In article ,
Bill Jillians wrote:

1. No Gaussians

You can fit a Gaussian to random noise, it just would be a very poor
fit. You would have to specify how poor for this post to make any

Not in S@H. It tries to fit so many gaussians to each work unit that
approximately one of them will be good enough to report to Berkeley.
That's true of all the detection modes; they are all calibrated for
the order of one false positive per work unit. What will stop gaussians
is an unacceptable angle range, that stops them being tested for.

Also note that there is no absolute amplitude information in the work
unit so connecting the feed to any noise source will produce similar
statistics.