The Velocity Field Olympics: Assessing velocity field reconstructions with direct distance tracers Article Swipe
Richard Stiskalek
,
Harry Desmond
,
Julien Devriendt
,
Adrianne Slyz
,
Guilhem Lavaux
,
Michael J. Hudson
,
Deaglan J. Bartlett
,
H. M. Courtois
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.00121
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.00121
The peculiar velocity field of the local Universe provides direct insights into its matter distribution and the underlying theory of gravity, and is essential in cosmological analyses for modelling deviations from the Hubble flow. Numerous methods have been developed to reconstruct the density and velocity fields at $z \lesssim 0.05$, typically constrained by redshift-space galaxy positions or by direct distance tracers such as the Tully-Fisher relation, the fundamental plane, or Type Ia supernovae. We introduce a validation framework to evaluate the accuracy of these reconstructions against catalogues of direct distance tracers. Our framework assesses the goodness-of-fit of each reconstruction using Bayesian evidence, residual redshift discrepancies, velocity scaling, and the need for external bulk flows. Applying this framework to a suite of reconstructions -- including those derived from the Bayesian Origin Reconstruction from Galaxies (BORG) algorithm and from linear theory -- we find that the non-linear BORG reconstruction consistently outperforms others. We highlight the utility of such a comparative approach for supernova or gravitational wave cosmological studies, where selecting an optimal peculiar velocity model is essential. Additionally, we present calibrated bulk flow curves predicted by the reconstructions and perform a density--velocity cross-correlation using a linear theory reconstruction to constrain the growth factor, yielding $S_8 = 0.793 \pm 0.035$. The result is in good agreement with both weak lensing and Planck, but is in strong disagreement with some peculiar velocity studies.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.00121
- https://arxiv.org/pdf/2502.00121
- OA Status
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- Cited By
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All OpenAlex metadata
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https://openalex.org/W4407123207Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.00121Digital Object Identifier
- Title
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The Velocity Field Olympics: Assessing velocity field reconstructions with direct distance tracersWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-31Full publication date if available
- Authors
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Richard Stiskalek, Harry Desmond, Julien Devriendt, Adrianne Slyz, Guilhem Lavaux, Michael J. Hudson, Deaglan J. Bartlett, H. M. CourtoisList of authors in order
- Landing page
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https://arxiv.org/abs/2502.00121Publisher landing page
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https://arxiv.org/pdf/2502.00121Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2502.00121Direct OA link when available
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Physics, Peculiar velocity, Redshift, Astrophysics, GalaxyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Planck, | 218 |
| abstract_inverted_index.against | 85 |
| abstract_inverted_index.density | 42 |
| abstract_inverted_index.derived | 125 |
| abstract_inverted_index.factor, | 200 |
| abstract_inverted_index.lensing | 216 |
| abstract_inverted_index.methods | 35 |
| abstract_inverted_index.optimal | 169 |
| abstract_inverted_index.others. | 149 |
| abstract_inverted_index.perform | 187 |
| abstract_inverted_index.present | 177 |
| abstract_inverted_index.tracers | 60 |
| abstract_inverted_index.utility | 153 |
| abstract_inverted_index.Applying | 114 |
| abstract_inverted_index.Bayesian | 100, 128 |
| abstract_inverted_index.Galaxies | 132 |
| abstract_inverted_index.Numerous | 34 |
| abstract_inverted_index.Universe | 7 |
| abstract_inverted_index.\lesssim | 48 |
| abstract_inverted_index.accuracy | 81 |
| abstract_inverted_index.analyses | 26 |
| abstract_inverted_index.approach | 158 |
| abstract_inverted_index.assesses | 93 |
| abstract_inverted_index.distance | 59, 89 |
| abstract_inverted_index.evaluate | 79 |
| abstract_inverted_index.external | 111 |
| abstract_inverted_index.gravity, | 20 |
| abstract_inverted_index.insights | 10 |
| abstract_inverted_index.peculiar | 1, 170, 226 |
| abstract_inverted_index.provides | 8 |
| abstract_inverted_index.redshift | 103 |
| abstract_inverted_index.residual | 102 |
| abstract_inverted_index.scaling, | 106 |
| abstract_inverted_index.studies, | 165 |
| abstract_inverted_index.studies. | 228 |
| abstract_inverted_index.tracers. | 90 |
| abstract_inverted_index.velocity | 2, 44, 105, 171, 227 |
| abstract_inverted_index.yielding | 201 |
| abstract_inverted_index.agreement | 212 |
| abstract_inverted_index.algorithm | 134 |
| abstract_inverted_index.constrain | 197 |
| abstract_inverted_index.developed | 38 |
| abstract_inverted_index.essential | 23 |
| abstract_inverted_index.evidence, | 101 |
| abstract_inverted_index.framework | 77, 92, 116 |
| abstract_inverted_index.highlight | 151 |
| abstract_inverted_index.including | 123 |
| abstract_inverted_index.introduce | 74 |
| abstract_inverted_index.modelling | 28 |
| abstract_inverted_index.positions | 55 |
| abstract_inverted_index.predicted | 182 |
| abstract_inverted_index.relation, | 65 |
| abstract_inverted_index.selecting | 167 |
| abstract_inverted_index.supernova | 160 |
| abstract_inverted_index.typically | 50 |
| abstract_inverted_index.calibrated | 178 |
| abstract_inverted_index.catalogues | 86 |
| abstract_inverted_index.deviations | 29 |
| abstract_inverted_index.essential. | 174 |
| abstract_inverted_index.non-linear | 144 |
| abstract_inverted_index.underlying | 17 |
| abstract_inverted_index.validation | 76 |
| abstract_inverted_index.comparative | 157 |
| abstract_inverted_index.constrained | 51 |
| abstract_inverted_index.fundamental | 67 |
| abstract_inverted_index.outperforms | 148 |
| abstract_inverted_index.reconstruct | 40 |
| abstract_inverted_index.supernovae. | 72 |
| abstract_inverted_index.Tully-Fisher | 64 |
| abstract_inverted_index.consistently | 147 |
| abstract_inverted_index.cosmological | 25, 164 |
| abstract_inverted_index.disagreement | 223 |
| abstract_inverted_index.distribution | 14 |
| abstract_inverted_index.Additionally, | 175 |
| abstract_inverted_index.gravitational | 162 |
| abstract_inverted_index.Reconstruction | 130 |
| abstract_inverted_index.discrepancies, | 104 |
| abstract_inverted_index.reconstruction | 98, 146, 195 |
| abstract_inverted_index.redshift-space | 53 |
| abstract_inverted_index.goodness-of-fit | 95 |
| abstract_inverted_index.reconstructions | 84, 121, 185 |
| abstract_inverted_index.cross-correlation | 190 |
| abstract_inverted_index.density--velocity | 189 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |