The Two Micron All Sky Survey at IPAC

2MASS Science

• Low-Mass Stars and Brown Dwarfs in 2MASS

  Initial results on the definition of a new spectral type, the ``L''-type dwarfs, the discovery of the methane ``T''-type dwarfs, along with other work on late-M field dwarfs and the mass function of field brown dwarfs, by J. Davy Kirkpatrick (IPAC) and collaborators.

• The Search for Red AGNs in 2MASS

  A summary of Brant Nelson (IPAC) et al.'s work on the search for red AGNs, typified by QSOs, Seyfert 1, and Seyfert 2 nuclei, in the 2MASS data.

• 2MASS Extended Source Catalog: Overview and Algorithms

  Tom Jarrett (IPAC) et al. describe the basic algorithms used to detect and characterize extended sources in the 2MASS database and catalog. They introduce and provide examples of the types of extended sources that 2MASS detects across the sky, including galaxies, Galactic nebulae and resolved stellar objects, multiple stars and clusters, and artifacts arising from bright stars and transient events.

• Near-Infrared Galaxy Morphology

  Tom Jarrett (IPAC) presents an image Atlas of a sample of galaxies observed with 2MASS. The images are separated according to the traditionally-based Hubble type or morphological classification. In addition to the main elliptical/spheroidal and spiral-type classes, examples of compact, dwarf, irregular/peculiar and AGN/Seyfert types of extragalactic objects are also included. A simple analysis of the Atlas shows that the different classes are not as well separated with infrared colors (the exception being the extremely red class of AGN galaxies), but are clearly distinguished when comparing central surface brightness and total flux. A detailed comparison between optical and infrared properties, including sizes, elongation, colors and surface brightness, reveals clear differences between spiral types and between barred and normal disk galaxies. With more sophisticated measurements (e.g., decoupled bulge-to-disk ratios) and methods (e.g., supervised neural networks), and in combination with optical imaging, it should be possible to derive morphological classifications for a data set that is numbered in the millions.

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