Hostname: page-component-6bf8c574d5-b4m5d Total loading time: 0 Render date: 2025-02-19T22:00:40.200Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust Constraint of Luminosity Function Evolution through MCMC Sampling

Published online by Cambridge University Press:  01 July 2015

Noah Kurinsky  and
Anna Sajina
Noah Kurinsky
Affiliation:
Department of Physics and Astronomy, Tufts University, 4 Colby Street, Medford, MA 02155, USA Email: [email protected]
Anna Sajina
Affiliation:
Department of Physics and Astronomy, Tufts University, 4 Colby Street, Medford, MA 02155, USA 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.

We present a new galaxy survey simulation package, which combines the power of Markov Chain Monte Carlo (MCMC) sampling with a robust and adaptable model of galaxy evolution. The aim of this code is to aid in the characterization and study of new and existing galaxy surveys. In this paper we briefly describe the MCMC implementation and the survey simulation methodology and associated tools. A test case of this full suite was to constrain the evolution of the IR Luminosity Function (LF) based on the HerMES (Herschel SPIRE) survey of the Spitzer First Look Survey field. The initial results are consistent with previous studies, but our more general approach should be of wider benefit to the community.

Keywords

galaxies: luminosity functiongalaxies: evolutiongalaxies: statisticsmethods: analyticaltechniques: photometricinfrared: galaxies
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 295 - 297
Copyright
Copyright © International Astronomical Union 2015 

References

Dunkley, J., Bucher, M., Ferreira, P. G., Moodley, K., & Skordis, C. 2005, MNRAS, 356, 925Google Scholar
Lewis, A. & Bridle, S. 2002, Phys. Rev. D. 66, 10Google Scholar
Kurinsky, N., Sajina, A., & Silva, A. 2014, in. prep.Google Scholar
Marsden, G., Chapin, E. L., Halpern, M., Patanchon, G., Scott, D., Truch, M. D. P., Valiante, E., Viero, M. P., & Wiebe, D. V. 2011, MNRAS 417, 1192CrossRefGoogle Scholar
Rieke, G. H., Alonso-Herrero, A., Weiner, B. J., Pérez-González, P. G., Blaylock, M., Donley, J. L., & Marcillac, D. 2009, ApJ 692, 556CrossRefGoogle Scholar
Sajina, A., Yan, L., Fadda, D., Dasyra, K., & Huynh, M. 2012, ApJ, 757, 13CrossRefGoogle Scholar
Silva, A., Sajina, A., & Kurinsky, N. 2015, in. prep.Google Scholar