I have worked with ResearchSEA on getting some videos out there to promote the sciences. I have been interviewing some people, condensing their stories into a minute(-ish) of Youtubey goodness. The videos are available on the new ResearchSEA website here and here. They are the first two, there will be more at a rate of about one every three weeks.
As always, feel free to let me know what you think!
I have worked on monte-carlo procedures for a while now. Initially (while working for Aarhus University), I managed to make these work to extract the particle size distribution from isotropic scattering patterns, and it works fine and quick.
Now though, I managed to get this trial-and-error procedure to work for whole 2D pattens as well! The added dimension adds some coolness to the whole thing, and carries extra information to boot. I will be showing the details and results in my upcoming talks (hopefully at SAS2012 as well), but for now, please enjoy the short movie where the new method is applied to a measurement of PPTA fibre:
(just a quick post about interesting things to keep the blog alive. More interesting developments are afoot, I hope to be able to post about them shortly)
I suspect that all of the readers here are familiar with the wonderful resource of fascinating talks that is TED. Yesterday, a group of people had their first meeting for setting up a TEDxTsukuba event. We are planning to hold the event early July in the center of the city we are in.
It is going to be small, partly due to TEDx rules which allow only 100 people to attend. I will give another shout out about this as soon as there is a website for the event, and then I will talk about it no more (rest assured).
If you are interested in helping out, please leave a comment or drop me a line!
If you are doing SAXS-y or SANS-y things to metals, or are just interested in the technique or meeting some very good people: come and present at the IUMRS-ICEM 2012 meeting in Yokohama. I promise it will be good!
“Our” session is session D-8, and the deadline for abstract submission has been extended until the 17th of April. Abstracts only have to be one page long, so the barrier of entry remains low (apart from the quite sizable conference fee, that is).
See you in Yokohama in September!
So it seems science has beaten us to the punch once again. Remember last week’s optimistic story on how you can make better use of your (measurement) time? Turns out it has been done (at least once) before.
The year was 1993, the authors were M. Steinhart and J. Pleštil, and they did the same from a different perspective [1]. Credit where credit is due, their yet un-cited paper contains a good study of measurement stability and its effects on inferred information, and indeed has the equation for effective time-expenditure available (though written up in a confusing way).
So all sadness on our side aside (as there is now no short-sweet-and-quick publication possible on this), please use your time wisely and cite that 1993 paper as it deserves. Do not let good methods like this be covered by years of dust.
To give you some more ammunition for your citation-gun, here is a good paper detailing dead-time correction, and how Poisson statistics fail when these corrections are applied [2]. I found that I should not blindly square-root my photons (real and imaginary), but that I should use the equations they provided where applicable. If you have a Pilatus detector, there is no reason to sweat much, as this correction is only applied in the very extreme count-rate regions [3].
As always: let us know what you think and leave a comment!
Often, especially when measuring on big facilities, you are given a limited amount of time. So when it comes to measuring the sample and the background, this limited time has to be divided between a measurement of the sample, and a measurement of the background.
Normally, one would spend about 50% of the time on a sample, and 50% on the background, or even more time on the background “because the counts are so low” (I know, I did the same!). There must be a better way to calculate the optimum division of time!
So me and a colleague, Samuel Tardif, spent a little bit of time jotting down some equations, and plotting the result. The result is that for large differences in the signal-to-noise ratio (c.q. sample count rate to background count rate), significant reductions in uncertainty can be obtained through better division of time for any small-angle scattering measurement.
In the case of a Bonse-Hart camera or a step-scan small-angle scattering measurement, each measurement point can be tuned to the optimal dwell time for the sample and background, after a quick initial scan to determine the signal-to-noise ratio at each point. Please let me know how it works for you! The calculation can be checked from this document where we wrote up the results: ideal_background
Short news first; by going through the motions and waiting for Elsevier to get back to me, I have gotten permission (for the royal sum of 0.00 eurodollars) to repost one more paper from Polymer on my site. So that has now gone in the 2010 publications page here.
Then it is time to give you something. For those who have to do their own data processing and would like to get my way of doing it, I have attached my data processing flowchart to this post. It is not a perfect method, but as far as I can tell it works quite well. If you are interested in getting the actual Python code that does all this work, drop me a line. Since the code is quite new, it does not support many strange detectors, so if support needs to be built for a particular detector, I’ll be happy to spend some of my time looking at whether it can be done.
So there:imp_imagecorrect_and_imgint. Let me know if there are improvements, obscurities or if you have any other comments on this.
“Hello,
I am in the workshop at the moment, learning to cut, file, drill, lathe, mill, wire-EDM, and CNC, so I can construct enough components to assemble a Bonse-Hart camera on an unused X-ray generator. Please leave a message (or wish me luck) in the comments section, and I will get back to you as soon as I can.”
Dear scatterers,
Those of you who have been reading this weblog for a while now, may remember the calculation of the sample self-absorption correction for plate-like samples. The result of this was a straightforward equation which could be used to correct the scattering of strongly absorbing samples (>30%) with a plate-like geometry. It was mentioned then, that the calculation of this correction for capillary samples is more complicated, but would be good to have. This sample self-absorption of a capillary will show up as a butterfly-shaped shadow on your scattering pattern.
In the latest issue of J. Appl. Cryst., there is a new paper discussing exactly this. Sulyanov et al. have (programmed) a solution to calculate the sample self-absorption factor for cylindrical samples. The code they provide is available in Fortran, and I will spend some time to try to transcode this into Python in the near future. Judging from their solutions, I am happy I did not try to solve it. The solution seems to be a little bit more complicated than I thought.
Additionally, in the same issue, Zeidler has a solution for samples of spherical geometry. While I have not encountered a problem requiring this solution before, it is certainly noteworthy, and may be of use to some of you doing scattering from suspended objects.
Lastly, there is a new video of one of my latest short presentations online here, explaining a little about my work as well as the monte-carlo analysis method. It’s very short, and there will be a more detailed MC method explanation shortly (as I have promised for quite a while now).
Scatter well!
If you give bad presentations, you can kill people. If you want to find out why, and get some tips on presentation techniques (the ones I use, anyway), you can watch this video I recorded recently.
( http://www.youtube.com/watch?v=eVtL9NCXyEs )