Hostname: page-component-76fb5796d-9pm4c Total loading time: 0 Render date: 2024-04-26T02:18:40.727Z Has data issue: false hasContentIssue false

Mapping the Distributions of Exoplanet Populations with NICI and GPI

Published online by Cambridge University Press:  27 January 2016

Eric L. Nielsen
Affiliation:
SETI Institute, 189 Bernardo Ave. Suite 100, Mountain View, CA 94043. email: enielsen@seti.org Kavli Institute for Particle Astrophysics and Cosmology, 452 Lomita Mall, Stanford, CA 94305.
Michael C. Liu
Affiliation:
Institute for Astronomy, University of Hawaii at Manoa.
Zahed Wahhaj
Affiliation:
European Southern Observatory.
Beth A. Biller
Affiliation:
Institute for Astronomy, University of Edinburgh.
Thomas L. Hayward
Affiliation:
Gemini Observatory.
Laird M. Close
Affiliation:
Steward Observatory, University of Arizona.
Bruce Macintosh
Affiliation:
Kavli Institute for Particle Astrophysics and Cosmology, 452 Lomita Mall, Stanford, CA 94305.
Dmitry Savransky
Affiliation:
Cornell University.
Jason J. Wang
Affiliation:
University of California, Berkeley.
James R. Graham
Affiliation:
University of California, Berkeley.
Robert J. De Rosa
Affiliation:
University of California, Berkeley.
Abhijith Rajan
Affiliation:
Arizona State University.
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.

While more and more long-period giant planets are discovered by direct imaging, the distribution of planets at these separations (≳5 AU) has remained largely uncertain, especially compared to planets in the inner regions of solar systems probed by RV and transit techniques. The low frequency, the detection challenges, and heterogeneous samples make determining the mass and orbit distributions of directly imaged planets at the end of a survey difficult. By utilizing Monte Carlo methods that incorporate the age, distance, and spectral type of each target, we can use all stars in the survey, not just those with detected planets, to learn about the underlying population. We have produced upper limits and direct measurements of the frequency of these planets with the most recent generation of direct imaging surveys. The Gemini NICI Planet-Finding Campaign observed 220 young, nearby stars at a median H-band contrast of 14.5 magnitudes at 1”, representing the largest, deepest search for exoplanets by the completion of the survey. The Gemini Planet Imager Exoplanet Survey is in the process of surveying 600 stars, pushing these contrasts to a few tenths of an arcsecond from the star. With the advent of large surveys (many hundreds of stars) using advanced planet-imagers we gain the ability to move beyond measuring the frequency of wide-separation giant planets and to simultaneously determine the distribution as a function of planet mass, semi-major axis, and stellar mass, and so directly test models of planet formation and evolution.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2016 

References

Beuzit, J. L., Boccaletti, A., Feldt, M., Dohlen, K., Mouillet, D., et al. 2010, ASPC, 430, 231.Google Scholar
Biller, B. A., Liu, M. C., Wahhaj, Z., Nielsen, E. L., Close, L. M., et al. 2010, ApJ, 720, 82.Google Scholar
Biller, B. A., Liu, M. C., Wahhaj, Z., Nielsen, E. L., Hayward, T. L., et al. 2013, ApJ, 777, 160.Google Scholar
Biller, B. A., Liu, M. C., Rice, K., Wahhaj, Z., Nielsen, E. L., et al. 2015, MNRAS, 450, 4446.Google Scholar
Cumming, A., Butler, R. P., March, G. W., Vogt, S. S., et al. 2008, PASP, 120, 531.Google Scholar
Chilcote, J., Barman, T., Fitzgerald, M. P., Graham, J. R., et al. 2015, ApJ, 798, 3.Google Scholar
Liu, M. C., Wahhaj, Z., Biller, B. A., Nielsen, E. L., Chun, M., et al. 2010, SPIE, 7736, 1.Google Scholar
Hayward, T. L., Biller, B. A., Liu, M. C., Nielsen, E. L., et al. 2014, PASP, 126, 1112.Google Scholar
Johnson, J. A., Aller, K. M., Howard, A. W., & Crepp, J. R. 2010, PASP, 122, 905.CrossRefGoogle Scholar
Macintosh, B., Graham, J. R., Ingraham, P., Konopacky, Q., et al. 2014, PNAS, 1111 2661.Google Scholar
Macintosh, B., Anthony, A., Atwood, J., Bauman, B., Cardwell, A., et al. 2014b, SPIE, 9148 0.Google Scholar
Macintosh, B., Graham, J. R., Barman, T., De Rosa, R. J.et al. 2015, Science, in press.Google Scholar
Males, J. R., Close, L. M., Morzinski, K. M., Wahhaj, Z., Liu, M. C., et al. 2014, ApJ, 786, 32.Google Scholar
Nielsen, E. L., Close, L. M., Guirado, J. C., Biller, B. A., Lenzen, R., et al. 2005, AN, 326, 1033.Google Scholar
Nielsen, E. L., Close, L. M., Biller, B. A., Masciadri, E., & Lenzen, R. 2008, ApJ, 674, 466.Google Scholar
Nielsen, E. L. & Close, L. M. 2010, ApJ, 717, 878.Google Scholar
Nielsen, E. L., Liu, M. C., Wahhaj, Z., Biller, B. A., Hayward, T. L., et al. 2012, ApJ, 750, 53.CrossRefGoogle Scholar
Nielsen, E. L., Liu, M. C., Wahhaj, Z., Biller, B. A., Hayward, T. L., et al. 2013, ApJ, 776, 4.CrossRefGoogle Scholar
Nielsen, E. L., Liu, M. C., Wahhaj, Z., Biller, B. A., Hayward, T. L., et al. 2014, ApJ, 794, 158.Google Scholar
Perrin, M. D., Maire, J., Ingraham, P., Savransky, D., et al. 2014, SPIE, 9147, 3.Google Scholar
Perrin, M. D., Duchene, G., Millar-Blanchaer, M., Fitzgerald, M. P., et al. 2015, ApJ, 799, 182.Google Scholar
Skemer, A. J., Hinz, P., Esposito, S., Skrutskie, M. F., Defrere, D., et al. 2014, SPIE, 9148, 0.Google Scholar
Tamura, M., et al. 2014, IAUS, 299, 12.Google Scholar
Vigan, A., Patience, J., Marois, C., Bonavita, M., De Rosa, R. J., et al. 2012, A&A, 544, 9.Google Scholar
Wahhaj, Z., Liu, M. C., Biller, B. A., Clarke, F., Nielsen, E. L., et al. 2011, ApJ, 729, 139.Google Scholar
Wahhaj, Z., Liu, M. C., Nielsen, E. L., Biller, B. A., et al. 2013a, ApJ, 773, 179.Google Scholar
Wahhaj, Z., Liu, M. C., Biller, B. A., Nielsen, E. L., Hayward, T. L., et al. 2013b, ApJ, 779, 80.Google Scholar
Wahhaj, Z., Liu, M. C., Biller, B. A., Nielsen, E. L., Hayward, T. L., et al. 2014, ApJ, 567, 34.Google Scholar