Tracing the evolution of galaxy environments with manifold learning
Abstract
We present SONDE (SOrted Neighbor Distance Embedding): a scalable manifold learning approach to create a nuanced, continuous, low-dimensional representation of the geometry of galaxies' local environments. SONDE is a variation on Isomap that uses similarity distances between galaxy neighborhoods to create an embedding space, within which galaxies cluster according to their neighborhood geometries. We additionally present a novel method to project different datasets into the same embedding space, enabling comparisons of environmental embeddings across redshifts. We demonstrate this method on the TNG100 simulations at z = 0 − 5 and show that the embedding values evolve with redshift and correlate with galaxies' physical properties. We train a neural network to predict physical properties based on environmental features and show that our first embedding dimension outperforms a standard environmental quantifier, the number of neighbors, while the top three dimensions encode as much environmental information as a ten-bin radial profile. Our results are consistent with known density correlations, including galaxy halo mass, central vs. satellite status, and quenching. SONDE provides a straightforward way to encode subtleties of galaxy environments that could have significant implications for future analysis of galaxy-environment interactions.
Related
- Tracing the Host-Galaxy Environments of FRBs with a New Sample of Localized Sources Detected with the DSA-110
- SAMI and TNG-Cluster: tracing galaxy spin and environmental transformation across cluster phase-space and cosmic time
- SAMI and TNG-Cluster: tracing galaxy spin and environmental transformation across cluster phase-space and cosmic time
- Connecting galaxy structure, quenching, and halo environments during cosmic noon
- The impact of relativistic AGN jets on realistic galaxy cluster environments
BibTeX
@misc{uzsoy2026tracing,
title = {{Tracing the evolution of galaxy environments with manifold learning}},
author = {Ana Sofía M. Uzsoy and Claire Lamman and Peixin Zhu and Melanie Weber},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03850},
eprint = {2610.03850},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03850},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Tracing the evolution of galaxy environments with manifold learning
AU - Ana Sofía M. Uzsoy
AU - Claire Lamman
AU - Peixin Zhu
AU - Melanie Weber
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03850
UR - https://arxiv.org/abs/2610.03850
AB - We present SONDE (SOrted Neighbor Distance Embedding): a scalable manifold learning approach to create a nuanced, continuous, low-dimensional representation of the geometry of galaxies' local environments. SONDE is a variation on Isomap that uses similarity distances between galaxy neighborhoods to create an embedding space, within which galaxies cluster according to their neighborhood geometries. We additionally present a novel method to project different datasets into the same embedding space, enabling comparisons of environmental embeddings across redshifts. We demonstrate this method on the TNG100 simulations at z = 0 − 5 and show that the embedding values evolve with redshift and correlate with galaxies' physical properties. We train a neural network to predict physical properties based on environmental features and show that our first embedding dimension outperforms a standard environmental quantifier, the number of neighbors, while the top three dimensions encode as much environmental information as a ten-bin radial profile. Our results are consistent with known density correlations, including galaxy halo mass, central vs. satellite status, and quenching. SONDE provides a straightforward way to encode subtleties of galaxy environments that could have significant implications for future analysis of galaxy-environment interactions.
ER -