Uncertainties and the angle of minimum deviation

In summary, the conversation discusses the use of the angle of minimum deviation formula in a physics experiment to find uncertainties. There is a question about how to calculate the uncertainty and a discussion about using derivatives to do so. The conversation also touches on the difficulty of understanding the concept of derivatives and suggests using a graphical approach. Finally, the conversation includes a step-by-step explanation of how to use calculus to calculate the uncertainty.
  • #1
Jane Smith
12
1

Homework Statement


For a physics experiment I need to find the uncertainties and I am using the angle of minimum derivation formula:
1a4ee7cc8377d03e0e50298e6cae5debc6a747b3

The value of A=60° and one of the values of D is 29.7° which has a uncertainty of ±2 (I know it's a very high value)

Homework Equations


How do I calculate the uncertainty for this?

The Attempt at a Solution


I found a video which included some formulas but I did not understand (attached photos)
Screenshot_20190109-165444.png
Screenshot_20190109-165433.png
 

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  • #3
When you write ## n_{\lambda}=f(D_{\lambda} ) ##, then ## \sigma_{n_{\lambda}}=(\frac{\partial{n_{\lambda}}}{\partial{D_{\lambda}}}) \sigma_{D_{\lambda}} ##. ## \\ ## Do you know how to take the derivative? (In this case, you really only have one variable ## D_{\lambda} ##, so the partial derivative signs aren't necessary). ## \\ ## In the page that you show in the OP on the left, I think they are ignoring any uncertainties in the apex angle.
 
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  • #4
Charles Link said:
When you write ## n_{\lambda}=f(D_{\lambda} ) ##, then ## \sigma_{n_{\lambda}}=(\frac{\partial{n_{\lambda}}}{\partial{D_{\lambda}}}) \sigma_{D_{\lambda}} ##. ## \\ ## Do you know how to take the derivative? (In this case, you really only have one variable ## D_{\lambda} ##, so the partial derivative signs aren't necessary).
No
 
  • #5
See the additional line I added above (Post 3). The derivative involves the chain rule. The derivative of the ## \sin(\theta) ## is ## \cos(\theta) ##. On this problem, it is almost necessary to have had at least one calculus course to be able to see what they are doing.
 
  • #6
If the value of ## A=60^{\circ} ##, I think that should be ## 30^{\circ} ## rather than ## 45^{\circ} ## inside the cosine function.## \\ ## (I think they may have used ## A=90^{\circ} ##, and that would explain the factot of ## \sqrt{2} ## that they get in the denominator, i.e. ## \sin(\frac{90^{\circ}}{2})=\frac{\sqrt{2}}{2} ## ).
 
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  • #7
Charles Link said:
See the additional line I added above (Post 3). The derivative involves the chain rule. The derivative of the ## \sin(\theta) ## is ## \cos(\theta) ##. On this problem, it is almost necessary to have had at least one calculus course to be able to see what they are doing.
I did take calculus last year but its been so long I don't feel confident on my abilities. But once I did derive it how would I proceed from there. Would it be possible to calculate this graphically?
 
  • #8
I can show you the calculus. Let ## D_{\lambda}=x ##. We will treat ## A ## as a constant. The formula you have for ## n_{\lambda} ## is then ## n_{\lambda}=\frac{\sin(\frac{x}{2}+\frac{A}{2} )}{\sin(\frac{A}{2})} ##. ## \\ ## ## (\frac{dn_{\lambda}}{dx})=(\frac{1}{\sin(\frac{A}{2})})(\frac{1}{2})( \cos(\frac{x}{2}+\frac{A}{2})) ##. ## \\ ## [And ## \sigma_r=(\frac{dn_{\lambda}}{dx})(\sigma_L) ##].## \\ ## Can you follow the chain rule that I used to get the ## (\frac{1}{2} ) ##, (middle term), because it is ## \sin(\frac{x}{2}+\frac{A}{2} ) ##, (with a 2 below the ## x ##) ? ## \\ ## I'll show you the chain rule here: If ## f(x)=\sin(x) ##, then ## f'(x)=\cos(x) ##, ## \\ ## but if ## f(x)=\sin(\frac{x}{2}+c) ##, then ## f'(x)=(\frac{df(u)}{du})(\frac{du}{dx} ) ##, where in this case ## u=\frac{x}{2}+c ##. ## \\ ## Meanwhile the factor ## \sin(\frac{A}{2}) ## in the denominator is simply a constant factor: i.e. ## \frac{d( Cf(x))}{dx}= C \frac{d f(x)}{dx} ##. ## \\ ## The calculus is a little easier than it first looks. Don't be afraid to study it=I do think you should be able to follow the derivation.:smile: ## \\ ## Edit: See also post 6 above again=I added some detail.
 
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  • #9
Charles Link said:
I can show you the calculus. Let ## D_{\lambda}=x ##. We will treat ## A ## as a constant. The formula you have for ## n_{\lambda} ## is then ## n_{\lambda}=\frac{\sin(\frac{x}{2}+\frac{A}{2} )}{\sin(\frac{A}{2})} ##. ## \\ ## ## (\frac{dn_{\lambda}}{dx})=(\frac{1}{\sin(\frac{A}{2})})(\frac{1}{2})( \cos(\frac{x}{2}+\frac{A}{2})) ##. ## \\ ## [And ## \sigma_r=(\frac{dn_{\lambda}}{dx})(\sigma_L) ##].## \\ ## Can you follow the chain rule that I used to get the ## (\frac{1}{2} ) ##, (middle term), because it is ## \sin(\frac{x}{2}+\frac{A}{2} ) ##, (with a 2 below the ## x ##) ? ## \\ ## I'll show you the chain rule here: If ## f(x)=\sin(x) ##, then ## f'(x)=\cos(x) ##, ## \\ ## but if ## f(x)=\sin(\frac{x}{2}+c) ##, then ## f'(x)=(\frac{df(u)}{du})(\frac{du}{dx} ) ##, where in this case ## u=\frac{x}{2}+c ##. ## \\ ## Meanwhile the factor ## \sin(\frac{A}{2}) ## in the denominator is simply a constant factor: i.e. ## \frac{d( Cf(x))}{dx}= C \frac{d f(x)}{dx} ##. ## \\ ## The calculus is a little easier than it first looks. Don't be afraid to study it=I do think you should be able to follow the derivation.:smile: ## \\ ## See also post 6 above again=I added some detail.

upload_2019-1-9_19-22-36.png
Is this correct? How do I find the uncertainty using this?
 

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  • #10
The uncertainty is ## \sigma_r ##. They might do well to call it ## \Delta n ##, but they are using statistical type of language, where ## \sigma ## is the letter they use for standard deviation. And yes, what you have is correct. The uncertainty ## \Delta n=\sigma_r=(\frac{dn}{dx})\sigma_L=(\frac{dn}{dx}) \Delta_{\theta_L} ##. ## \\ ## One additional note: This one is a little tricky, because when you put in ## \Delta \theta_L= \sigma_L ## i this formula, you need to convert it to radians, i.e. express ## \Delta \theta_L ## in radians. The result is ## \Delta \theta_L=2 (\frac{\pi}{180} ) ##.Everywhere else you can use degrees, and it doesn't matter, but here you need to have ## \Delta \theta_L ## in radians. ## \\ ## The reason for this is the derivative of the ## \sin(x) ## is equal to the ## \cos(x) ## only when ## x ## is measured in radians. Otherwise there is an additional factor. (This is kind of an advanced topic. I'll try to explain:) To start this explanation, they are basically writing ## dn=\Delta n=(\frac{dn}{d \theta_L}) \, d \theta_L=(\frac{dn}{d \theta_L}) \, \Delta \theta_L ##. The derivative gives you the cosine only if it is measured in radians. The derivative derivation assumes ## \frac{\sin(\Delta x)}{\Delta x }=1 ## for small ## \Delta x ##, but that is only the case of ## \Delta x ## is measured in radians. If ## \Delta x ## is measured in degrees, then in taking the derivative, the factor that appears of ## \frac{\sin(\Delta x)}{\Delta x} ## for small ##\Delta x ## will give a factor of ## \frac{\pi}{180} ## instead of a factor of one.
 
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  • #11
Charles Link said:
The uncertainty is ## \sigma_r ##. They might do well to call it ## \Delta n ##, but they are using statistical type of language, where ## \sigma ## is the letter they use for standard deviation. And yes, what you have is correct. The uncertainty ## \Delta n=\sigma_r=(\frac{dn}{dx})\sigma_L=(\frac{dn}{dx}) \Delta_{\theta_L} ##. ## \\ ## One additional note: This one is a little tricky, because when you put in ## \Delta \theta_L= \sigma_L ## i this formula, you need to convert it to radians, i.e. express ## \Delta \theta_L ## in radians. The result is ## \Delta \theta_L=2 (\frac{\pi}{180} ) ##.Everywhere else you can use degrees, and it doesn't matter, but here you need to have ## \Delta \theta_L ## in radians. ## \\ ## The reason for this is the derivative of the ## \sin(x) ## is equal to the ## \cos(x) ## only when ## x ## is measured in radians. Otherwise there is an additional factor. (This is kind of an advanced topic. I'll try to explain:) To start this explanation, they are basically writing ## dn=\Delta n=(\frac{dn}{d \theta_L}) \, d \theta_L=(\frac{dn}{d \theta_L}) \, \Delta \theta_L ##. The derivative gives you the cosine only if it is measured in radians. The derivative derivation assumes ## \frac{\sin(\Delta x)}{\Delta x }=1 ## for small ## \Delta x ##, but that is only the case of ## \Delta x ## is measured in radians. If ## \Delta x ## is measured in degrees, then in taking the derivative, the factor that appears of ## \frac{\sin(\Delta x)}{\Delta x} ## for small ##\Delta x ## will give a factor of ## \frac{\pi}{180} ## instead of a factor of one.

