(Note figure 1 is missing, but is shown below (on this web page).)

Publications: (link
to ADS
list)

- A Comparison of Least-Squares and Bayesian Techniques in Fitting
the Orbits of Extrasolar Planets 2005AAS...207.6822D
- The N2K Consortium. I. A Hot Saturn Planet Orbiting HD 88133 2005ApJ...620..481F
- Fitting Astrometric Data With Markov Chain Monte Carlo: A Tool
for Detecting Planetary Signals 2004AAS...20513505D
- Search for Terrestrial Planets with the Space Interferometry
Mission 2004AAS...205.3907M

More than 160 extrasolar planet
have been discovered orbiting nearby stars in our Milky Way
Galaxy. Due to observational biasing, most of these planets are
Jupiter mass (1 M_{Jupiter} = 318 M_{Earth}) and have
rather short orbital periods. The California planet search team
detects the presence of planets indirectly, through the motion of the
primary star. As a planet (including the planets in our solar
system) orbits its primary star it induces a small gravitational wobble
in the star. If this wobble is aligned along our line of sight
(radially) then the light from the star is doppler shifted.
This doppler shift is measured as a shift of the stellar spectral
lines, and the radial velocity of the wobbling star is
calculated. The radial velocity of the star in time is a
sinusoidal variation described by the derivative of Kepler's Law of
orbital motion. Kepler's law is nonlinear and contains at least
six free parameters (per planet): period, amplitude, eccentricity,
argument of perihelion, time of periastron passage, and amplitude
off-set. To find the best fit orbital parameters for a given
radial velocity curve a parameter estimation technique must be
used. For simple data a frequentist method, such as
Levenberg-Marquardt, suffices to find the most likely orbital
parameters, or maximum likelihood estimation (MLE). For less than
ideal data sets with low phase coverage, number of observations, or
unknown number of planets a more robust parametric technique is employed.
My research is in developing and testing a Bayesian fitting method
called Markov chain Monte Carlo (MCMC), which uses a
Metropolis-Hastings algorithm to search parameter space. A
convergent Markov chain is proportional to the posterior probability
density function (PDF) for each parameter. For more details see
my thesis in progress. Here are some
links to expert Bayesian astrostatisticians.

Eric Ford (UC Berekely)

Phil
Gregory (U British Columbia)

Tom Loredo
(Cornell U)

Bill Jefferys (U Texas)

Here are some links to other experts in the field of planets and planet
formation.

Debra
Fischer (SFSU)

John Chambers
(Carnegie Institute)

Derek Richardson (U Maryland)

Greg Laughlin
(UC Santa Cruz)

Doug Lin (UC
Santa Cruz)

All my orbit fitting research is done in the IDL programming
language. For more information about my team at SFSU visit here.