The ability to see patterns in random data is known as Apophenia. Its what sets us apart from animals - on the other hand, scepticism of such patterns sets us apart from palaeontologists!
( I don't *really* mean that, honest!)
Hi Jon,
That is so true. In fact humans are even more adept at seeing faces in noise. If you see Elvis in peeling paint, this is probably "pareidolia" and is illustrated in the PPT slides attached below, which I used for years in my "Weird Science" freshmen seminar. Here's a famous example:
https://en.wikipedia.org/wiki/Chonosuke_Okamuraif so, will this actually produce a meaningful detection limit calculation for those binned areas _ i mean, it may potentially produce a better image (which is all that matters), but you'd have to propagate the peak/background in some meaningful way, no?
This is essentially what I was trying to get across to Ben above.
If one bins the raw data images before loading them in CalcImage (that is on and off-peak x-ray maps) using a 2 x 2 bin, each pixel will now be approximately 4 times the intensity and hence about twice the sensitivity- but with lower resolution of course. In this case the binned pixels will propagate the improved statistics quite naturally through the quant calculations.
However, if one really wants to improve detection limits for x-ray mapping, one can also utilize MAN background corrections. With this method we can improve sensitivity by roughly 40% and in approximately 1/2 the acquisition time as described here:
http://probesoftware.com/smf/index.php?topic=425.msg2296#msg2296It won't work for all samples, but it can work for pure elements, oxides, simple silicates and sulfides. That is, it probably won't work for monazite, but will work for SiO2, TiO2, ZrSiO4, FeS2, etc. Any matrix that one can obtain a "blank" standard for to maintain accuracy.