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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Astrophysics and Astronomy

2001

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Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

The Peculiar Motions Of Early-Type Galaxies In Two Distant Regions - Vi. The Maximum-Likelihood Gaussian Algorithm, M. Colless, R. P. Saglia, D. Burstein, R. L. Davies, R. K. Mcmahan, G. Wegner Jun 2001

The Peculiar Motions Of Early-Type Galaxies In Two Distant Regions - Vi. The Maximum-Likelihood Gaussian Algorithm, M. Colless, R. P. Saglia, D. Burstein, R. L. Davies, R. K. Mcmahan, G. Wegner

Dartmouth Scholarship

The EFAR project is designed to measure the properties and peculiar motions of early-type galaxies in two distant regions. Here we describe the maximum-likelihood algorithm we developed to investigate the correlations between the parameters of the EFAR data base. One-, two- and three-dimensional Gaussian models are constructed to determine the mean value and intrinsic spread of the parameters, and the slopes and intrinsic parallel and orthogonal spread of the Mg2–Mgb′,Mg2–σ,Mgb′–σ relations, and the Fundamental Plane. In the latter case, the cluster peculiar velocities are also determined. We show that this method is superior to …


The Peculiar Motions Of Early-Type Galaxies In Two Distant Regions -- Vii. Peculiar Velocities And Bulk Motions, Matthew Colless, R. P. Saglia, David Burstein, Roger L. Davies, Robert K. Mcmahan Jr, Gary Wegner May 2001

The Peculiar Motions Of Early-Type Galaxies In Two Distant Regions -- Vii. Peculiar Velocities And Bulk Motions, Matthew Colless, R. P. Saglia, David Burstein, Roger L. Davies, Robert K. Mcmahan Jr, Gary Wegner

Dartmouth Scholarship

We present peculiar velocities for 85 clusters of galaxies in two large volumes at distances between 6000 and 15 000 km s−1 in the directions of Hercules-Corona Borealis and Perseus-Pisces-Cetus (the EFAR sample). These velocities are based on Fundamental Plane (FP) distance estimates for early-type galaxies in each cluster. We fit the FP using a maximum likelihood algorithm which accounts for both selection effects and measurement errors, and yields FP parameters with smaller bias and variance than other fitting procedures. We obtain a best-fitting FP with coefficients consistent with the best existing determinations. We measure the bulk motions of …