Working backwards to find parameters that produced best fit model and Contour plot annotation.

Im comparing some field data to a model and I'm trying to find the values for two variables (dke and theta) that produce the best fitting model, i.e. lowest RMS (root mean square) value. I know what the best RMS is but I don't know how to work backwards to find the best fitting values for dke and theta.

I also wish to annotate a contour plot for RMS over the range of dke and theta tested. Is there a way I can annotate the plot with the best RMS value with an arrow pointing to its point? Hope someone can help. Cheers.

% Define the variables x = rawdata(:,1); I = -64.*pi/180; h = 1; A = 45.*pi/180; T = 55000; d = 5;

% Calculate dT

for i = 1:31
   for j = 1:46

      dke = 0.002:0.0001:0.005; 
      theta = 45.*pi/180:2.*pi/180:135.*pi/180; 

      a = 2.*atan(tan(I)./cos(A))-theta(j);

      AMP = 2.*dke(i).*T.*((sin(I)./(sin(atan(tan(I)./cos(A)))))^2).*sin(theta(j));

      dT_calc = AMP.*(sin(a).*(atan((x + d) ./ h) - atan((x - d) ./ h)) - ...
          (cos(a)./2).*log(((x + d).^2 + h.^2)./((x - d).^2 + h.^2)));

      % RMS
      % Comparing the quality of fit between the observed and calculated data

      n = 201;        % Number of values

      RMS(i,j) = sqrt((1./n).*(sum((dT_obs - dT_calc).^2)));

     end
  end

Best_RMS = min(RMS(:));