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Published July 2010 | Published
Journal Article Open

100 μm and 160 μm emission as resolved star-formation rate estimators in M 33 (HERM33ES)

Abstract

Context. Over the past few years several studies have provided estimates of the SFR (star-formation rate) or the total infrared luminosity from just one infrared band. However these relations are generally derived for entire galaxies, which are known to contain a large scale diffuse emission that is not necessarily related to the latest star-formation episode. Aims. We provide new relations to estimate the SFR from resolved star-forming regions at 100 μm and 160 μm. Methods. We select individual star-forming regions in the nearby (840 kpc) galaxy M33. We estimate the SFR combining the emission in Hα and at 24 μm to calibrate the emission at 100 μm and 160 μm as SFR estimators, as mapped with PACS/Herschel. The data are obtained in the framework of the HERM33ES open time key program. Results. There is less emission in the HII regions at 160 μm than at 100 μm. Over a dynamic range of almost 2 dex in Σ_(SFR) we find that the 100 μm emission is a nearly linear estimator of the SFR, whereas that at 160 μm is slightly superlinear. Conclusions. The behaviour of individual star-forming regions is surprisingly similar to that of entire galaxies. At high Σ_(SFR), star formation drives the dust temperature, whereas uncertainties and variations in radiation-transfer and dust-heated processes dominate at low Σ_(SFR). Detailed modelling of both galaxies and individual star forming regions will be needed to interpret similarities and differences between the two and assess the fraction of diffuse emission in galaxies.

Additional Information

© 2010 ESO. Received 31 March 2010, Accepted 21 April 2010, Published online 16 July 2010. Herschel is an ESA space observatory with science instruments provided by European–led Principal Investigator consortia and with important participation from NASA. We thank Herschel scientists for their valuable help with the PACS data reduction, in particular Babar Ali, Bruno Altieri, Bidushi Bhattacharya, Nicolas Billot and Marc Sauvage. We also thank the NHSC for providing the computing architecture used in the reduction of the data. We also thank our referee, C. K. Xu, for useful comments that helped improve the quality of this article.

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