One of the reduction steps performed has to do with the scaling of the bias images due to the thermostat-induced temperature variations in the CCD chip. This temperature variation causes a 20 minute period in the mean level of the bias with an amplitude of almost 1 count – thus of importance to our attempts to analyse extremely small signal levels.
We take Bias frames on both sides of all science exposures – one just before and one just after. If we were to just subtract the average of these frames from our science image we would be adding noise to the result – we therefore need to subtract a smoother bias frame. We have constructed a ‘super bias’ frame as the average of hundreds of bias frames – it is very smooth, but probably has a level that is unrelated to the actual level in each frame.
By scaling the super bias to the mean level of the average bias frames taken at the time of the science frame we get a scaled superbias that we can subtract – it has the right level and very little noise.
We need to understand how the scaling procedure performs, so we have extracted the scaling factor from the 5000+ exposures we have.
Top frame shows the factor on the superbias as a function of the sequence number, and the bottom panel as a a function of the Julian Day of the observation.
Most factors are near 1 but some stand out. 9 files have factors above 1.09 – their Julian days (integer part) are:
2455938 (7 images) 2455940 2456032 (one on each).
A list of the 209 images with factors over 1.08 is here:
The 4 unique JDs are: 2455814 2455938 2455940 2456032, with the majority of cases on 2455814.
These images should perhaps be inspected very carefully for problems with bias.
A close-up of the factors nearest 1 looks like this:
