Mathematics – Probability
Scientific paper
Jan 2012
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012aas...21915109o&link_type=abstract
American Astronomical Society, AAS Meeting #219, #151.09
Mathematics
Probability
Scientific paper
We describe a method for automatic detection of star clusters in M31, using the multi-band images taken with the Hubble Space Telescope (HST) as part of the Panchromatic Hubble Andromeda Treasury (PHAT). Our method relies on the fact that at the distance of M31, images of star clusters observed with HST contain both a resolved stellar population and a significant amount of unresolved light. First, we use the point source photometry generated as part of the PHAT pipeline to subtract point sources from the images, leaving behind only the unresolved light, in addition to image artifacts. Next, we use the filtered, subtracted images to identify peaks in unresolved light. We compare these peaks to the point source photometric catalog to remove some of the contaminants and to identify likely candidate clusters. Finally, we employ neural network techniques to remove additional contaminants. Tests of the method show that we can automatically identify 85-90% of star clusters identified through by-eye searches, as well as find additional possible star clusters not found by eye. Implementation of this method will allow for the quantitative measurement of star cluster detection probability and completeness in the PHAT data, something which will be otherwise difficult to accomplish with by-eye searches.
Dalcanton Julianne J.
Fouesneau Morgan
Johnson Laura L.
Olsen Knut A.
Seth Anil C.
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