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Heisenberg Uncertainty Principle |
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| Aug29-06, 11:48 AM | #1 |
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Heisenberg Uncertainty Principle
I was reading about the derivation of the Heisenberg Uncertainty Principle and how Heisenberg used Gaussian Distributions to represent the uncertainty of position and momentum in his calculation. Why is it that Gaussian Distributions were used? There are many different types of distributions out there, why this kind in particular?
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| Aug30-06, 06:09 AM | #2 |
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The Gaussian is easy to do mathematically, and it turns out that the FT of a Gaussian is a Gaussian. It also turns out that the Gaussian has the minimum product of dxdp (as usually defined). For this reason, the HUP is stated as an inequality. dxdp=hbar/2 only for the Gaussian.
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| Aug30-06, 07:31 AM | #3 |
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| Aug30-06, 09:22 AM | #4 |
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Heisenberg Uncertainty Principle
It's better (in my opinion) to show that for any two operators which don't commute, there exists a corresponding uncertainty principle in the pair of observables those operators represent. In this case, you don't need to worry about specifics, as the result is fairly general.
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| Aug30-06, 11:07 AM | #5 |
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deltaX x deltaP = h-bar/2 Where does the 2 come from and why? |
| Aug30-06, 12:43 PM | #6 |
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The 2 comes from the FT of a Gaussian.
Do it yourself. The math is fairlyl simple. The HUP is usually written as "greater than or equal". H picked G for the two reasons I gave. Given any spatially confined wave function, dxdp (suitably defined)can be calculated by FT. If it is not Gaussian, dxdp will be greater than hbar/2. I'm outtta here now. |
| Aug30-06, 12:47 PM | #7 |
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You have basically answered my question by saying "because the math works out." I've seen the derivation (Why do you think I'm asking the question?. I'll see you guys in the literature, this forum is a joke.
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| Aug30-06, 01:28 PM | #8 |
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No, it is not equal. The general uncertainty principle for any two hermitian operators [tex] \hat A[/tex], [tex]\hat B[/tex] is [tex]\Delta A \Delta B \ge \frac{i}{2}<[\hat A, \hat B]>[/tex] This is a provable fact for any two hermitian operators in hilbert space, regardless of the wave functions (so long as again, the wave functions are in hilbert space). You do not have to make any assumptions about the wave functions (except, that again, they are in hilbert space). see: http://galileo.phys.virginia.edu/cla...tPrinciple.htm In deriving the general uncertainty principle, no assumptions are made about the wave functions. There is no physical reason to have used the gaussian distribution in initially finding the uncertainty principle, its just the easiest to work with, and happens to be the distribution thatg gives the minimal uncertainty. |
| Aug30-06, 01:40 PM | #9 |
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Heisenberg did not just use any arbitrary number to describe the minimum standard deviation between position and momentum of a particle. Heisenberg knew that particles behaved like waves and he knew that the energy of any wave is the frequency multiplied by Planck's constant. In a wave, a cycle is defined by the return from a certain position to the same position such as from the top of one crest to the next crest. This actually is equivalent to a circle of 360 degrees, or 2π radians. Therefore, dividing h by 2π describes a constant that when multiplied by the frequency of a wave gives the energy of one radian. Heisenberg took ½ of as his standard deviation. This can be written as over 2 as above or it can be written as h/(4π). Normally one will see over 2 as this is simpler. Two years earlier in 1925 when Heisenberg had developed his matrix mechanics the difference in position and momentum were already showing up in the formula. In developing matrix mechanics Heisenberg was measuring amplitudes of position and momentum of particles such as the electron that have a period of 2π, like a cycle in a wave, which are called Fourier series variables. When amplitudes of position and momentum are measured and multiplied together, they give intensity. However, Heisenberg found that when the position and momentum were multiplied together in that respective order or in the reverse order, there was a difference between the two calculated intensities of h/(2π). In other words, the two quantities position and momentum did not commute. In 1927, to develop the standard deviation for the uncertainty principle, Heisenberg took the gaussian distribution or bell curve for the imprecision in the measurement of the position q of a moving electron to the corresponding bell curve of the measured momentum p. Please note that last sentence that says Heisenberg took the gaussian distribution or bell curve for the imprecision in the measurement of the position q of a moving electron...... My question here is why would he do that. Is there a physical reason to expect a gaussian distribution? Thats all I want to know. I'm not some quack trying to rewrite physics, I'm just curious about the history. I find this forum condescending and insulting. I'm doing research and publishing. You can use my name and look it up (Roger H Pink)(Roger Pink). I understand that the fourier transform of a gaussian is a gaussian. I understand that fourier transforms can be used to derive the uncertainty relation. Neither of these facts tells me the physical reason behind the choice. |
| Aug30-06, 01:45 PM | #10 |
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Well joke or not, QM is a very serious subject. For more details of what I'm talking about, see Shankar, Principles of QM, pgs. 237-239.
Two operators that don't commute have a minimum uncertainty, and the product of the uncertainties in those pair of observables is at least [itex]\hbar/2.[/itex] Note that this has nothing to do with Gaussians. It even gives you the kind of state which will have the minimum value in equations (9.2.15), and it still doesn't specify that they must be Gaussians. EDIT: I started typing this (then took a long break) before franznietzsche's post. |
| Aug30-06, 01:51 PM | #11 |
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| Aug30-06, 02:00 PM | #12 |
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I'm very good at Physics and I certainly don't need people who don't understand my question insulting me. This isn't a Math forum so its reasonable to ask for the physical meaning of mathematical choices. I was just hopeful that on a physics forum there might be someone who knew the history behind Heisenberg's derivation. Instead I got a bunch of guys yelling at me about basic quantum mechanics. |
| Aug30-06, 02:33 PM | #13 |
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Careful |
| Aug30-06, 02:43 PM | #14 |
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"The Central Limit Theorem which states that if the sum of the variables has a finite variance, then it will be approximately normally distributed." But of course we are talking about a product, not a sum, so I'm not sure. Still, at least your answer: a) doesn't assume I don't know basic quantum mechanics b) doesn't assume I don't know math So thanks for that. |
| Aug30-06, 02:46 PM | #15 |
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So I read some more and found the following:
"The central limit theorem tells us what to expect about the sum of independent random variables, but what about the product? Well, the logarithm of a product is simply the sum of the logs of the factors, so the log of a product of random variables tends to have a normal distribution, which makes the product itself have a log-normal distribution. Many physical quantities (especially mass or length, which are a matter of scale and cannot be negative) are the product of different random factors, so they follow a log-normal distribution." According to this, wouldn't he have used a log-normal distribution instead of a gaussian distribution? Does it make a difference in terms of standard deviation? |
| Aug30-06, 02:49 PM | #16 |
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ps. I'd eat my hat and coat if it had anything to do with the central limit theorem: that says that randomly distributed variables tend to the normal distribution as [itex]N \rightarrow \infty[/itex]; why a wavefunction should be that is arbitrary. |
| Aug30-06, 02:52 PM | #17 |
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As has been said, the uncertainty between two non-commuting operators is not equal to h-bar/2, but is strictly greater than or equal to h-bar/2. The Gaussian distribution is the "best" in this regard, because it achieves this minimum uncertainty. You are free to carry on using any other kind of distribution you want, but you will not achieve this minimal uncertainty with anything but the Gaussian.
That's the reason it's commonly used -- it achieves the minimum uncertainty. That's all. - Warren |
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