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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Introduction to Clustering Algorithms: DBSCAN, OPTICS, and HDBSCAN
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DBSCAN Pros and Cons Summary
Advantages:
Introduction to Clustering Algorithms: Gaussian Mixture Models
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Gaussian Mixed Model (GMM) refers to a linear combination of multiple Gaussian distribution functions. Gaussian Mixture Models are probabilistic models that assume all samples are derived from a mixture of an indefinite number of Gaussian distributions with unknown parameters. It belongs to the category of soft clustering methods in which each data point will be a member of each cluster in the dataset, but to varying degrees. This membership is assigned as a probability ranging from 0 to 1 of belonging to a certain cluster. Theoretically, GMM can fit any type of distribution, and is usually used to solve cases where data under the same set contains several different distributions (either the same type of distribution but with different parameters, or different types of distributions, such as normal and Bernoulli distributions).
portfolio
Portfolio item number 1
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Short description of portfolio item number 1
Portfolio item number 2
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Short description of portfolio item number 2
publications
How close dark matter haloes and MOND are to each other: three-dimensional tests based on Gaia DR2
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
Galaxy Manifold: Characterizing and understanding galaxies with two parameters
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
Density-based clustering algorithm for galaxy group/cluster identification
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
research
Teaching experience 2
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This is a description of a teaching experience. You can use markdown like any other post.
Research project: MOND vs. Dark Matter
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This is a description of a teaching experience. You can use markdown like any other post.
talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.