From Halos to Galaxies. VI. Improved halo mass estimation for SDSS groups and measurement of the halo mass function
Dingyi Zhao, Yingjie Peng, Yipeng Jing, Xiaohu Yang, Luis C. Ho, Alvio, Renzini, Anna R. Gallazzi, Cheqiu Lyu, Roberto Maiolino, Jing Dou, Zeyu Gao,, Qiusheng Gu, Filippo Mannucci, Houjun Mo, Bitao Wang, Enci Wang, Kai Wang,, Yu-Chen Wang, Bingxiao Xu, Feng Yuan, and Xingye Zhu

TL;DR
This paper introduces a machine learning approach to improve halo mass estimation for SDSS galaxy groups, reducing biases and aligning well with theoretical and observational benchmarks, thus enhancing understanding of galaxy-halo relationships.
Contribution
The study develops a novel ML algorithm trained on simulations that corrects biases in halo mass estimates, outperforming traditional abundance matching methods.
Findings
ML method eliminates systematic bias between blue and red groups
Achieves approximately 33% higher accuracy than AM method
Derived halo mass function aligns with theoretical predictions
Abstract
In CDM cosmology, galaxies form and evolve in their host dark matter (DM) halos. Halo mass is crucial for understanding the halo-galaxy connection. The abundance matching (AM) technique has been widely used to derive the halo masses of galaxy groups. However, quenching of the central galaxy can decouple the coevolution of its stellar mass and DM halo mass. Different halo assembly histories can also result in significantly different final stellar mass of the central galaxies. These processes can introduce substantial uncertainties in the halo masses derived from the AM method, particularly leading to a systematic bias between groups with star-forming centrals (blue groups) and passive centrals (red groups). To improve, we developed a new machine learning (ML) algorithm that accounts for these effects and is trained on simulations. Our results show that the ML method eliminates…
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Taxonomy
TopicsAstronomy and Astrophysical Research · Astronomical Observations and Instrumentation · Stellar, planetary, and galactic studies
