The Tully-Fisher relation is a vital distance indicator, but its precise inference is challenged by selection bias, statistical bias, and uncertain inclination corrections. This study presents a Bayesian framework that simultaneously addresses these issues. To eliminate the need for individual inclination corrections, inclination is treated as a latent variable with a known probability distribution. To correct for the distance-dependent Malmqvist bias arising from sample selection, the model incorporates Gaussian scatter in the dependent variable, the distribution of the independent variable, and the observational selection function into the data likelihood. To mitigate the statistical bias -- termed the ``general Eddington bias'' -- caused by Gaussian scatter and the non-uniform distribution of the independent variable, two methods are introduced: (1) analytical bias corrections applied to the dependent variable before likelihood computation, and (2) a dual-scatter model that accounts for Gaussian scatter in the independent variable within the likelihood function. The effectiveness of these methods is demonstrated using simulated datasets. By rigorously addressing selection and statistical biases in a latent-variable regression analysis, this work provides a robust approach for unbiased distance estimates from standardizable candles, which is critical for improving the accuracy of Hubble constant determinations.
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This content will become publicly available on August 7, 2026
Python Code for Mitigating Malmquist and Eddington Biases in Latent-Inclination Regression of the Tully-Fisher Relation
This software repository provides the Python functions and a Jupyter notebook that implement the latent-variable bias-mitigating inference methods for the Tully-Fisher Relation. The methods are described in Fu (2025), titled "Mitigating Malmquist and Eddington Biases in Latent-Inclination Regression of the Tully-Fisher Relation". Repository DOI: https://doi.org/10.5281/zenodo.16378199
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- Award ID(s):
- 2103251
- PAR ID:
- 10630960
- Publisher / Repository:
- Zenodo AAS Journals
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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