Linear regression to radii of multiple circles

In summary: You have a set of simulated data in two dimensions. Each circle corresponds to a different trajectory. You then add noise to the radii to simulate real world effects. You need to reconstruct the most probable path that the muon took.You have a set of simulated data in two dimensions. Each circle corresponds to a different trajectory. You then add noise to the radii to simulate real world effects. You need to reconstruct the most probable path that the muon took. You also need to fit a linear regression on your entire set of simulated data.
  • #1
Nick.Kallas
8
0
Hi,
I am trying to simulate muon paths through drift tubes and I have ran into a problem performing a linear regression. I have generated simulated muon trajectories in 2 dimensions and they passes through my simulated drift tubes represented as black circles with a '+' in the center. As the trajectories passes through the tubes they leave an omnidirectional radius represented as colored circles. Each color corresponds to a different trajectory. I then take these radii and add noise to them simulating real world effects. Using these noisy radii I need to reconstruct the most probable path that the muon took.

Basically I have a number of circles that I need to fit a tangent line to. If anyone could help point me in the right direction it would be greatly appreciated.

https://decibel.ni.com/content/servlet/JiveServlet/showImage/105-24702-34909/MUON+RUN.jpg [Broken]
 
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  • #2
Hey Nick.Kallas.

Are you trying to fit a linear regression on your entire set of simulated data?

If this is the case, then I would suggest you use a GLM where you have discrete measurements for each corresponding "row" of circles (tubes) and in this context you will have four variables corresponding to the four rows.

Then you fit the model using a statistics program (SAS, R, whatever) and you will get an equation that fits the rows.

You'll probably find that you get a lot of dependencies (since it is a straight line) and you can eliminate the dependencies in a few ways.

The first way I would suggest is to look at Principal Component Analysis and the second way is to use back-ward selection and select the best sub-model which doesn't lose too much variability within the model.

Are you familiar with these techniques?
 
  • #3
I just need to fit a single trajectory at a time, sorry for the ambiguity. I need to make my own algorithm so that I can eventually implement it on my hardware and there are also other factors this algorithm needs to take into account. So I am using a single trajectories information to recreate it that is using the information from a single color radii on this simulation.

So basically I think i just need to figure out a way to apply a linear regression in polar coordinates with only radius data. I am sort of at a loss of how to do this and any pertinent literature would even be helpful.

If you want a better idea of what I am tying to do here is our projects website.
https://decibel.ni.com/content/groups/muon-detector-nmt-senior-design-team-2012-2013
 
  • #4
Just to clarify, is the radius data the intersection distance from the centre of a drift tube that it passes through?
 
  • #5
the radii data is represented by the closest point along the trajectory to the center of the respective tube.
 
  • #6
Sounds like this is going to look like a normal residual against the origin of the tube.

However I need to ask, what variables do you have in total and what are you trying to relate? (Is it angle against radii or vice-versa or something else)?
 

1. What is linear regression?

Linear regression is a statistical method used to model the relationship between a dependent variable (such as radii of circles) and one or more independent variables (such as the number of circles). It assumes that there is a linear relationship between the variables, meaning that the change in the dependent variable can be explained by changes in the independent variable(s).

2. How is linear regression used in analyzing radii of multiple circles?

In the context of radii of multiple circles, linear regression can be used to determine the relationship between the number of circles and the average radii of those circles. This can help identify any patterns or trends in the data and make predictions about the radii of future circles based on the number of circles.

3. What are the assumptions of linear regression?

The main assumptions of linear regression are that the relationship between the variables is linear, the data is normally distributed, and there is little to no multicollinearity (high correlation) among the independent variables. Additionally, the data should be free of outliers and homoscedastic (constant variance).

4. How do you interpret the results of a linear regression analysis on radii of multiple circles?

The results of a linear regression analysis will typically include a regression equation, which shows the relationship between the variables, as well as statistical measures such as the coefficient of determination (R-squared) and the p-value. These measures can help determine the strength and significance of the relationship between the variables, and whether the model is a good fit for the data.

5. What are the limitations of using linear regression for analyzing radii of multiple circles?

One limitation of using linear regression for analyzing radii of multiple circles is that it assumes a linear relationship between the variables, which may not always be the case. Additionally, linear regression is a parametric method, meaning it makes assumptions about the underlying distribution of the data. If these assumptions are not met, the results may be inaccurate. Lastly, linear regression may not be suitable for complex relationships between variables, as it can only capture linear patterns.

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