An Instructive Challenge

This question came to the CfA Public Affairs office, and I am sharing it with y’all because I think the solution is instructive.

A student had to figure out the name of a stellar object as part of an assignment. He was given the following information about it:

  • apparent [V] magnitude = 5.76
  • B-V = 0.02
  • E(B-V) = 0.00
  • parallax = 0.0478 arcsec
  • radial velocity = -18 km/s
  • redshift = 0 km/s

He looked in all the stellar databases but was unable to locate it, so he asked the CfA for help.

Just to help you out, here are a couple of places where you can find comprehensive online catalogs:

See if you can find it!

Answer next week month.

Update (2010-aug-02):
The short answer is, I could find no such star in any commonly available catalog. But that is not the end of the story. There does exist a star in the Hipparcos catalog, HIP 103389, that has approximately the right distance (21 pc), radial velocity (-16.1 km/s), and V magnitude (5.70). It doesn’t match exactly, and the B-V is completely off, but that is the moral of the story.

The thing is, catalogs are not perfect. The same objects often have very different numerical entries in different catalogs. This could be due to a variety of reasons, such as different calibrations, different analysers, or even intrinsic variations in the source. And you can bet your bottom dollar that the quoted statistical uncertainties in the quantities do not account for the observed variance. Take the B-V value, for instance. It is 0.5 for HIP 103389, but the initial problem stated that it was 0.02, which makes it an A type star. But if it were an A type star at 21 pc, it should have had a magnitude of V~1.5, much brighter than the required 5.76!

I think this illustrates one of the fundamental tenets of science as it is practiced, versus how it is taught. The first thing that a practicing scientist does (especially one not of the theoretical persuasion) is to try and see where the data might be wrong or misleading. It should only be included in analysis after it passes various consistency checks and is deemed valid. The moral of the story is, don’t trust data blindly just because it is a “number”.

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