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Old 15-02-2016, 07:09 PM
E_ri_k (Erik)
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Problems calibrating images in PI

Hi guys, I'm having a problem calibrating images in PixInsight (Its been a while!). I have acquired 100 bias frames and 50 dark frames, and created masters of both following the PI tutorial.

When calibrated I end up with an image which looks badly clipped?? (see screenshot)

I noticed if I calibrate using ONLY using the bias, or ONLY the dark, the image looks fine, but when I use BOTH bias and dark I get the strange output?

Same result if I don't use the masters, and use the single bias and dark?

Any ideas?

Erik
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Old 15-02-2016, 07:44 PM
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RickS (Rick)
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Hi Erik,

Which tutorial was that? Did you use BatchPreprocessing or did you do the individual steps to integrate the bias frames to create a master bias, calibrate the darks, integrate the calibrated darks to create a master dark, etc?

I can't think of anything obvious that might have gone wrong but it's always possible Could be something to do with the pedestal on the FITS files. It's also possible that your result is OK and something weird has happened with the screen stretch.

I'd start by looking at your bias and dark masters to see if they look reasonable on visual inspection and then check them with the Statistics process to see if the data values look sensible too.

If you don't mind uploading a few bias, dark and light frames and the masters I'd be happy to take a look at them.

Cheers,
Rick.
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Old 15-02-2016, 07:59 PM
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Atmos (Colin)
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To me it sounds a bit like the darks aren't being calibrated so it is effectively double removing the bias from the images.
That's all I can think of.
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Old 15-02-2016, 08:04 PM
E_ri_k (Erik)
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Hey Rick. The tutorial was from the PI website. I followed all the steps manually......I think I found the solution though. There's tick box under the master dark and master bias which reads "calibrate" or "optimise" after a bit of playing around I for it worked when the "calibrate" boxes were ticked.

Thanks for the offer, that would be great I'm happy with my darks, but something is not sitting right with the bias, the master looks too "clean"?

I'll dropbox them and PM you the link when they're done

Erik
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Old 15-02-2016, 09:06 PM
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Thanks for the data, Erik. It all looks OK. You've done one thing differently to the way I used to build my calibration masters - you haven't calibrated the master dark to remove the bias. I can tell because your master bias has a mean value of 2803 and the master dark has a mean value of 2873. If you'd calibrated the darks to remove bias your master dark would have a much smaller mean value (around 2873-2803 = 70 ADU).

Both your master bias and your master dark include bias. That's why you need to tick the "Calibrate" box for the master dark when you calibrate the lights. That tells PI to calibrate the master dark with the master bias before applying it. Otherwise, it will subtract the bias twice and you get clipped data.

It's perfectly valid to do things the way you have, so long as you tick the right box

FYI, your read noise is 14.71 ADU. I calculated this by subtracting the master bias from the single bias frame and calculating the standard deviation of the result (which is just a frame full of read noise!)

These days I don't bother with manual calibration and integration to create master frames and calibrate the lights. The BatchProcessing script does this just as well with less effort. I always do my final integration of the lights manually though. That's the part of the process where careful tweaking of the rejection algorithm and parameters can make a big difference to the quality of the result.

Cheers,
Rick.
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Old 16-02-2016, 07:11 PM
E_ri_k (Erik)
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Hey Rick, thanks for looking over that data for me. So basically I just went the long way around right?

While I was doing it I was thinking "there must be a more efficient way!" I'm only using the very basic tools in PI, I have a lot to learn, but I'm experimenting with new tools when I get the chance.

I'll have a look at the scripts in future, although I did use the debayer script, that worked well.
Erik
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