The RMS survey: far-infrared photometry of young massive stars^*

¹ School of Physics and Astronomy, University of Leeds, Leeds, LS2 9JT, UK e-mail: joe@astro.ex.ac.uk
² Department of Physics and Astronomy, University of Exeter, Exeter, Devon, EX4 4QL, UK
³ Australia Telescope National Facility, CSIRO, Sydney, NSW 2052, Australia
⁴ Univ. of Wisconsin – Madison, Dept. of Astronomy, 475 N. Charter St., Madison, WI 53716, USA
⁵ Astrophysics Research Institute, Liverpool John Moores University, Twelve Quays House, Egerton Wharf, Birkenhead, CH41 1LD, UK

Received: 18 September 2009
Accepted: 7 December 2009

Abstract

Context. The Red MSX Source (RMS) survey is a multi-wavelength campaign of follow-up observations of a colour-selected sample of candidate massive young stellar objects (MYSOs) in the galactic plane. This survey is returning the largest well-selected sample of MYSOs to date, while identifying other dust contaminant sources with similar mid-infrared colours including a large number of new ultra-compact (UC) H ii regions.

Aims. To measure the far-infrared (IR) flux, which lies near the peak of the spectral energy distribution (SED) of MYSOs and UCH ii regions, so that, together with distance information, the luminosity of these sources can be obtained.

Methods. Less than 50% of RMS sources are associated with IRAS point sources with detections at 60 μm and 100 μm, though the vast majority are visible in Spitzer MIPSGAL or IRAS Galaxy Atlas (IGA) images. However, standard aperture photometry is not appropriate for these data due to crowding of sources and strong spatially variable far-IR background emission in the galactic plane. A new technique using a 2-dimensional fit to the background in an annulus around each source is therefore used to obtain far-IR photometry for young RMS sources.

Results. Far-IR fluxes are obtained for a total of 1113 RMS candidates identified as young sources. Of these 734 have flux measurements using IGA 60 μm and 100 μm images and 724 using MIPSGAL 70 μm images, with 345 having measurements in both data sets.