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Analysis Tools for Spectral Surveys

Published online by Cambridge University Press:  21 December 2011

Peter Schilke ,
Rainer Rolffs  and
Claudia Comito
Peter Schilke
Affiliation:
I. Physikalisches Institut der Universität zu Köln, Zülpicher Str. 77, 50937 Köln, Germany email: [email protected]
Rainer Rolffs
Affiliation:
I. Physikalisches Institut der Universität zu Köln, Zülpicher Str. 77, 50937 Köln, Germany email: [email protected] MPIfR, Auf dem Hügel 69, 53121 Bonn, Germany email: [email protected], [email protected]
Claudia Comito
Affiliation:
MPIfR, Auf dem Hügel 69, 53121 Bonn, Germany email: [email protected], [email protected]
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Abstract

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Spectral surveys in the past were a hobby of a few, usually restricted to strong, line-rich and close-by sources which were considered templates for source classes, e.g. Orion KL for hot cores, IRC+10216 for AGB stars, and CRL618 for protoplanetary nebulae. Not any more, since with the large bandwidths and high sensitivities of modern instruments, notably ALMA, all but a few sources will show many lines from many molecules at every observations. So (involuntary) line surveys will be the norm rather than the exception. A common strategy is to ignore all lines but the few one is interested in. Since all data will be available through the archive, this does not mean that the data are lost, since eventually the information will be extracted. Another strategy is to take the bull by the horns, and try to analyze all or at least a large portion of the spectrum. This includes the steps of line identification, source modeling and linking to physical and chemical models. With the data volumes at hand doing it the traditional, pedestrian, way is somewhere between impractical and impossible, semi-automatic methods need to be employed.

Keywords

Astrochemistyline:identificationradiative transfermethods:data analysisstars: formationISM: molecules
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 7 , Symposium S280: The Molecular Universe , June 2011 , pp. 440 - 448
Copyright
Copyright © International Astronomical Union 2011

References

Belloche, A., Menten, K. M., Comito, C., Müller, H. S. P., Schilke, P., Ott, J., Thorwirth, S., & Hieret, C. 2008, A&A, 482, 179Google Scholar
Belloche, A., Garrod, R. T., Müller, H. S. P., Menten, K. M., Comito, C., & Schilke, P. 2009, A&A, 499, 215Google Scholar
Bergin, E. A., et al. 2010, A&A, 521, L20Google Scholar
Bernst, I., Schilke, P., Möller, T., Panoglou, D., Ossenkopf, V., Röllig, M., Stutzki, J., & Muders, D. 2010, Astronomical Society of the Pacific Conference Series, 442, 505Google Scholar
Brinch, C. & Hogerheijde, M. R. 2010, A&A, 523, A25Google Scholar
Ceccarelli, C., et al. 2010, A&A, 521, L22Google Scholar
Chavarría, L., et al. 2010, A&A, 521, L37Google Scholar
Comito, C., Schilke, P., Phillips, T. G., Lis, D. C., Motte, F., & Mehringer, D. 2005, ApJS, 156, 127CrossRefGoogle Scholar
Hogerheijde, M. R. & van der Tak, F. F. S. 2000, A&A, 362, 697Google Scholar
Juvela, M. 1998, A&A, 329, 659Google Scholar
Keto, E. R. 1990, ApJ, 355, 190CrossRefGoogle Scholar
Keto, E. & Caselli, P. 2010, MNRAS, 402, 1625CrossRefGoogle Scholar
Keto, E., Rybicki, G. B., Bergin, E. A., & Plume, R. 2004, ApJ, 613, 355CrossRefGoogle Scholar
Krumholz, M. R., Klein, R. I., McKee, C. F., Offner, S. S. R., & Cunningham, A. J. 2009, Science, 323, 754CrossRefGoogle Scholar
Maret, S., Hily-Blant, P., Pety, J., Bardeau, S., & Reynier, E. 2011, A&A, 526, A47Google Scholar
Müller, H. S. P., Schlöder, F., Stutzki, J. & Winnewisser, G. 2005 J. Mol. Struct. 742, 215CrossRefGoogle Scholar
Müller, H. S. P., Thorwirth, S., Roth, D. A., & Winnewisser, G. 2001, A&A, 370, L49Google Scholar
Nenkova, M., Ivezić, Ž., & Elitzur, M. 2000, Thermal Emission Spectroscopy and Analysis of Dust, Disks, and Regoliths, 196, 77Google Scholar
Panić, O., Hogerheijde, M. R., Wilner, D., & Qi, C. 2009, A&A, 501, 269Google Scholar
Peters, T., Klessen, R. S., Mac Low, M.-M., & Banerjee, R. 2010, ApJ, 725, 134CrossRefGoogle Scholar
Pickett, H. M., Poynter, R. L., Cohen, E. A., Delitsky, M. L., Pearson, J. C., & Müller, H. S. P. 1998 J. Quant. Spectrosc. & Rad. Transfer 60, 883CrossRefGoogle Scholar
Rolffs, R., et al. 2010, A&A, 521, L46Google Scholar
Rolffs, R., Schilke, P., Wyrowski, F., Menten, K. M., Güsten, R., & Bisschop, S. E. 2011, A&A, 527, A68Google Scholar
Schilke, P., Benford, D. J., Hunter, T. R., Lis, D. C., & Phillips, T. G. 2001, ApJS, 132, 281CrossRefGoogle Scholar
Shi, H., Zhao, J.-H., & Han, J. L. 2010, ApJ, 710, 843CrossRefGoogle Scholar
van der Tak, F. F. S., van Dishoeck, E. F., Evans, N. J. II, & Blake, G. A. 2000, ApJ, 537, 283CrossRefGoogle Scholar
van Dishoeck, E. F. & van der Tak, F. F. S. 2000, From Molecular Clouds to Planetary Systems, Proc. IAU Symposium No. 197, p. 97CrossRefGoogle Scholar
Wang, P., Li, Z.-Y., Abel, T., & Nakamura, F. 2010, ApJ, 709, 27CrossRefGoogle Scholar