98 citations · 687 across the 22 of their papers we have counts for
3 papers · 1 filter
A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys
Michelle Ntampaka, Daniel J. Eisenstein, Sihan Yuan +1
We present a deep machine learning (ML)-based technique for accurately determining and from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos sui…
Can Assembly Bias Explain the Lensing Amplitude of the BOSS CMASS Sample in a Planck Cosmology?
Sihan Yuan, Daniel J. Eisenstein, Alexie Leauthaud
In this paper, we investigate whether galaxy assembly bias can reconcile the 20-40% disagreement between the observed galaxy projected clustering signal and the galaxy-galaxy lensi…
Decorrelating the errors of the galaxy correlation function with compact transformation matrices
Sihan Yuan, Daniel J. Eisenstein
Covariance matrix estimation is a persistent challenge for cosmology, often requiring a large number of synthetic mock catalogues. The off-diagonal components of the covariance mat…