Papers › Galactic Globular and Open Clusters in the Sloan Digital Sky Survey. I. Crowded Field...

Galactic Globular and Open Clusters in the Sloan Digital Sky Survey. I. Crowded Field Photometry and Cluster Fiducial Sequences in ugriz

31 Jul 2008arXiv:0808.0001links table onlyarchive 2025-07-28

Deokkeun An, Jennifer A. Johnson, James L. Clem, Brian Yanny, Constance M. Rockosi, Heather L. Morrison, Paul Harding, James E. Gunn, Carlos Allende Prieto, Timothy C. Beers, Kyle M. Cudworth, Inese I. Ivans, Zeljko Ivezic, Young Sun Lee, Robert H. Lupton, Dmitry Bizyaev, Howard Brewington, Elena Malanushenko, Viktor Malanushenko, Dan Oravetz, Kaike Pan, Audrey Simmons, Stephanie Snedden, Shannon Watters, Donald G. York

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We present photometry for globular and open cluster stars observed with the Sloan Digital Sky Survey (SDSS). In order to exploit over 100 million stellar objects with r < 22.5 mag observed by SDSS, we need to understand the characteristics of stars in the SDSS ugriz filters. While star clusters provide important calibration samples for stellar colors, the regions close to globular clusters, where the fraction of field stars is smallest, are too crowded for the standard SDSS photometric pipeline to process. To complement the SDSS imaging survey, we reduce the SDSS imaging data for crowded cluster fields using the DAOPHOT/ALLFRAME suite of programs and present photometry for 17 globular clusters and 3 open clusters in a SDSS value-added catalog. Our photometry and cluster fiducial sequences are on the native SDSS 2.5-meter ugriz photometric system, and the fiducial sequences can be directly applied to the SDSS photometry without relying upon any transformations. Model photometry for red giant branch and main-sequence stars obtained by Girardi et al. cannot be matched simultaneously to fiducial sequences; their colors differ by ~0.02-0.05 mag. Good agreement (< ~0.02 mag in colors) is found with Clem et al. empirical fiducial sequences in u'g'r'i'z' when using the transformation equations in Tucker et al.

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Runjing-Liu120/DeblendingStarfields mentioned on GitHubpytorch report
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