Home List of Titles Markov Chain Monte Carlo methods applied to measuring the fine structure constant from quasar spectroscopy
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/211154
- Markov Chain Monte Carlo methods applied to measuring the fine structure constant from quasar spectroscopy
- King, J. A.; Mortlock, D. J.; Webb, J. K.; Murphy, M. T.
- Recent attempts to constrain cosmological variation in the fine structure constant, alpha , using quasar absorption lines have yielded two statistical samples which initially appear to be inconsistent. One of these samples was subsequently demonstrated to not pass consistency tests; it appears that the optimisation algorithm used to fit the model to the spectra failed. Nevertheless, the results of the other hinge on the robustness of the spectral fitting program VPFIT, which has been tested through simulation but not through direct exploration of the likelihood function. We present the application of Markov Chain Monte Carlo (MCMC) methods to this problem, and demonstrate that VPFIT produces similar values and uncertainties for Delta alpha /alpha , the fractional change in the fine structure constant, as our MCMC algorithm, and thus that VPFIT is reliable.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology. Faculty of Information and Communication Technologies. Centre for Astrophysics and Supercomputing
- Memorie della Societa Astronomica Italiana (Journal of the Italian Astronomical Society): incorporating proceedings Joint Discussion 9, the 27th IAU General Assembly, Rio de Janeiro, 10-11 August 2009, Vol. 80, no. 4 (2009), pp. 864-869
- Publication year
- FOR Code(s)
- 0201 Astronomical and Space Sciences
- Atomic processes; Cosmology: observations; Methods: numerical; Methods: statistical; Quasars: absorption lines; Quasars: individual: LBQS 0013-0029; Quasars: individual: LBQS 2206-1958; Quasars: individual: Q 0551-366
- Italian Astronomical Society
- Publisher URL
- Copyright © 2009 SAIt.