Undergrad What is the width of a wave packet?

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The discussion focuses on the concept of wave packets and their associated widths as introduced in Gasiorowicz's Quantum Physics. The term "width" is clarified to mean "full width at 1/e times maximum," which differs from the full width at half maximum (FWHM). The relationship between the width and the standard deviation (σ) of a Gaussian distribution is emphasized, noting that the standard deviation represents a half-width. A factor of 2 is necessary to convert the standard deviation to a full width for comparison. The conversation highlights the importance of understanding these definitions in the context of quantum physics.
RicardoMP
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I'm reading Gasiorowicz's Quantum Physics and at the beggining of chapter 2, SG introduces the concept of "wave packet" and gaussian functions associated to them. The first attached image is the 28th page of the book's 1st edition I suppose, and my question is about the paragraph inside the red box. I suppose f(x) is a normal distribution. What does SG means by the function's width? Is it its FWHM? If it were, wouldn't the width be 2\sqrt{2ln(2)}\sigma? Or if not, why is the width of order 2\sqrt{2}, since the function at x=\pm 2\sqrt{2} falls to \frac{1}{e} of its peak value?
The second attached image is the "same page" from the 3rd edition (which I found harder to understand) and another explanation (green box). How can I conclude that, since the "square falls 1/3 of its peak value when \alpha(k-k_0)^2=1, \Delta k = \frac{2}{\sqrt{\alpha}}? Is this reffering to the standard deviation \sigma in the normal distribution (third attached image)?
I hope I'm not missing something obvious.
Thank you for your time!
 

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It says in the red box it means "full width, 1/e times maximum"
 
MisterX said:
It says in the red box it means "full width, 1/e times maximum"
And is there a reason for using "full width, 1/e times maximum" instead of FWHM?
 
For a Gaussian distribution, this gives you a value equal to the standard deviation of the distribution, which is actually the more fundamental definition. $$\Delta x = \sigma_x = \sqrt {\langle x^2 \rangle - {\langle x \rangle}^2}$$ Actually, the standard deviation is a half-width (think ##\mu \pm \sigma##), so if you want to compare it to a full-width, you need a factor of 2.
 
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