How do I combine relative errors in particle energy measurements?

In summary, the conversation discusses combining relative errors in evaluating the energy of a particle. There are two main errors: one due to the wrong location of the spectrogram centre and one due to the resolution of the system. The question is how to combine these two errors, and the answer is that as long as they are independent, the total error can be calculated using the square root of the sum of the squares of the individual errors. However, it is unclear if the two errors are truly independent, and further analysis is needed to determine the covariance and how it affects the overall error.
  • #1
1Keenan
101
4
Hi,

I'm getting confused in combining relative errors.
I have a relative error in evaluating the energy of some particle which is due to a wrong location of the spectrogram centre. this errors affects the energy and the relative error due to the energy resolution of the system.
Thus I have two relative errors: the one due to the centre location and the one due to the resolution which are actually dependet.

How I have to combine both errors? is it correct to sum them?
 
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  • #2
So long as two errors are independent, the total error is square root of sum of the squares of errors.

<X + Y> = <X> + <Y>
<(X+Y)²> = <X²> + <Y²> + 2<XY>

Square of the error is the variance, which is defined Var(X) = <X²>-<X>²

Var(X+Y) = Var(X) + Var(Y) + 2Cov(X,Y)

Where covariance, Cov(X,Y) = <XY>-<X><Y> and is zero for independent X and Y.
 
  • #3
You are absolutely right, but the thing is that I don't think the errors are not indipendent: the error in the energy due to the wrong centre location affects the error due to the intrinsic resolution of the system...
 
  • #4
You need to figure out how the two are related and estimate covariance. Assume P(X) is normal. Then figure out what you can say about P(Y|X). Sounds from your description like you might expect it to be a normal distribution whose width depends on X. I'm not entirely sure, though. It's not clear why you think the two are related. Maybe you can explain it in more detail.

At any rate, once you have P(X) and P(Y|X), get covariance, and use it with formulae above.
 
  • #5
I don't know... maybe it is me making the thing too complicate... I'm just confused and there is no literature about my problem...
 

1. What is the formula for combining relative errors?

The formula for combining relative errors is:
R = √(R1^2 + R2^2 + ... + Rn^2)
Where R represents the combined relative error, and R1, R2, ..., Rn represent the individual relative errors.

2. Why is it important to combine relative errors?

Combining relative errors is important because it provides a more accurate estimate of the overall uncertainty or variability in a measurement or calculation. By combining the relative errors of multiple components, we can better understand the total error and make more informed decisions.

3. Can relative errors be combined for any type of measurement or calculation?

Yes, relative errors can be combined for any type of measurement or calculation, as long as the individual relative errors are known. This includes both experimental measurements and mathematical calculations.

4. Is the combined relative error always smaller than the individual relative errors?

No, the combined relative error can be either smaller or larger than the individual relative errors, depending on the values of the individual errors. If the individual errors are all of similar magnitude, the combined relative error will likely be larger. However, if some of the individual errors are very small, the combined relative error may be smaller.

5. How can combined relative errors be used in decision making?

Combined relative errors can be used in decision making by providing a measure of the overall uncertainty or variability in a measurement or calculation. This information can help scientists determine the reliability of their results and make decisions about the significance or accuracy of their findings.

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