Redshift-independent distance estimates were used by Edwin Hubble to establish the extragalactic distance scale and the rate of universal expansion (Hubble 1926, 1929). Today, such estimates (hereafter simply distances) are available for more than 147,000 galaxies, as tabulated in the NASA/IPAC Extragalactic Database (NED), and updates are released on a regular basis. Most galaxies with distances have only a single estimate available. Around 11,000 of the nearest well studied galaxies however, have multiple estimates and in some cases dozens. Further, published estimates are not all based on the same extragalactic distance scale. Around 20% of the estimates published assume either a different Hubble constant or a different distance scale zero point than the canonical values. Currently, the NED user interface presents simple summary statistics along with the individual, raw, un-scaled estimates. Clearly many science applications will benefit greatly from a single, scale-adjusted mean distance for galaxies with multiple estimates. Here, we present preliminary results of testing six different methods to derive mean estimate distances. Those include the most common practice followed, which involves best estimate distances derived by selecting individual estimates per indicator with the smallest uncertainties. The intent is to generate these derived mean estimates by an algorithm in the database as new distances are entered. This research and NED are funded by and operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
American Astronomical Society Meeting Abstracts #233
- Pub Date:
- January 2019