activity
20102023
most citedSecond Data Release of the Hyper Suprime-Cam Subaru Strategic Program

514 citations · 2.9k across the 43 of their papers we have counts for

collaborators

43 papers

astro-ph.GA2023

Unraveling the Complexity of Dwarf Galaxy Dynamics: A study of Binary Orbital Motions

Wenting Wang, Ling Zhu, Yipeng Jing +8

We investigate the impact of binary orbital motions on the dynamical modeling of dwarf galaxies with intrinsic line-of-sight velocity dispersions () of 1 to 9 km/s. Using…

astro-ph.GA2022★ 150 cited

Overview of the DESI Milky Way Survey

Andrew P. Cooper, Sergey E. Koposov, Carlos Allende Prieto +62

We describe the Milky Way Survey (MWS) that will be undertaken with the Dark Energy Spectroscopic Instrument (DESI) on the Mayall 4m telescope at the Kitt Peak National Observatory…

astro-ph.GA2022★ 11 cited

Is the core-cusp problem a matter of perspective: Jeans Anisotropic Modeling against numerical simulations

Wenting Wang, Ling Zhu, Zhaozhou Li +14

Mock member stars for 28 dwarf galaxies are constructed from the cosmological Auriga simulation, which reflect the dynamical status of realistic stellar tracers. The axis-symmetric…

astro-ph.CO2022★ 25 cited

What to expect from dynamical modelling of cluster haloes II. Investigating dynamical state indicators with Random Forest

Qingyang Li, Jiaxin Han, Wenting Wang +5

We investigate the importances of various dynamical features in predicting the dynamical state (DS) of galaxy clusters, based on the Random Forest (RF) machine learning approach. W…

astro-ph.CO2022★ 4 cited

Snowmass2021 Cosmic Frontier White Paper: Prospects for obtaining Dark Matter Constraints with DESI

Monica Valluri, Solene Chabanier, Vid Irsic +29

Despite efforts over several decades, direct-detection experiments have not yet led to the discovery of the dark matter (DM) particle. This has led to increasing interest in altern…

astro-ph.GA2021★ 10 cited

A machine learning approach to infer the accreted stellar mass fractions of central galaxies in the TNG100 simulation

Rui Shi, Wenting Wang, Zhaozhou Li +5

We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions () of central galaxies, based on various dark matter halo…