This question might be obvious and dumb, but are you doing dark subtraction, bias frames, and flat fielding for these images? The biggest sources of noise in astronomical images are dark current, readout noise, and the chip not being evenly illuminated/evenly sensitive. Poisson noise is below all of those, and is what's left after you subtract out the first three things.Danny Uncanny7 wrote:I want to tell if what I am looking at is just noise and no signal.
Also, what are you planning to do with these images? Just counting stars? Or some kind of photometry with them? Your S/N ratio requirements are going to be different for those tasks, and when reducing/analyzing your data, you should keep those requirements in mind.
D.B. wrote:Can I just ask, do you have any more info on what the "optimizer" you refer to in the original post doing? Can it give you probabilistic data of star location, numbers, etc, or is it essentially a black box that spits out a list of 'stars found here, here and here'?
See, as you've explained it, you're trying to determine whether or not the remaining pixels in an image (following subtraction) is best described by all noise or by the presence of another star. You've got a probabilistic model for the noise (gaussian at the moment, possibly getting improved later). If you had access to a probabilistic model of what a star looks like then this would be really really useful information - it's going to be much easier to find a faint signal in noise if you have a good idea what that signal should be. You could also think about testing some standard methods of model selection to see how they do. It's going to be easier to defend using some custom method of finding remaining stars that you come up with yourself later if you've already considered stuff like BIC, Laplace's method, etc, and can show why yours is better.
(EDIT: Of course, perhaps what your friends want is an entirely separate method of spotting missed stars that doesn't use their model, so that systematic errors caused by inaccuracies in said model can be eliminated...)Also, what are you planning to do with these images? Just counting stars? Or some kind of photometry with them? Your S/N ratio requirements are going to be different for those tasks, and when reducing/analyzing your data, you should keep those requirements in mind.
I think this would be a valuable thing to find out.
A1) Reading this as "what is the probability that a given probability distribution would produce this arbitrary image?"Danny Uncanny7 wrote:Edit: without letting the problem sprawl out of hand, I guess what I really want to figure out is this: for an arbitrary image (or any data set really), what is the probability that each pixel was independently selected from the same probability distribution? I think that the answer will in some way come down to the autocorrelation, or some form of it.
for i in xrange(1,dim):
for ii in xrange(1,dim):
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