Research

Local Group Analogs in a cosmological context: Relating the velocity structure to the cosmic web

Date:2025-05-06

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Title:Local Group Analogs in a cosmological context: Relating the velocity structure to the cosmic web

Time:2025-06-11,15:00

Speaker:Kai Wang 王凯 (Durham)

Address:Physics Building E225

Language:English

主讲人 Kai Wang 王凯 (Durham) 时间 2025-06-11,15:00
地点 Physics Building E225 报告语言 English
办公室

The Local Group (LG), as a gravitationally bound system of the Milky Way and Andromeda, as well as their satellites, is a cornerstone of near-field cosmology. However, its utility as a cosmological probe requires understanding how it is related to the cosmic web. Using the ABACUSSUMMIT simulation, we identify LG analogues and quantify their environmental dependence. We find that the coupling energy of LG-analogue systems strongly correlates with large-scale overdensity, revealing a secondary bias effect. Crucially, we demonstrate that the LG-analogues are aligned to the anisotropic part of the cosmic web, and the alignment pattern is dependent on the coupling energy of the system. Our results underscore the role of non-local environmental effects in shaping LG-like systems and argue against treating the LG as an isolated system. Instead, we advocate for integrating the large-scale cosmic web into studies of LG analogues.

BIO

Dr. Kai Wang is a Postdoctoral Research Associate at the Institute for Computational Cosmology and Centre for Extragalactic Astrophysics of Durham University. He received his BS (2017) from the University of Science and Technology of China and PhD (2022) from Tsinghua University. Prior to joining Durham, he was a KIAA Postdoctoral Fellow at the Kavli Institute for Astronomy and Astrophysics of Peking University (2022-2024). Dr. Wang's research focuses on galaxy formation and evolution within dark matter halos in a cosmological context, combining theoretical modeling with multi-wavelength observations. His work bridges semi-analytic approaches, hydrodynamic simulations, and observational datasets to understand how galaxies form and evolve within the cosmic web.

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