星系天体物理：2025–2026 年度综述

An annual survey of the astro-ph.GA arXiv category, covering research published from September 2025 – August 2026. Built on a close-reading set of 108 papers and organised into 40 chapters, it maps the year by the field's research sub-directions.

Book framework (share of the year's output):

• Supermassive Black Hole Growth and Galactic Evolution (79.7%) — Investigating the co-evolution of supermassive black holes and their host galaxies through observational surveys and theoretical modeling of accretion, feedback, and merger processes.
• Interstellar Medium Chemistry and Molecular Cloud Structure (7.6%) — Characterizing the physical and chemical conditions of molecular clouds and the interstellar medium, including organic molecule formation, turbulence, and magnetic field effects.
• Dark Matter Halo Dynamics and Local Group Structure (7.6%) — Constraining dark matter halo properties and the dynamical evolution of the Milky Way, Local Group, and dwarf galaxies using kinematic data and cosmological simulations.
• Gravitational Lensing and Radio Continuum Surveys (1.8%) — Using gravitational lensing and high-resolution radio/submillimeter observations to study faint sources, mergers, and the background gravitational wave signal.
• Supernova Remnants and Stellar Feedback Processes (1.2%) — Modeling supernova explosions, their remnants, and the role of stellar feedback in shaping galaxy evolution through winds, nucleosynthesis, and emission-line diagnostics.
• Star Formation Scaling Relations and Galaxy Evolution (1.0%) — Measuring star formation rates, stellar masses, and dust properties across cosmic time to establish scaling relations and understand galaxy evolution from high redshift to present.
• Nuclear Disks, Bars, and Galactic Center Dynamics (0.4%) — Studying the dynamical structure of galactic centers, including nuclear disks, bars, and star clusters, using proper motion measurements and high-resolution observations.
• Machine Learning Classification and Cosmological Probes (0.4%) — Applying machine learning techniques to galaxy morphological classification and developing new probes for cosmology, dark matter, and fundamental physics.
• Stellar Population Tracers and Distance Scale Calibration (0.2%) — Using stellar populations, chemical abundance gradients, and distance indicators to trace galactic structure and constrain the cosmic distance scale.

Each sub-direction is covered across three chapters — how it framed its questions, the strength of its key findings, and its open debates and frontiers — so the reader gets a traceable picture of what this part of the field actually produced over the year.