Open vpicouet opened 2 years ago
Sorry for the user forum. I had to close it as it was too difficult to maintain because of increasing security issues. You've come to the right place. There is some ambiguity about the background noise for a given source, as it may vary a lot over the isophotal footprint. Would you like something similar to BACKGROUND
, which is limited to the centroid pixel?
You may also use FLUX_ERR
divided by the square-root of ISOAREAF_IMAGE
. This assumes of course that the noise is uncorrelated from pixel to pixel (which is what SExtractor assumes anyway).
Another valuable estimator, if you are using model fitting, is King's equivalent noise area NOISEAREA_MODEL
.
Thanks Emmanuel for the answer!
Basically I wanted to get the SNR of the extracted sources but SNR_WIN does not seem to depend on the background that I have (as I am actually controlling the level of hot pixels, see gif).
So I wanted to create a very basic SNR estimator: FLUX_MAX / BACKGROUND_NOISE
where BACKGROUND_NOISE would by the estimated noise around the source (we could add the shot noise of the source but I do not really need it right now).
With your previous message, it appears that FLUXERR_ISO/ISOAREAF_IMAGE**0.5 is constant:
Do you know the easiest way to acces this information. Basically i could just extract the background 20 pixels away from the source and compute the standard deviation but I guess it is already implemented in SExtractor.
I guess BACKGROUND
is estimated on an annulus around the source and the value at the central position is interpolated/modeled.
Is there a way to retrieve the standard deviation of the same annulus or its interpolation at the central position?
Hello, is there a way to access the local noise/std for each extracted source in the output catalog? I have seen that in the starlink sextractor project there was a parameter for this (https://github.com/Starlink/sextractor/blob/master/src/param.h BKGSIG) but did not find one here. I posted it here as the user forum is a broken link in the readme. Thanks a lot!