We evaluate the effect of two different scattered-light removing techniques on our data, comparing to no removal at all. We do this by considering the DS/BS ratio – and we look at the ratio of this ratio in observations to the same ratio in models – so it is a ratio of ratios, ok?
The graphics do not reproduce well on this blog so I put a link to a pdf file here: download it and read on.
There are 3 pages to look at, and on each page there are 5 panels – one panel per filter, one page per data set.
The first data set shows the ratio of ratios extracted from data where only a bias has been subtracted, plotted against lunar phase. Second page shows the ratio of ratios when the LINEAR BBSO method has been applied, and the last page shows the ratio of ratios when our logarithmic variant of the BBSO method has been applied.
Full moon is at phase 0.
We see that there is a scatter and there is a systematic dependence on lunar phase – the points ‘curve up’ towards full moon (the middle) and towards the edges (new moon).
Near Full Moon it becomes increasingly difficult to remove scattered light because the BS is closer and closer to the patch on the DS where photometry was extracted. The Eartshine is also weaker and weaker as you approach Full Moon because that corresponds to approaching New Earth.
Therefore a hypothesis was that the ‘curve up’ towards Full Moon was due to incompletely removed scattered light. Looking at the raw vs linear vs log methods it is evident that this ‘curve up’ is substantially reduced by application of either of the scattered-light removal methods. The linear method does well and the log method adds an increment of improvement.
I believe these plots are a demonstration that BBSO linear is not perfect – and that small improvements are possible. It is open to discussion what BBSO has done about this problem – light seems not to be completely removed in their method. How do they average their data to compensate? We should note that our image scales and observing methods (ND filters; coadd etc) are not identical.
Soon we will add a third method – the EFM!