The PAndAS View of the Andromeda Satellite System. I. A Bayesian Search for Dwarf Galaxies Using Spatial and Color-Magnitude Information

Nicolas F. Martin, Rodrigo A. Ibata, Alan W. McConnachie, A. Dougal Mackey, Annette M. N. Ferguson, Michael J. Irwin, Geraint F. Lewis, Mark A. Fardal

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We present a generic algorithm to search for dwarf galaxies in photometric catalogs and apply it to the Pan-Andromeda Archaeological Survey (PAndAS). The algorithm is developed in a Bayesian framework and, contrary to most dwarf galaxy search codes, makes use of both the spatial and color-magnitude information of sources in a probabilistic approach. Accounting for the significant contamination from the Milky Way foreground and from the structured stellar halo of the Andromeda galaxy, we recover all known dwarf galaxies in the PAndAS footprint with high significance, even for the least luminous ones. Some Andromeda globular clusters are also recovered and, in one case, discovered. We publish a list of the 143 most significant detections yielded by the algorithm. The combined properties of the 39 most significant isolated detections show hints that at least some of these trace genuine dwarf galaxies, too faint to be individually detected. Follow-up observations by the community are mandatory to establish which are real members of the Andromeda satellite system. The search technique presented here will be used in an upcoming contribution to determine the PAndAS completeness limits for dwarf galaxies. Although here tuned to the search of dwarf galaxies in the PAndAS data, the algorithm can easily be adapted to the search for any localized overdensity whose properties can be modeled reliably in the parameter space of any catalog.
Original languageEnglish
Pages (from-to)80
JournalAstrophysical Journal
Volume776
Publication statusPublished - 1 Oct 2013

Keywords / Materials (for Non-textual outputs)

  • galaxies: dwarf
  • Local Group
  • methods: data analysis
  • methods: statistical

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