Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-23T08:04:18.546Z Has data issue: false hasContentIssue false
  • English
  • Français

Quick-MESS: A fast statistical tool for Exoplanet Imaging Surveys

Published online by Cambridge University Press:  06 January 2014

Mariangela Bonavita ,
Ernst De Mooij ,
Ray Jayawardhana  and
Raffaele Gratton
Mariangela Bonavita
Affiliation:
Osservatorio Astronomico di Padova - INAF Vicolo dell'Osservatorio, 5 35141 Padova (Italy) email: [email protected] Dept. of Astronomy & Astrophysics, University of Toronto, 50 St. George St. M5S 3H4 Toronto ON (Canada)
Ernst De Mooij
Affiliation:
Dept. of Astronomy & Astrophysics, University of Toronto, 50 St. George St. M5S 3H4 Toronto ON (Canada)
Ray Jayawardhana
Affiliation:
Dept. of Astronomy & Astrophysics, University of Toronto, 50 St. George St. M5S 3H4 Toronto ON (Canada)
Raffaele Gratton
Affiliation:
Osservatorio Astronomico di Padova - INAF Vicolo dell'Osservatorio, 5 35141 Padova (Italy) email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Several tools have been developed for the analysis of the results of direct imaging exoplanet surveys, mostly using a combination of Monte-Carlo simulations or a Bayesian approach. Here we present a novel approach to the statistical analysis of Direct Imaging surveys, called Quick-MESS, which allows for a much faster and flexible analysis.

Keywords

stars: low-massbrown dwarfs(stars:) planetary systemsmethods: statistical
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 8 , Symposium S299: Exploring the Formation and Evolution of Planetary Systems , June 2013 , pp. 28 - 29
Copyright
Copyright © International Astronomical Union 2013 

References

Baraffe, I., Chabrier, G., Barman, T. S., Allard, F., & Hauschildt, P. H. 2003, A&A, 402, 701Google Scholar
Beuzit, J.-L., Feldt, M., Dohlen, K., et al. 2008, SPIE, 7014Google Scholar
Bonavita, M., Chauvin, G., Desidera, S., et al. 2012, A&A, 537, A67Google Scholar
Bonavita, M., de Mooij, E. J. W., & Jayawardhana, R. 2013, PASP, 125, 849Google Scholar
Burrows, A., Sudarsky, D., & Lunine, J. I. 2003, ApJ, 596, 587Google Scholar
Chauvin, G., Lagrange, A.-M., Bonavita, M., et al. 2010, A&A, 509, A52Google Scholar
Cumming, A., Butler, R. P., Marcy, G. W., et al. 2008, PASP, 120, 531CrossRefGoogle Scholar
Lafrenière, D., Jayawardhana, R. & van Kerkwijk, M. H. 2008, ApJL, 689, L153Google Scholar
Nielsen, E. L. & Close, L. M. 2010, ApJ, 717, 878CrossRefGoogle Scholar
Vigan, A., Patience, J., Marois, C., et al. 2012, A&A, 544, A9Google Scholar