44 citations · 90 across the 3 of their papers we have counts for
6 papers
Illustrating galaxy-halo connection in the DESI era with IllustrisTNG
Sihan Yuan, Boryana Hadzhiyska, Sownak Bose +1
We employ the hydrodynamical simulation IllustrisTNG to inform the galaxy-halo connection of the Luminous Red Galaxy (LRG) and Emission Line Galaxy (ELG) samples of the Dark Energy…
Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function
Sihan Yuan, Boryana Hadzhiyska, Sownak Bose +2
Building accurate and flexible galaxy-halo connection models is crucial in modeling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by its…
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…
Exploring the squeezed three-point galaxy correlation function with generalized halo occupation distribution models
Sihan Yuan, Daniel J. Eisenstein, Lehman H. Garrison
We present the GeneRalized ANd Differentiable Halo Occupation Distribution (GRAND-HOD) routine that generalizes the standard 5 parameter halo occupation distribution model (HOD) wi…