Cross-correlation methods for surface photometry: two-component models

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The cross-correlation or matched filter technique for determining surface photometric parameters in low signal-to-noise ratio data is extended to two-component models (i.e. up to four model parameters). It is shown that the linearity and distributivity of the convolution process greatly reduce the number of templates with which the data must be convolved. Indeed, provided that we are dealing with a significant number of images, obtaining a full four-parameter maximization requires only two to three times as much work as for a simple single-component fit. The method is simple to implement and particularly suited to determining the component parameters for many images in a single-frame