at modest computational expense. We describe two schemes for covariance matrices. The scheme which uses the geometric structure of such matrices performs roughly twice as well as the simplest scheme, though both perform very well.

17 pages, 4 figures, matches version published in JCAP

References in corpus (6)

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• Improving Cosmological Distance Measurements by Reconstruction of the Baryon Acoustic Peak
• Reconstruction within the Zeldovich approximation
• Tests of redshift-space distortions models in configuration space for the analysis of the BOSS final data release
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Cited by in corpus (14)

• The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: RSD measurement from the power spectrum and bispectrum of the DR12 BOSS galaxies
• Dark Energy Survey Year 1 Results: Measurement of the Baryon Acoustic Oscillation scale in the distribution of galaxies to redshift 1
• Cosmological constraints from BOSS with analytic covariance matrices
• Dark Energy Survey Year 3 Results: Covariance Modelling and its Impact on Parameter Estimation and Quality of Fit
• Looking through the same lens: shear calibration for LSST, Euclid & WFIRST with stage 4 CMB lensing
• Response Approach to the Matter Power Spectrum Covariance
• Likelihood Non-Gaussianity in Large-Scale Structure Analyses
• A Bayesian method for combining theoretical and simulated covariance matrices for large-scale structure surveys
• Application of Bayesian graphs to SN Ia data analysis and compression
• Robustness of the covariance matrix for galaxy clustering measurements
• Flow-Based Likelihoods for Non-Gaussian Inference
• Cosmological Model Parameter Dependence of the Matter Power Spectrum Covariance from the DEUS-PUR $Cosmo$ Simulations
• Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does)
• Super sample covariance and the volume scaling of galaxy survey covariance matrices