With various systems to simulate realistic observations at hand – Chris’ noise simulator and Hans’ ideal image simulator – it becomes possible to pose some questions, such as:

1) Is it better to reduce every
blessed image and then average derived results, or should images be
coadded and then reduced?

2) Knowing that the bias has a 1-count
amplitude, 20-minute period is it then a sensible
strategy to scale a low-noise ‘superbias’ to the observed but noisy bias?

3) What is the relationship between precision in alfa and scatter in
DS/BS ratio, in the presence of realistic noise?

A partial, idealized, answer to 3) is hinted at by considering the change in DS and BS intensities as a function of change in alfa:

DS change: -15.9935 %
BS change: 0.0893807 %
alfa ch: 0.995023 % [Note that PSF was normalized]

I.e. If we change alfa by 1% we get a 16% change in a typical DS point and a 0.1% change in a typical BS point. This suggest that we need to know alfa to an accuracy of 0.6% (alfa was 1.7) or 0.006. This would be the typical step-size of a grid-search, for instance, and the tolerance on any downward-descent search. Perhaps it is easy to get such accuracy with methods that ‘fit the sky’ – such as both the BBSO and our own forward methods.

How does the above change in the presence of realistic noise?