Okay so, since 29.7° is equal to 0.5183628 radians. Do I simply just use that in the formula.
upload_2019-1-9_20-31-31.png
To which I get the answer 0.7, meaning there is an uncertainty of ±0.7?
 

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  • #12
The only place you need to convert to radians is in computing ## \sigma_L=\Delta \theta_L=2(\frac{\pi}{180}) ##. Everywhere else, simply use degrees and set your calculator to "degrees". ## \\ ## The uncertainty in the refractive index ## \Delta n=\sigma_n=(\frac{dn}{dD_{\lambda}})\sigma_L ##. I think you are saying you get a factor of .7 for the derivative (which is also what I get), but you need to multiply by ## \sigma_L \approx .035 ## (radians) to get the uncertainty ## \sigma_n=\Delta n ##. ## \\ ## I don't know if they specify in the notes they gave you that ## \sigma_L= \Delta \theta_L ## needs to be measured in radians in your formula for ## \sigma_n \, (=\sigma_r) ##, but this is very important. Without it, the results are incorrect= you get uncertainty in ## n ## that is ## \Delta n=\pm 1.4 ##. (Remember ## \sigma_L=2 ## if you incorrectly write it in degrees in this formula). ## \\ ## If you compute the ## \Delta \theta_L ## in radians (the correct way to do it), you get ## \Delta n=\pm .02 ##. ## \\ ## The index ## n ## is normally about 1.5 for glass, and with an uncertainty of ## \Delta n=\pm 1.4 ##, it would be a very useless experiment. Fortunately, the correct uncertainty ## \Delta n =\pm .02 ##, and it is actually a very accurate experiment.
 
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  • #13
Charles Link said:
The only place you need to convert to radians is in computing ## \sigma_L=\Delta \theta_L=2(\frac{\pi}{180}) ##. Everywhere else, simply use degrees and set your calculator to "degrees". ## \\ ## The uncertainty in the refractive index ## \Delta n=\sigma_n=(\frac{dn}{dD_{\lambda}})\sigma_L ##. I think you are saying you get a factor of .7 for the derivative (which is also what I get), but you need to multiply by ## \sigma_L \approx .035 ## (radians) to get the uncertainty ## \sigma_n=\Delta n ##. ## \\ ## I don't know if they specify in the notes they gave you that ## \sigma_L= \Delta \theta_L ## needs to be measured in radians in your formula for ## \sigma_n \, (=\sigma_r) ##, but this is very important. Without it, the results are incorrect= you get uncertainty in ## n ## that is ## \Delta n=\pm 1.4 ##. (Remember ## \sigma_L=2 ## if you incorrectly write it in degrees in this formula). ## \\ ## If you compute the ## \Delta \theta_L ## in radians (the correct way to do it), you get ## \Delta n=\pm .02 ##. ## \\ ## The index ## n ## is normally about 1.5 for glass, and with an uncertainty of ## \Delta n=\pm 1.4 ##, it would be a very useless experiment. Fortunately, the correct uncertainty ## \Delta n =\pm .02 ##, and it is actually a very accurate experiment.
I am sorry, but I am confused to how you get to 0.035
Charles Link said:
The only place you need to convert to radians is in computing ## \sigma_L=\Delta \theta_L=2(\frac{\pi}{180}) ##. Everywhere else, simply use degrees and set your calculator to "degrees". ## \\ ## The uncertainty in the refractive index ## \Delta n=\sigma_n=(\frac{dn}{dD_{\lambda}})\sigma_L ##. I think you are saying you get a factor of .7 for the derivative (which is also what I get), but you need to multiply by ## \sigma_L \approx .035 ## (radians) to get the uncertainty ## \sigma_n=\Delta n ##. ## \\ ## I don't know if they specify in the notes they gave you that ## \sigma_L= \Delta \theta_L ## needs to be measured in radians in your formula for ## \sigma_n \, (=\sigma_r) ##, but this is very important. Without it, the results are incorrect= you get uncertainty in ## n ## that is ## \Delta n=\pm 1.4 ##. (Remember ## \sigma_L=2 ## if you incorrectly write it in degrees in this formula). ## \\ ## If you compute the ## \Delta \theta_L ## in radians (the correct way to do it), you get ## \Delta n=\pm .02 ##. ## \\ ## The index ## n ## is normally about 1.5 for glass, and with an uncertainty of ## \Delta n=\pm 1.4 ##, it would be a very useless experiment. Fortunately, the correct uncertainty ## \Delta n =\pm .02 ##, and it is actually a very accurate experiment.
I think I finally understand, thank you so much :)
 
