Power Spectrum Estimation from Peculiar Velocity Catalogues
The peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field...
| Main Authors: | , , , , , , |
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| Format: | Journal article |
| Published: |
2011
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| _version_ | 1826289133539033088 |
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| author | Macaulay, E Feldman, H Ferreira, P Jaffe, A Agarwal, S Hudson, M Watkins, R |
| author_facet | Macaulay, E Feldman, H Ferreira, P Jaffe, A Agarwal, S Hudson, M Watkins, R |
| author_sort | Macaulay, E |
| collection | OXFORD |
| description | The peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field, which suggests a large scale excess in the matter power spectrum, and can appear to be in some tension with the LCDM model. We use a composite catalogue of 4,537 peculiar velocity measurements with a characteristic depth of 33 h-1 Mpc to estimate the matter power spectrum. We compare the constraints with this method, directly studying the full peculiar velocity catalogue, to results from Macaulay et al. (2011), studying minimum variance moments of the velocity field, as calculated by Watkins, Feldman and Hudson (2009) and Feldman, Watkins and Hudson (2010). We find good agreement with the LCDM model on scales of k > 0.01 h Mpc-1. We find an excess of power on scales of k < 0.01 h Mpc-1, although with a 1 sigma uncertainty which includes the LCDM model. We find that the uncertainty in the excess at these scales is larger than an alternative result studying only moments of the velocity field, which is due to the minimum variance weights used to calculate the moments. At small scales, we are able to clearly discriminate between linear and nonlinear clustering in simulated peculiar velocity catalogues, and find some evidence (although less clear) for linear clustering in the real peculiar velocity data. |
| first_indexed | 2024-03-07T02:24:15Z |
| format | Journal article |
| id | oxford-uuid:a50c133d-76c7-4e87-9eb7-eb662f88a172 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:24:15Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | oxford-uuid:a50c133d-76c7-4e87-9eb7-eb662f88a1722022-03-27T02:37:44ZPower Spectrum Estimation from Peculiar Velocity CataloguesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a50c133d-76c7-4e87-9eb7-eb662f88a172Symplectic Elements at Oxford2011Macaulay, EFeldman, HFerreira, PJaffe, AAgarwal, SHudson, MWatkins, RThe peculiar velocities of galaxies are an inherently valuable cosmological probe, providing an unbiased estimate of the distribution of matter on scales much larger than the depth of the survey. Much research interest has been motivated by the high dipole moment of our local peculiar velocity field, which suggests a large scale excess in the matter power spectrum, and can appear to be in some tension with the LCDM model. We use a composite catalogue of 4,537 peculiar velocity measurements with a characteristic depth of 33 h-1 Mpc to estimate the matter power spectrum. We compare the constraints with this method, directly studying the full peculiar velocity catalogue, to results from Macaulay et al. (2011), studying minimum variance moments of the velocity field, as calculated by Watkins, Feldman and Hudson (2009) and Feldman, Watkins and Hudson (2010). We find good agreement with the LCDM model on scales of k > 0.01 h Mpc-1. We find an excess of power on scales of k < 0.01 h Mpc-1, although with a 1 sigma uncertainty which includes the LCDM model. We find that the uncertainty in the excess at these scales is larger than an alternative result studying only moments of the velocity field, which is due to the minimum variance weights used to calculate the moments. At small scales, we are able to clearly discriminate between linear and nonlinear clustering in simulated peculiar velocity catalogues, and find some evidence (although less clear) for linear clustering in the real peculiar velocity data. |
| spellingShingle | Macaulay, E Feldman, H Ferreira, P Jaffe, A Agarwal, S Hudson, M Watkins, R Power Spectrum Estimation from Peculiar Velocity Catalogues |
| title | Power Spectrum Estimation from Peculiar Velocity Catalogues |
| title_full | Power Spectrum Estimation from Peculiar Velocity Catalogues |
| title_fullStr | Power Spectrum Estimation from Peculiar Velocity Catalogues |
| title_full_unstemmed | Power Spectrum Estimation from Peculiar Velocity Catalogues |
| title_short | Power Spectrum Estimation from Peculiar Velocity Catalogues |
| title_sort | power spectrum estimation from peculiar velocity catalogues |
| work_keys_str_mv | AT macaulaye powerspectrumestimationfrompeculiarvelocitycatalogues AT feldmanh powerspectrumestimationfrompeculiarvelocitycatalogues AT ferreirap powerspectrumestimationfrompeculiarvelocitycatalogues AT jaffea powerspectrumestimationfrompeculiarvelocitycatalogues AT agarwals powerspectrumestimationfrompeculiarvelocitycatalogues AT hudsonm powerspectrumestimationfrompeculiarvelocitycatalogues AT watkinsr powerspectrumestimationfrompeculiarvelocitycatalogues |