Control variates with Zeldovich mocks reduce covariance matrix variance by up to an order of magnitude on large scales in DESI-like mocks.
The Effect of Covariance Estimator Error on Cosmological Parameter Constraints
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Extracting parameter constraints from cosmological observations requires accurate determination of the covariance matrix for use in the likelihood function. We show here that uncertainties in the elements of the covariance matrix propagate directly to increased uncertainties in cosmological parameters. When the covariance matrix is determined by simulations, the resulting variance of the each parameter increases by a factor of order $1+N_b/N_s$ where $N_b$ is the number of bands in the measurement and $N_s$ is the number of simulations.
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Using idealized synthetic data, knowing the true continuum in Lyα forest auto- and cross-correlations reduces uncertainties on the AP parameter and Ω_m by ~10%, with extension to 240 h^{-1}Mpc scales adding up to ~15% further improvement equivalent to a 40% larger survey area.
FolpsD combines EFT power spectrum and tree-level bispectrum with damping to enable joint analyses that improve cosmological constraints from DESI-like galaxy mocks by up to 30% on As and omega_cdm while extending the usable k-range without significant biases for LRG samples.
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
citing papers explorer
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Fewer simulations, sharper covariances: Reducing mock covariance noise with Zeldovich approximation control variates
Control variates with Zeldovich mocks reduce covariance matrix variance by up to an order of magnitude on large scales in DESI-like mocks.
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Probing the limits of cosmological information from the Lyman-$\alpha$ forest 2-point correlation functions
Using idealized synthetic data, knowing the true continuum in Lyα forest auto- and cross-correlations reduces uncertainties on the AP parameter and Ω_m by ~10%, with extension to 240 h^{-1}Mpc scales adding up to ~15% further improvement equivalent to a 40% larger survey area.
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FolpsD: combining EFT and phenomenological approaches for joint power spectrum and bispectrum analyses
FolpsD combines EFT power spectrum and tree-level bispectrum with damping to enable joint analyses that improve cosmological constraints from DESI-like galaxy mocks by up to 30% on As and omega_cdm while extending the usable k-range without significant biases for LRG samples.
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Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.