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Published in MNRAS, 2022
Investigation about the tight couplings between dark matter and baryonic matter on galactic scales: the radial vs. vertical.
Recommended citation: Zhu, Y.*, Ma, H.-X. (*co-first author), Dong, X.-B.; Huang, Y.; Mistele, T.; Peng, B.; Long, Q.; Wang, T.; Chang, L.; "How close dark matter haloes and MOND are to each other: three-dimensional tests based on Gaia DR2." 2023, MNRAS. 519, 4479 https://doi.org/10.1093/mnras/stac3483
Published in MNRAS, 2023
Discovery of a 2D Galaxy Manifold which is one of the most efficient representations of galaxies.
Recommended citation: Cooray, S.; Takeuchi, T. T.; Kashino, D.; Yoshida, S. A.; Ma, H.-X.; Kono, K. T.; "Galaxy Manifold: Characterizing and understanding galaxies with two parameters", MNRAS, 2023, 524, 4, 4976 https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.4976C/abstract
Published in arXiv, 2024
A new density-based clustering algorithm, sOPTICS, which outperforms the traditional Friends-of-Friends (FoF) algorithm in identifying galaxy groups and clusters by incorporating a scaling factor to account for line-of-sight positional uncertainties due to redshift space distortions.
Recommended citation: Ma, H.-X.; Takeuchi, T. T.; Cooray, S.; Zhu, Y.; "Unsupervised Machine Learning for Identification of Galaxy Groups: A Comparative Study of Clustering Algorithms " 2024, arXiv e-prints, arXiv:2405.09855 https://arxiv.org/abs/2405.09855
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