Least squares method for cloud of atom

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Homework Help Overview

The problem involves analyzing the expansion of a cold cloud of 87 Rb atoms after turning off a magnetic-optic pit. The relationship governing the size of the cloud over time is to be explored using the least squares method to determine the temperature and initial size of the cloud.

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss transforming the given formula into a linear form suitable for regression analysis. There are questions about how to handle the Boltzmann constant and mass in the calculations. Some participants clarify the relationship between the constants and the variables involved in the least squares method.

Discussion Status

Participants are actively engaging with the problem, sharing insights on the least squares method and its application. There is a focus on understanding how to validate the results obtained from the method, with suggestions to plot the data for visual confirmation of the fit.

Contextual Notes

Some participants express concerns about the accuracy and acceptability of the results, particularly in relation to upcoming assessments. There is mention of using tools like Excel or manual methods for plotting data points and fitting lines.

diracdelta
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Homework Statement


After turnig of magnetic-optic pit, cold cloud of atom 87 Rb is expanding. Size of cloud after time t, is given with relation:
ma_%7B0%7D%5E%7B2%7D%20+%20%5Cfrac%7Bk_%7BB%7D%5Ccdot%20T%7D%7Bm%7D%5Ccdot%20t%5E%7B2%7D%7D.gif

where, k_B is Boltzmann constant, m mass of 87 Rb.
Draw a plot, then use least squares method to find temperature T, and initial size of cloud sigma_0, for next results of measurement.
t(ms)
sigma(mm)

The Attempt at a Solution


I am familiar with least squares method. What I don't understand in this problem is next.
First of i must get this formulae to some linear shape ( regrression).
So, ok, firstly we square the whole equation.
I get next.

ma_%7B0%7D%5E%7B2%7D%20+%20%5Cfrac%7Bk_%7BB%7D%5Ccdot%20T%7D%7Bm%7D%5Ccdot%20t%5E%7B2%7D%7D.gif


now, on to assigning values to get form like y=ax+b, its obvius,
y= sigma^2
b=sigma_0^2
x=t^2
x= (k_B * T)/m

and now, to use least square methd, i must calculate, x,y,xy,x^2,y^2, and sum of them all.
my question here is next:

This member Boltzmann constant/m. What should i do with it?
Shuld i calculate it and continue to multiply numbers with it whole process?
Or just write results like 0.157746 * ( K-b/m) and in the end multiply a with it?
 
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If you are given a series of data points you can use the squares method. You will find the constants that define your line: a and b. Since ##a = \frac {k_b T }{m} ## you can simply get T out of it, using the known values for the Boltzmann constant and the mass.
 
You mean ##a=\frac{k_B T}{m}##, right?
I'm not sure what your question is. The least squares method will give you a value for a, which you can then multiply by ##\frac m{k_B}## to get T. What else would you do?
 
Ok. I see that. THank you for answer.
What bothers me is how to know when that solution is good? I.e. i get a=0.01785
How do i recognize it is acceptable, and it fits the formulae.
 
Try plotting the values (in Excel, Matlab or whatever) and see if the slope of the line agrees with what you got.
 
diracdelta said:
Ok. I see that. THank you for answer.
What bothers me is how to know when that solution is good? I.e. i get a=0.01785
How do i recognize it is acceptable, and it fits the formulae.
That I can't help with. Does it give a reasonable T?
 
OK, I will do it in excel now.
Reason why I'm asking you this is because i have midterm where i must know least square method, and i want to know if i do it right there.
Because using only calculator and milimeter paper..
THanks for reply guys. I really appreciate it.
 
If you have the millimeter paper you can still draw your line and see how much it "fits" the existing points. It won't pass over every one of them, because there's always a certain error, but the point of this method is that this error (or rather, its square) will be minimal (hence "least squares")
 

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