Issue |
A&A
Volume 696, April 2025
|
|
---|---|---|
Article Number | A185 | |
Number of page(s) | 10 | |
Section | Extragalactic astronomy | |
DOI | https://doi.org/10.1051/0004-6361/202554164 | |
Published online | 17 April 2025 |
A new uncertainty scheme for galaxy distances from flow models
1
INAF – Arcetri Astrophysical Observatory, Largo Enrico Fermi 5, 50125 Firenze, Italy
2
Dipartimento di Fisica e Astronomia, Università degli Studi di Firenze, Via G. Sansone 1, 50019 Sesto Fiorentino, Firenze, Italy
3
Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106, USA
4
Department of Physics, University of Oregon, Eugene, OR 97403, USA
⋆ Corresponding author; konstantin.haubner@inaf.it
Received:
17
February
2025
Accepted:
11
March
2025
The systemic velocity or redshift of galaxies is a convenient tool to calculate their distances in the absence of primary methods, but the uncertainties on these flow distances may be substantial due to galaxy peculiar motions. Here, we derived a simple and easily applicable method to assign uncertainties to flow distances from four different methodologies, namely the Hubble law with both heliocentric and local-sheet velocities, the Cosmicflows-4 model, and the numerical action methods model. Our uncertainty scheme was constructed by comparing these flow distances to accurate, redshift-independent distances of a subsample of ∼2000 galaxies from the Cosmicflows-4 database, using the tip magnitude of the red giant branch, Cepheids, surface brightness fluctuations, supernovae type Ia, masers, and supernovae type II. We provide simple functions and tables to calculate the distance uncertainties for all the flow models considered. This uncertainty scheme is generally applicable except for the region around the Virgo cluster, where we assign increased uncertainties due to larger peculiar motions.
Key words: methods: miscellaneous / galaxies: distances and redshifts
© The Authors 2025
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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