Small victories and Weighting Plan

I have now created my smeared gauusians and developed a way to import grids into idl variables :)

I have also decided on a plan of action for the "weighting". It served the purpose to decide cluster membership, so I will proceed to take into account cluster membership according to a Bayesian approach.

Let X= parameters of cluster and Prob(member|X) be A

Prob(Data| X )=Prob(D, member | X) + Prob(D, non-member |X)

=Prob(member| X)*Prob(D | member, X) + Prob(non-member|X)*Prob(D | non-member,X)

=A*Prob(D|member,x) + (1-A)/[(Vmax-Vmin)*(Bmax-Bmin)*(Umax-Umin)]

The last step follows from the probability being independent of the parameters and uniform over the parameter space. The size of the parameter space will be determined by the span of the data.

Though adding complication, I will follow a process to determine A similar to that of Kharchenko et al 2005. Solving my problem of uniform data set, I will use ASCC-2.5 data. I may limit that data source to those clusters with distance moduli less than 11 to keep a wider range of main sequence above the 14 magnitude limit. I will model location and proper motion (parrallax for closer clusters too), and determine membership probabilities based of the dispersion in those values.