Uncertainty Calculation for Quantum Physics Lab | General Physics FAQ

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SUMMARY

The discussion focuses on calculating uncertainty in a quantum physics lab when multiplying two quantities, x and y, where x has an uncertainty of b and y has an uncertainty of d. The maximum and minimum values for both quantities are defined as x_L, x_H, y_L, and y_H. The proposed method for determining the uncertainty of the product z = xy is to use the formula z \pm \left( \frac{z_H - z_L}{2} \right). Additionally, the partial derivatives method is mentioned, leading to the equation \sigma_{z}=\sqrt{d^{2}x^2 +b^2 y^2}, applicable when x and y are independent.

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Zarlucicil
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I'm in the process of doing a quantum physics lab and am having a bit of trouble with uncertainty. The specific things going on in the lab aren't relevant, I don't think, only the general procedure of my calculation. Also, I'm not certain where this question should be asked, so I decided to put it here (General Physics).

Let's say we are measuring a quantity x that has an uncertainty of b. Thus,
[tex]x \pm b[/tex].

Clearly, x has a maximum and minimum value. Namely,
[tex]x_L = x - b[/tex]
[tex]x_H = x + b[/tex].

There is another quantity y, which is constant, that has an uncertainty of d. It appears y also has a maximum and minimum value,
[tex]y_L = y - d[/tex]
[tex]y_H = y + d[/tex].

Now we want to multiply these two quantities together to get a new quantity z = xy. It seems that this value z has a maximum and minimum value as well,
[tex]z_L = y_L x_L[/tex]
[tex]z_H = y_H x_H[/tex].

Is it permissible to say that the uncertainty on z is the following? (The length between the two extreme values of z divided by 2)
[tex]\frac{z_H - z_L}{2}[/tex].

So, after we measure x we need to multiply by y to get z, and y is a constant which only needs to be measured once, but still as an uncertainty. Can we describe z in the following manner?

[tex]z \pm \left( \frac{z_H - z_L}{2} \right)[/tex]
 
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What I would use is the partial derivatives method. What you have is f(x,y)=z=xy then [tex]\sigma_{z}^{2}=\sigma_{x}^{2} (\frac{\partial z}{\partial x})_{x,y}^2+ \sigma_{y}^{2} (\frac{\partial z}{\partial y})_{x,y}^2[/tex] will give in your case: [tex]\sigma_{z}=\sqrt{d^{2}x^2 +b^2 y^2}[/tex].This is good only if your x and y do not have covariance.
 

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