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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DESI LAE clustering measurements give a linear bias of 2.31-2.62 with constraints on radiative transfer effects and halo occupation from correlation functions and power spectra.
A control variate technique using differenced estimates from realistic masked and isotropic simulations reduces the computational cost of CMB lensing bias calculations by a factor of three to five.
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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The 3D clustering of Lyman Alpha Emitters measured with DESI
DESI LAE clustering measurements give a linear bias of 2.31-2.62 with constraints on radiative transfer effects and halo occupation from correlation functions and power spectra.
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Faster CMB lensing with control variates
A control variate technique using differenced estimates from realistic masked and isotropic simulations reduces the computational cost of CMB lensing bias calculations by a factor of three to five.