maximum-likelihood estimates are often optima of cross-correlations

I've been writing like mad in a paper about measuring radial velocities. The standard practice involves cross correlations. The information-theoretic results suggest maximum-likelihood estimates. What gives? It turns out that, provided that your models are normalized in a certain way, maximizing a cross-correlation between data and a template can be equivalent to minimizing a chi-squared or maximizing a log likelihood. I wrote words about all this in my de-novo re-write of my paper with Bedell (Flatiron) on how you measure a radial velocity.