[ml] Statistics / software question
jake at spaz.org
Fri May 11 22:30:16 PDT 2012
well i've stashed graphical plots of the bins, but I do not have stored
raw data at this time because my java program what makes these graphics
doesn't save the serial data to a file. oops.
But i will correct that asap and start providing data. in the meantime
you can look at these plots:
and you will notice that there's absolutely no data there, it's all noise.
I have determined that this is because my scintillating crystal of NaI(Tl)
is "fried" meaning the person who sold it to me was an asshole, and the
amount of blue light coming out of the crystal has nothing to do with the
energy of the original gamma. Random data.
fortunately I have other crystals (including a BGO crystal) and I will
capture more data soon, which will hopefully look more like this:
there is other data, from an older version of this detector with a
different crystal (a nasty ruined crystal but maybe not quite fried)
which you can look at here:
On Fri, 11 May 2012, Christoph Maier wrote:
> Link to the raw data, please.
> On Fri, May 11, 2012 at 8:55 PM, Wladyslaw Zbikowski
> <embeddedlinuxguy at gmail.com> wrote:
> Hi, my friend Jake has a little project for which we might be
> able to
> use some statistics expertise.
> We are taking analog readings from a device (a voltage pulse).
> voltage pulse represents the energy of a single X-ray particle
> MeV). We know what the energy signature is supposed to look like
> this particular radioactive material; i.e. there are peaks where
> certain energies are highly represented, and valleys where other
> energy levels are rare. So we would like to correlate our
> with the expected signature.
> The problems are:
> 1. A lot of noise. We have a signal:noise ratio around 1:1 or as
> as 4:1, because of background radiation and attempts at
> 2. We don't know exactly how the voltage we read maps to MeV.
> Voltage is a function of Energy, presumably linear, but we don't
> exactly the scale (how many MeV per volt).
> SO in short, we have a graph of our data, and we want to
> force-fit it
> to the graph we expect. My idea is to apply noise removal and
> getting the closest possible match. Any thoughts on this? R?
> Possible topic for a meetup? We can post the graphs and the
> if anyone is interested to see.
> Thanks in advance!
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