Abstract: Methods and systems for detecting and characterizing a pattern (or patterns) of interest in a low signal-to-noise ratio (SNR) data set are disclosed. One method is a two-stage Likelihood pipeline analysis that takes advantage of the benefits of a full Likelihood analysis while providing computational tractability. The two-stage pipeline may include a first stage including the application of approximate Likelihood functions in which one or more of the following assumptions or modifications may be applied: (i) the pattern of interest and background are at a specified position in a segment of the data set under examination; (ii) the SNR is low; and (iii) measurement noise can be represented in such a form that all non-position parameters of the representation are linear with respect to the derivative of the Log Likelihood versus lambda. The second stage may include a full Likelihood analysis.