Discovering and Characterizing Open Clusters with the GLIMPSE-360 Survey Karen Hamm University of Virginia, Charlottesville, VA, USA. Introduction. Cluster Classifications. Results.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Discovering and Characterizing Open Clusters with the GLIMPSE-360 Survey
University of Virginia, Charlottesville, VA, USA
We have identified a number of undiscovered clusters and have derived parameters for previously catalogued clusters with little data. These findings display the fact that there is still much to discover in the outer regions of our galaxy. With future progress in technology, it will be possible to collect data for even more distant stellar objects.
The stars within a given cluster are formed from the same giant molecular cloud in the same star-forming episode, and thus all have the same age and chemical composition. Because of this, the effects of mass and age are more easily determined for stars within an open cluster than for solitary stars. By understanding the evolution of these stars and obtaining a more complete census of open clusters, astronomers can better understand the Milky Way’s evolution and structure, and its historical and present rate of star formation.
The Spitzer Space Telescope and the Galactic Infrared Mid-Plane Survey Extraordinaire surveys have opened a number of new windows into the study of star formation and the structure of the Milky Way. GLIMPSE-360 is the newest chapter in the galactic surveys, spanning the outer regions of the Galactic disk. Combining the low-extinction of the mid-IR and the high angular resolution and sensitivity of Spitzer, GLIMPSE-360 has produced new image mosaics that have been used to identify a number of new and previously discovered open clusters.
Once all of the images were examined, we identified a number of published cluster identifications using the WEBDA database and the updated catalog of Dias et al. (2002). We then searched through the literature for any parameters derived for the clusters.
For each cluster, we generated color magnitude diagrams using 2MASS near-IR data of the identified center and a comparison equal-area annulus around the cluster. The potential and known clusters were sorted based on the image from Spitzer and the strength of the CMDs.
The individual mid-IR mosaics were examined using the “SAOImage ds9” program. The images were scaled to approximately 1°×1° bins of the original sizes. Panning through the remainder of the image, potential open clusters were selected based on an overdensity of stars with similar size and brightness. The regions were then encircled, providing a rough estimate of the center coordinate and size of each of the clusters.
Figure 2, 3, 4, … : Mid-IR images
Isochrones were created for the 2MASS CMDs of both the known clusters and unknown clusters with convincing CMDs.
Figure 1: SAOImage ds9 program
Figure … : Isochrones of catalogued clusters with few parameters and unknown clusters with convincing CMDs.