Virgo Filaments VIII: Characterizing the Structural Parameters of Virgo Galaxies with Machine Learning to Probe Environmental Quenching
Abstract
A persistent challenge in galaxy evolution involves disentangling the many correlated properties in order to isolate the effects of a galaxy's environment on its star formation history. To address this multidimensionality problem, we apply k-means clustering to 2831 galaxies in the extended regions around the Virgo cluster to define objective, reproducible subsets of structurally similar galaxies. Using measurements of size, light distribution, and stellar mass, k-means partitions these galaxies into three feature classes (FCs): dwarfs, spheroids, and large disks. In addition to being structurally different, these FCs show distinct offsets from the star-forming main sequence to >3σ significance, with the spheroid population systematically shifted to lower star formation rates. Examining environmental dependence within each FC, we find that denser environments are associated with progressively stronger quenching. However, star formation for the dwarf and large disk galaxies is not strongly affected until the rich group and cluster environments. For the spheroid galaxies, star formation instead smoothly decreases as environment density increases. We verify that these trends are not driven by differences in Sersic index within each environment, suggesting that the effectiveness of environmental quenching depends on the galaxy's structural class. Our results speak to the utility of a simple machine learning model to create broad classes of structurally similar galaxies based on a small set of parameters, which has important implications for navigating this data rich era of astronomy.
Related
- The Structural Anatomy of Galaxies: Environmental Dependence of Morphological and Quenching Evolution at 0.1 < z < 0.5
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BibTeX
@misc{conger2026virgo,
title = {{Virgo Filaments VIII: Characterizing the Structural Parameters of Virgo Galaxies with Machine Learning to Probe Environmental Quenching}},
author = {Kim Conger and Gregory Rudnick and Rose A. Finn and Rebecca A. Koopmann and Benedetta Vulcani and Dennis Zaritsky and Gianluca Castignani and Francoise Combes and Gabriella De Lucia and Matteo Fossati and Gautam Nagaraj and Daria Zakharova},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01739},
eprint = {2609.01739},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01739},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Virgo Filaments VIII: Characterizing the Structural Parameters of Virgo Galaxies with Machine Learning to Probe Environmental Quenching
AU - Kim Conger
AU - Gregory Rudnick
AU - Rose A. Finn
AU - Rebecca A. Koopmann
AU - Benedetta Vulcani
AU - Dennis Zaritsky
AU - Gianluca Castignani
AU - Francoise Combes
AU - Gabriella De Lucia
AU - Matteo Fossati
AU - Gautam Nagaraj
AU - Daria Zakharova
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.01739
UR - https://arxiv.org/abs/2609.01739
AB - A persistent challenge in galaxy evolution involves disentangling the many correlated properties in order to isolate the effects of a galaxy's environment on its star formation history. To address this multidimensionality problem, we apply k-means clustering to 2831 galaxies in the extended regions around the Virgo cluster to define objective, reproducible subsets of structurally similar galaxies. Using measurements of size, light distribution, and stellar mass, k-means partitions these galaxies into three feature classes (FCs): dwarfs, spheroids, and large disks. In addition to being structurally different, these FCs show distinct offsets from the star-forming main sequence to >3σ significance, with the spheroid population systematically shifted to lower star formation rates. Examining environmental dependence within each FC, we find that denser environments are associated with progressively stronger quenching. However, star formation for the dwarf and large disk galaxies is not strongly affected until the rich group and cluster environments. For the spheroid galaxies, star formation instead smoothly decreases as environment density increases. We verify that these trends are not driven by differences in Sersic index within each environment, suggesting that the effectiveness of environmental quenching depends on the galaxy's structural class. Our results speak to the utility of a simple machine learning model to create broad classes of structurally similar galaxies based on a small set of parameters, which has important implications for navigating this data rich era of astronomy.
ER -