S
Sihan Yuan
Researcher at Smithsonian Institution
Publications - 27
Citations - 336
Sihan Yuan is an academic researcher from Smithsonian Institution. The author has contributed to research in topics: Galaxy & Dark matter. The author has an hindex of 7, co-authored 12 publications receiving 200 citations. Previous affiliations of Sihan Yuan include Stanford University & Harvard University.
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Spectroscopic identification of type 2 quasars at z < 1 in SDSS-III/BOSS
TL;DR: In this paper, the authors presented a sample of 2758 type 2 quasars from the SDSS-III/BOSS spectroscopic database, selected on the basis of their emission-line properties.
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New light on 21 cm intensity fluctuations from the dark ages
TL;DR: In this paper, the effect of the relative velocity on 21 cm brightness temperature fluctuations from redshifts from redshift $z\ensuremath{\ge}30$ is computed and it is shown that the 21 cm power spectrum is affected on most scales.
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Exploring the squeezed three-point galaxy correlation function with generalized halo occupation distribution models
TL;DR: Yuan et al. as mentioned in this paper presented the GeneRalized ANd Differentiable Halo Occupation Distribution (GRAND-HOD) routine that generalizes the standard 5 parameter halo occupation distribution model with various halo-scale physics and assembly bias.
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Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function
TL;DR: In this article, an extended halo occupation distribution model (HOD) is proposed that includes both a concentration-based assembly bias term and an environment-based bias term, and it achieves a good fit (chi 2/DoF = 1.35) on the 2D redshift-space 2-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample.
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A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys
Michelle Ntampaka,Michelle Ntampaka,Daniel J. Eisenstein,Sihan Yuan,Lehman H. Garrison,Lehman H. Garrison +5 more
TL;DR: A deep machine learning (ML)-based technique for accurately determining $\sigma_8$ and $\Omega_m$ from mock 3D galaxy surveys is presented and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs is explored.