Minimization of Biases in Galaxy Peculiar Velocity Catalogs
Jenny G. Sorce

TL;DR
This paper introduces an iterative correction method for galaxy peculiar velocity catalogs to reduce biases like Malmquist effects, improving the accuracy of local universe velocity field reconstructions.
Contribution
The paper presents a novel iterative bias correction technique tested on realistic mock catalogs, effectively minimizing non-Gaussianities and spurious infall in galaxy velocity data.
Findings
Bias correction suppresses spurious infall in velocity fields.
Method achieves non-biased velocity reconstructions within 100-150 km/s.
Results are consistent across different mock datasets.
Abstract
Galaxy distances and derived radial peculiar velocity catalogs constitute valuable datasets to study the dynamics of the Local Universe. However, such catalogs suffer from biases whose effects increase with the distance. Malmquist biases and lognormal error distribution affect the catalogs. Velocity fields of the Local Universe reconstructed with these catalogs present a spurious overall infall onto the Local Volume if they are not corrected for biases. Such an infall is observed in the reconstructed velocity field obtained when applying the BayesianWiener-Filter technique to the raw second radial peculiar velocity catalog of the Cosmicflows project. In this paper, an iterative method to reduce spurious non-Gaussianities in the radial peculiar velocity distribution, to retroactively derive overall better distance estimates resulting in a minimization of the effects of biases, is…
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