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arXiv · astro-ph/0104163

The CFHT Open Star Cluster Survey I -- Cluster Selection and Data Reduction

Abstract

We present this paper in conjuction with the following as the first results in the CFHT Open Star Cluster Survey. This survey is a large BVR imaging data set of 19 open star clusters in our Galaxy. This data set was taken with the CFH12K mosaic CCD (42' X 28') and the majority of the clusters were imaged under excellent photometric, sub-arcsecond seeing conditions. The combination of multiple exposures extending to deep (V ~ 25) magnitudes with short (< 10 second) frames allows for many different studies. The primary aim of this survey is to catalogue the white dwarf stars in these clusters and establish observational constraints on the initial-final mass relationship for these stars and the upper mass limit to white dwarf production. Additionally, we hope to better determine the properties of the clusters, such as age and distance, and also test evolution and dynamical theories by analyzing luminosity and mass functions. In order to more easily incorporate this data in further studies, we have produced a catalogue of positions, magnitudes, colors, and stellarity confidence for all stars in each cluster of the survey. This paper is intended both as a source for the astronomical community to obtain information on the clusters in the survey and as a detailed reference of reduction procedures for further publications of individual clusters. We discuss the methods employed to reduce the data and compute the photometric catalogue. The scientific results for each individual cluster and global results from the study of the entire survey will be presented in further publications (results for the rich old open cluster NGC 6819 immediately follow this paper).

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BibTeXRIS

Jasonjot Singh Kalirai, Harvey B. Richer, Gregory G. Fahlman, Jean-Charles Cuillandre, Paolo Ventura, Francesca D'Antona, Emmanuel Bertin, Gianni Marconi, Patrick R. Durrell. 2001-07-19. The CFHT Open Star Cluster Survey I -- Cluster Selection and Data Reduction. https://doi.org/10.1086/321140

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