Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-09T22:25:15.785Z Has data issue: false hasContentIssue false

16 - Statistical astrometry

from Part IV - From detected photons to the celestial sphere

Published online by Cambridge University Press:  05 December 2012

By
Anthony G. A. Brown
Edited by
William F. van Altena
Anthony G. A. Brown
Affiliation:
Leiden University
William F. van Altena
Affiliation:
Yale University, Connecticut
Get access

Summary

Introduction

The term “statistical astrometry” refers to the inference of astrophysical quantities from samples (i.e. collections) of stars or other sources. Often the mean value of the astrophysical quantity or a description of its distribution is sought. The applications are numerous and include luminosity calibration, star-cluster membership determination, separating the Galaxy's structural components, detecting low-contrast substructure in the Galactic halo, etc. These studies become especially interesting now that astrometric catalogs are available with large numbers of parallaxes. However, there are a number of effects that can lead to errors in the inference of astrophysical quantities and thus complicate the interpretation of astrometric data. This chapter is focused on discussing these complicating effects with the aim of providing guidance on the optimal use of astrometric data in statistical studies.

Effects complicating the interpretation of astrometric data

The main effects that can complicate the interpretation of astrometric data for samples of objects are summarized below.

Completeness and selection effects

Any sample chosen from an astrometric catalog will suffer from incompleteness in the data and selection effects. Both issues are related to the properties of the astrometric survey and how the sample is chosen. In addition, they may depend on the location on the sky or the source properties. Hence, the chosen sample almost never represents the true distribution of sources in the astrophysical parameter space. Ignoring this fact leads to biased results in the scientific interpretation of the data.

Type
Chapter
Information
Astrometry for Astrophysics
Methods, Models, and Applications
 , pp. 242 - 256
Publisher: Cambridge University Press
Print publication year: 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arenou, F. and Luri, X. (1999). Distances and absolute magnitudes from trigonometric parallaxes. ASP Conf. Ser., 167, p. 13.Google Scholar
Binney, J. (2011). Extracting science from surveys of our Galaxy. Pramana, 77, 39–52.CrossRefGoogle Scholar
Binney, J. and Merrifield, M (1998). Galactic Astronomy. Princeton, NJ: Princeton University Press.Google Scholar
Brown, A. G. A., Arenou, F., van Leeuwen, F., Lindegren, L., and Luri, X. (1997). Some considerations in making full use of the Hipparcos Catalogue. In Hipparcos Venice '97. ESA SP-402, p. 63.Google Scholar
de Bruijne, J. H. J. (1999). Structure and colour–magnitude diagrams of Scorpius OB2 based on kinematic modelling of Hipparcos data. MNRAS, 310, 585.CrossRefGoogle Scholar
de Bruijne, J. H. J., Hoogerwerf, R., and de Zeeuw, P. T. (2001). A Hipparcos study of the Hyades open cluster. Improved colour–absolute magnitude and Hertzsprung–Russell diagrams. A&A, 367, 111.Google Scholar
Butkevich, A. G., Berdyugin, A. V., and Teerikorpi, P. (2005). Statistical biases in stellar astronomy: the Malmquist bias revisited. MNRAS, 362, 321.Google Scholar
Dravins, D., Lindegren, L., and Madsen, S. (1999). Astrometric radial velocities. I. Nonspectroscopic methods for measuring stellar radial velocity. A&A, 348, 1040.Google Scholar
,ESA (1989). The Hipparcos Mission: Pre-launch Status. ESA SP-1111.Google Scholar
,ESA (1997). The Hipparcos and Tycho Catalogues. ESA SP-1200.Google Scholar
Feast, M.W. and Catchpole, R. M. (1997). The Cepheid period-luminosity zero-point from Hipparcos trigonometrical parallaxes. MNRAS, 286, L1–L5.CrossRefGoogle Scholar
Gómez, A. E., Luri, X., Mennessier, M.O., Torra, J., and Figueras, F. (1997). The luminosity calibration of the HR Diagram revisited by HIPPARCOS. In Hipparcos Venice '97. ESA SP-402, p. 207.Google Scholar
Gould, A. (2004). v⊥ CMD. astro-ph/0403506.
Hanson, R. B. (1979). A practical method to improve luminosity calibrations from trigonometric parallaxes. MNRAS, 186, 875.CrossRefGoogle Scholar
Lindegren, L., Madsen, S., and Dravins, D. (2000). Astrometric radial velocities. II. Maximum-likelihood estimation of radial velocities in moving clusters. A&A, 356, 1119.Google Scholar
Luri, X., Mennessier, M. O., Torra, J., and Figueras, F. (1996). A new maximum likelihood method for luminosity calibrations. A&AS, 117, 405.Google Scholar
Lutz, T. E. and Kelker, D. H. (1973). On the use of trigonometric parallaxes for the calibration of luminosity systems: theory. PASP, 85, 573.CrossRefGoogle Scholar
Madsen, S., Dravins, D., and Lindegren, L. (2002). Astrometric radial velocities. III. Hipparcos measurements of nearby star clusters and associations. A&A, 381, 446.Google Scholar
Malmquist, K. G. (1922). Lund Medd. Ser.I, 100, 1.
Perryman, M. A. C., Brown, A. G. A., Lebreton, Y., et al. (1998). The Hyades: distance, structure, dynamics, and age. A&A, 331, 81.Google Scholar
Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery, B. P. (2007). Numerical Recipes: The Art of Scientific Computing, 3rd edn. Cambridge: Cambridge University Press.Google Scholar
Ratnatunga, K. U. and Casertano, S. (1991). Absolute magnitude calibration using trigonometric parallax: incomplete, spectroscopic samples. AJ, 101, 1075.CrossRefGoogle Scholar
Smith, H. Jr., (2003). Is there really a Lutz–Kelker bias? Reconsidering calibration with trigonometric parallaxes. MNRAS, 338, 891.CrossRefGoogle Scholar
Smith, H. Jr., and Eichhorn, H. (1996). On the estimation of distances from trigonometric parallaxes. MNRAS, 281, 211.CrossRefGoogle Scholar
Turon, C. and Crézé, M. (1977). On the statistical use of trigonometric parallaxes. A&A, 56, 273.Google Scholar
Turon, C. et al. (1992). The HIPPARCOS Input Catalogue. I – Star Selection. A&A, 258, 74.Google Scholar
van Leeuwen, F. (2007). Hipparcos, the New Reduction of the Raw Data. Dordrecht: Springer.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×