Teaching Galaxies When to Arrive: Using Machine Learning to Time the Fall of Milky Way Satellites

Teaching Galaxies When to Arrive: Using Machine Learning to Time the Fall of Milky Way Satellites

Kim et al. present a machine-learning method to estimate when dwarf galaxies fell into the Milky Way using observable properties like quenching time, stellar mass, and metallicity. Trained on realistic simulations, their model shows that the earliest infall event strongly shapes when star formation stops, especially for low-mass galaxies. The approach is fast, interpretable, and broadly consistent with observations.

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When Galaxies Tug: The Fragile Dance of the Milky Way’s Satellite Plane

When Galaxies Tug: The Fragile Dance of the Milky Way’s Satellite Plane

Pilipenko and Arakelyan study how Andromeda’s gravity affects the Milky Way’s “thin plane” of satellite galaxies. Using simulations based on cosmological models, they find that environmental forces can destabilize this plane within 2–3 billion years. While inner satellites remain stable, distant ones drift away, suggesting the plane is a temporary structure shaped by the Milky Way’s interaction with its cosmic neighborhood.

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Unearthing Ancient Stars: The Hidden History of the Boötes I Dwarf Galaxy

Unearthing Ancient Stars: The Hidden History of the Boötes I Dwarf Galaxy

Muratore et al. (2025) used Hubble and JWST data to study the ultra-faint dwarf galaxy Boötes I, revealing that about 85% of its stars have very low metallicities ([Fe/H] < −2). They found a total binary fraction of ~30%, similar to that in star clusters of comparable mass. These results show Boötes I is an ancient “fossil” galaxy, preserving stars from the Universe’s earliest generations.

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Counting the Milky Way’s Hidden Satellites: The DELVE Census

Counting the Milky Way’s Hidden Satellites: The DELVE Census

Tan and collaborators present the DELVE Milky Way Satellite Census, combining DES, DELVE, and PS1 data to search for faint companion galaxies. Using strict detection methods and efficiency tests, they recovered 49 known satellites and predicted about 265 in total. The results show satellites cluster near the Large Magellanic Cloud and broadly match cosmological predictions, offering key insights into galaxy formation and dark matter.

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Unraveling the Mystery of the Faintest Galaxies: A Deep Dive into Sagittarius II and Aquarius II

Unraveling the Mystery of the Faintest Galaxies: A Deep Dive into Sagittarius II and Aquarius II

Astronomers used the Gemini/GHOST spectrograph to study Sagittarius II (Sgr2) and Aquarius II (Aqu2), two faint Milky Way satellites. Their analysis suggests Aqu2 is a dark matter-dominated ultra-faint dwarf galaxy, while Sgr2 remains ambiguous, possibly a star cluster. Chemical signatures and star movements were key to these classifications. The study highlights the difficulty in distinguishing faint galaxies from clusters and the need for further observations and simulations.

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Unveiling the Structure of Milky Way Satellite Planes: Exploring Planarity in a Cosmic Context

Unveiling the Structure of Milky Way Satellite Planes: Exploring Planarity in a Cosmic Context

The study introduces "planarity" to assess the alignment of Milky Way satellite galaxies, finding significant positional but inconclusive kinematic coherence due to velocity data errors. Simulations reveal that such planarity is common and kinematically supported in MW-like galaxies, aligning with the ΛCDM model. This suggests satellite planes are shaped by cosmic web structures and are consistent with hierarchical galaxy formation theories.

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