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Studying Milky Way Emission to assess Galaxy Observations – LEGO

The Galactic star forming region W49 (Barnes et al. 2020)

LEGO is a Large Program that used the IRAM 30-m telescope to produce sensitive emission maps of key chemical tracers in more than a dozen Galactic star forming regions. LEGO is an international collaboration led by Jens Kauffmann (MIT).

In recent years, molecular emission lines have become major tools for the study of extragalactic molecular clouds. Species like HCN allow to selectively characterize the dense gas in galaxies, while molecules like CH3OH are used to trace shocks related to spiral arm structure. However, before being able to exploit extragalactic line emission data to its full potential, a detailed understanding of these — and many other — emission lines in local Milky Way molecular clouds is essential. LEGO is a major step in this direction by developing a first comprehensive picture of how 3mm-band emission lines in Milky Way molecular clouds which depends on factors like cloud density, star formation feedback, galactic environment, and metallicity. To do this a variety of Milky Way molecular clouds have been surveyed with a bandwidth of 32 GHz that is now easily accessible using the EMIR heterodyne receiver at the 30-meter telescope. These observations do support e.g. extra-galactic Large Programs of nearby galaxies like EMPIRE. The comprehensive data have been explored by the team of observers and theoretists specialized in galactic and extragalactic star formation, improving on the ability to interpret cosmic molecular line emission.

The official LEGO data repository is IRAM.

For basic details on the data processing see Barnes et al. (2020). The link above provides access to the online calibrated 30m files on the TA* scale. More data products (data cubes and integrated intensity maps) are in preparation. The README file provides a detailed description of the data release.

Please do not hesitate to contact Jens Kauffmann to tell him about your use of the data for scientific analysis. The following acknowledgement would be appreciated: “This work made use of LEGO (Barnes et al. 2020).” Acknowledgment of the LEGO project is also appreciated if data are used in public presentations.

Last updated: January 30, 2024