Determining peak of normal distributed data


#1

Hi there,

I am a super novice R Studio user and I am pretty sure, that what I want to do is pretty easy, but I just can’t get it to work.
I have a Data set from an Oscilloscope, in it there is a trigger signal and a normally distributed signal. (The Experiment was a Haynes-Shockley-Eperiment, where a laser triggers the liberation of a cloud of electrons that can be detected at a probe.)

I would like to determine the time, where the peak of the normally distributed signal is, and with what error.

I hope you can help me get into this…

Best regards,
Michael


#2

Can you try abstracting your question a bit as well as putting it in reprex?

At least for me it is not clear what exactly you want to do and especially since there are a lot of moving parts and your set-up is very specific.


#3

Thanks for your reply,
You can view the data here: https://www.dropbox.com/sh/zsn9s814u1gtvp2/AADV7SRaDNsjQ9d6th_S72Wza?dl=0

The first column is the Time, the second one is the Signal.

In the signal, there is a trigger signal and a peak, that should resemble a normal distribution.
My goal is to determine the time that passed between the trigger and the peak of the normal distribution.


#4

This is the graph of the data.

So, what you are saying, all you need to do is to find a difference between a trigger (I assume, it is a point with the lowest value of U around 0) and maximum value of the peak? You can find positions of both of those things using which.min and which.max respectively, e.g.:

m10$t[which.max(m10$U)] - m10$t[which.min(m10$U)]

#5

Thank you very much for the suggestion, but for the second peak, I have to use a Gauss fit since it is no sharp peak. Is there a simple way to do a Gaussian regression around the 2nd peak and get something like \mu, standard deviation etc?