Learning Galaxy Kinematics from Spatially Unresolved Spectra
Abstract
Galaxy kinematics encode information about the assembly histories of galaxies and the black holes at their centers, motivating the measurement of kinematic parameters for large galaxy populations. In particular, such parameters have been shown to help identify galaxies that host supermassive black hole binaries, a major outstanding question in multi-messenger astrophysics. Yet, inferring kinematic properties from spatially unresolved observations, where distant galaxies appear as point sources, remains a challenge. Spectroscopy can address this challenge because Doppler effects encode internal motions into spectral line profiles. Exploiting this encoding requires knowing how spatial structures map onto the line profiles but this cannot be derived from first principles for complex systems such as galaxies. We present a neural network trained on spatially resolved spectra that infers kinematic properties from spatially integrated spectra, offering two advantages over classical approaches: it learns the kinematic encoding directly from data without requiring spatial resolution and remains effective even when spectral resolution only marginally resolves the lines. This enables recovering the kinematic properties for millions of unresolved galaxies in existing and future surveys.
Related
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- Anisotropy and tracer-profile assumptions in aperture-based strong-lens dynamics: Conditional inference from supernova distance ratios and unresolved stellar kinematics
- TDCOSMO XXIX: JWST/NIRSpec IFU Spatially Resolved Kinematics of Three Time-delay Lenses
- TDCOSMO XXX: Spatially resolved kinematics of the deflectors in time-delay lens systems B1608+656 and SDSSJ1206+4332 from JWST-NIRSpec observation
- Decomposing the Kinematics of SNR G180.0-01.7 with Medium-Resolution Spectra
BibTeX
@misc{waldrop2026learning,
title = {{Learning Galaxy Kinematics from Spatially Unresolved Spectra}},
author = {Preston G. Waldrop and Dimitrios Psaltis and Feryal Özel and Tolga Güver},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40789},
eprint = {2610.40789},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40789},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Learning Galaxy Kinematics from Spatially Unresolved Spectra
AU - Preston G. Waldrop
AU - Dimitrios Psaltis
AU - Feryal Özel
AU - Tolga Güver
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40789
UR - https://arxiv.org/abs/2610.40789
AB - Galaxy kinematics encode information about the assembly histories of galaxies and the black holes at their centers, motivating the measurement of kinematic parameters for large galaxy populations. In particular, such parameters have been shown to help identify galaxies that host supermassive black hole binaries, a major outstanding question in multi-messenger astrophysics. Yet, inferring kinematic properties from spatially unresolved observations, where distant galaxies appear as point sources, remains a challenge. Spectroscopy can address this challenge because Doppler effects encode internal motions into spectral line profiles. Exploiting this encoding requires knowing how spatial structures map onto the line profiles but this cannot be derived from first principles for complex systems such as galaxies. We present a neural network trained on spatially resolved spectra that infers kinematic properties from spatially integrated spectra, offering two advantages over classical approaches: it learns the kinematic encoding directly from data without requiring spatial resolution and remains effective even when spectral resolution only marginally resolves the lines. This enables recovering the kinematic properties for millions of unresolved galaxies in existing and future surveys.
ER -