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  • #14
Just one additional comment: It would be a very easy mistake for the professor to make if he forgot to include the detail that the uncertainty in the measured angle needs to be in radians in using it to compute ## \Delta n ##. The "radians" is actually dimensionless just like the index of refraction is, so that both sides of the equation ## \Delta n=(\frac{d n}{d D_{\lambda}}) \Delta \theta_L ## are free of units. ## \\ ## In any case, it is very important that the number ## \Delta \theta_L ## is expressed in radians in the calculation of ## \Delta n ##, because of some finer details about the derivative of ## \sin(x) ## that are discussed in post 10 above. ## \\ ## ===========================================================================## \\ ## Additional item: If you have have ever seen the Taylor series: ## \\ ## ## \sin(x)=x-\frac{x^3}{3!} + \frac{x^5}{5!} +... ##, ## \\ ## you might ask, why does ## x ## need to be measured in radians for this formula to work? ## \\ ## It comes from ## f(x)=f(a)+f'(a)(x-a)+f''(a) \frac{(x-a)^2}{2!} +... ##.## \\ ## The reason the radians are required is because the derivatives are only correct when ## x ## is measured in radians. If ## x ## is measured in degrees, the derivatives pick up a factor of ## \frac{\pi}{180} ## as mentioned in post 10 above.
 
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  • #15
Jane Smith said:

Homework Statement


For a physics experiment I need to find the uncertainties and I am using the angle of minimum derivation formula:
1a4ee7cc8377d03e0e50298e6cae5debc6a747b3

The value of A=60° and one of the values of D is 29.7° which has a uncertainty of ±2 (I know it's a very high value)

Homework Equations


How do I calculate the uncertainty for this?

The Attempt at a Solution


I found a video which included some formulas but I did not understand (attached photos)View attachment 237048 View attachment 237050

The very simplest way is to take the three values ##D = 29.7## (central value), ##D_{low} = 29.7 - 2 = 27.7## (lowest value) and ##D_{hi} = 29.7 + 2 = 31.7## (highest value), then do the calculation for all three values of ##D##.
 
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  • #16
Ray Vickson said:
The very simplest way is to take the three values ##D = 29.7## (central value), ##D_{low} = 29.7 - 2 = 27.7## (lowest value) and ##D_{hi} = 29.7 + 2 = 31.7## (highest value), then do the calculation for all three values of ##D##.
i.e. Calculate ## n ## for the 3 values of ## D ##. Yes, this is a good way, and should also get the result that ## \Delta n \approx \pm .02 ##.
 

1. What is the angle of minimum deviation?

The angle of minimum deviation is the angle at which a ray of light passing through a prism is deviated the least from its original path. This angle varies depending on the material and shape of the prism.

2. How is the angle of minimum deviation calculated?

The angle of minimum deviation can be calculated using the equation: Dm = (A + D)/2, where Dm is the angle of minimum deviation, A is the angle of incidence, and D is the angle of prism.

3. What is the significance of uncertainties in measuring the angle of minimum deviation?

Uncertainties in measuring the angle of minimum deviation can affect the accuracy of the results. These uncertainties can arise from human error, limitations of the measuring equipment, and external factors such as temperature and air currents.

4. How can uncertainties be minimized in measuring the angle of minimum deviation?

To minimize uncertainties, it is important to use precise measuring equipment and techniques. The experiment should also be repeated multiple times to obtain an average value and reduce the impact of any outliers. Controlling external factors such as temperature and air currents can also help minimize uncertainties.

5. What are some applications of understanding uncertainties and the angle of minimum deviation?

Understanding uncertainties and the angle of minimum deviation is important in various fields such as optics, astronomy, and geology. It can help in the design and construction of optical instruments, determining the refractive index of materials, and studying the properties of minerals and crystals.

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