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I am doing photometric calibration of a telescope and am trying to work out the errors on the flux that I measure. I have attempted to work out the errors due to the process of removing bias, dark frames, etc. using Poisson statistics, but this gives a fractional error of about 0.7, which is poor. I may try this analysis again using Gaussian stats, but I also want a measure of the error on the raw flux in the image (ignoring processing).

I have tried to do this by taking many observations of one star to look at the variation of flux; it roughly looks like a Gaussian, but I can't get a fit to it without altering the data.

Is there a good way to find a flux variation from data I have taken?

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Why not just treat the photometry readings as an arbitrary scale and calibrate based on the known flux of stable reference stars? –  Andrew Apr 8 '12 at 17:33

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I don't know much about observational stuff, but since you haven't gotten any answers I thought I would chip in what I could...

The error of the raw flux will be based on poisson noise from the signal, and complex read-out/detector noise from your instrument. The latter should be documented by the manufacturer. The former you can calculate based on the observed flux and the poisson distribution.

In terms of your post-processing; I think the best way to find statistical errors would be to compare between different images and different rounds of calibration. I.e. how much do dark-frames vary? How much do flat-fields vary? etc.

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