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arXiv · 2512.18693

Towards Understanding the Milky Way's Matter Field and Dynamical Accretion History based on AI-GS3 Hunter

Hai-Feng Wang·Guan-Yu Wang·Giovanni Carraro·Yuan-Sen Ting·Thor Tepper-Garcia·Joss Bland-Hawthorn·Jeffrey Carlin·Yang-Ping Luo
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Abstract

We present GS3 Hunter (Galactic-Seismology Substructures and Streams Hunter), a novel deep-learning method that combines Siamese Neural Networks and K-means clustering to identify substructures and streams in stellar kinematic data. Applied to Gaia EDR3 and GALAH DR3, it recovers known groups (e.g., Thamnos, Helmi, GSE, Sequoia) and, with DESI dataset, reveals that GSE consists of four distinct components (GSH-GSH1 through GSE-GSH4), implying a multi-event accretion origin. Tests on LAMOST K-giants recover Sagittarius, Hercules-Aquila, and Virgo Overdensity, while also uncovering new substructures. Validation with FIRE simulations shows good agreement with previous results. GS3 Hunter thus offers a powerful tool to understand the Milky Way's halo assembly and tidal history.

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Hai-Feng Wang, Guan-Yu Wang, Giovanni Carraro, Yuan-Sen Ting, Thor Tepper-Garcia, Joss Bland-Hawthorn, Jeffrey Carlin, Yang-Ping Luo. 2025-12-21. Towards Understanding the Milky Way's Matter Field and Dynamical Accretion History based on AI-GS3 Hunter. https://arxiv.org/abs/2512.18693

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