Mean Estimate Distances for Galaxies with Multiple Estimates in NED-D

TL;DR

This paper introduces six methods for deriving a mean estimate distance from multiple galaxy distance measurements in NED-D, demonstrating their consistency and utility in cosmic distance scale research.

Contribution

It presents six novel methods for calculating mean estimate distances from multiple measurements, improving accuracy and consistency in cosmic distance scale applications.

Findings

All six MED methods produce consistent distances for key galaxies.

The methods effectively identify systematic trends in distance measurements.

NED-D MEDs enhance the reliability of cosmic distance scale data.

Abstract

Numerous research topics rely on an improved cosmic distance scale (e.g., cosmology, gravitational waves), and the NASA/IPAC Extragalactic Database of Distances (NED-D) supports those efforts by tabulating multiple redshift-independent distances for 12,000 galaxies (e.g., Large Magellanic Cloud (LMC) zero-point). Six methods for securing a mean estimate distance (MED) from the data are presented (e.g., indicator and Decision Tree). All six MEDs yield surprisingly consistent distances for the cases examined, including for the key benchmark LMC and M106 galaxies. The results underscore the utility of the NED-D MEDs in bolstering the cosmic distance scale and facilitating the identification of systematic trends.