# [ml] Statistics / software question

Fri May 11 20:55:00 PDT 2012

```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). The
voltage pulse represents the energy of a single X-ray particle (in
MeV). We know what the energy signature is supposed to look like for
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 measurement
with the expected signature.

The problems are:

1. A lot of noise. We have a signal:noise ratio around 1:1 or as good
as 4:1, because of background radiation and attempts at shielding.

2. We don't know exactly how the voltage we read maps to MeV. I.e.
Voltage is a function of Energy, presumably linear, but we don't know
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 scaling,
getting the closest possible match. Any thoughts on this? R? Python?
Possible topic for a meetup? We can post the graphs and the software
if anyone is interested to see.