Statistically bias-minimized peculiar velocity catalogs from Gibbs point processes and Bayesian inference
Jenny G. Sorce, Radu S. Stoica, Elmo Tempel

TL;DR
This paper introduces a novel algorithm using Gibbs point processes and Bayesian inference to statistically reduce biases in galaxy peculiar velocity catalogs, improving the accuracy of cosmological measurements.
Contribution
The paper presents a new bias-minimization algorithm for peculiar velocity catalogs based on an object point process model and simulated annealing, enhancing the reliability of cosmological analyses.
Findings
Reduces local peculiar velocity variance by an order of magnitude.
Recovers expected velocity and clustering properties in synthetic catalogs.
Successfully reconstructs the local cosmic web from observational data.
Abstract
Galaxy peculiar velocities are excellent cosmological probes provided that biases inherent to their measurements are contained before any study. This paper proposes a new algorithm based on an object point process model whose probability density is built to statistically reduce the effects of Malmquist biases and uncertainties due to lognormal errors in radial peculiar velocity catalogs. More precisely, a simulated annealing algorithm permits maximizing the probability density describing the point process model. The resulting configurations are bias-minimized catalogs. Tests are conducted on synthetic catalogs mimicking the second and third distance modulus catalogs of the Cosmicflows project from which peculiar velocity catalogs are derived. By reducing the local peculiar velocity variance in catalogs by an order of magnitude, the algorithm permits recovering the expected one while…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Advanced Statistical Methods and Models · Stellar, planetary, and galactic studies
