Understanding the Klein Gordon Lagrangian and Calculation Rules with Gradients

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In summary, the conversation is about the Klein Gordon Lagrangian and the factor of 2 that appears when differentiating by \partial_{\mu}\Phi. The factor comes from the product rule and is necessary for the Euler-Lagrange equation to produce the Klein Gordon equation. The factor also appears in the covariant picture and can be understood by considering \partial^{\mu}\varphi and \partial_{\mu}\varphi as not independent.
  • #1
flix
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ok, quick and dirty and stupid question about calculation rules with 4 gradients:


consider the Klein Gordon Lagrangian [tex]L_{KG} = \frac{1}{2} \partial_{\mu}\Phi\partial^{\mu} \Phi - \frac{1}{2} m^2 \Phi^2 [/tex].

Why is

[tex] \partial_{\mu} \left( \frac{\partial L_{KG} }{\partial(\partial_{\mu} \Phi)} \right) = \partial_{\mu}\partial^{\mu} \Phi[/tex]

Where does the factor 2 come from that cancels out the 1/2 ?
 
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  • #2
have you taken the lagrangian and 4gradient from same source?

I have always written KG lagrangian (density) as: [tex]L_{KG} = (\partial_{\mu}\Phi) ^{\dagger}\partial^{\mu} \Phi - m^2 |\Phi |^2 [/tex]

Then the 4gradient is the one you have written.
 
  • #3
same source.

the factors 1/2 are there throughout, and it certainly makes sense for the mass term where a factor 2 comes from differentiating.

But where does the factor 2 come from when differentiating by [tex] \partial_{\mu} \Phi[/tex] ?? Probably I miss out a very simple thing...
 
  • #4
flix said:
But where does the factor 2 come from when differentiating by [tex] \partial_{\mu} \Phi[/tex] ?? Probably I miss out a very simple thing...
I can't see where it comes from either, but then I often miss basic things.

Is there some reason you feel the 2 should be there?
 
  • #5
well yes, since applying the Euler Lagrange equation on the KG Lagrangian should produce the KG equation:

EL: [tex] \frac{\partial L}{\partial \Phi} - \partial_{\mu} \left( \frac{\partial L}{\partial(\partial_{\mu} \Phi} \right) = 0 [/tex]

KG equation: [tex] (\square + m^2) \Phi(x, t) = 0 [/tex]
 
  • #6
Maybe I'm overlooking something, but as far as I can see the factor 2 comes from the product rule. It gives you 2 delta functions.
 
  • #7
Ok, I see. Well, as I said above, I always miss obvious things: note that [itex]\partial^{\mu}\varphi[/itex] and [itex]\partial_{\mu}\varphi[/itex] are not independent, thus your derivative will include two terms. We can rewrite the Lagrangian as [tex]\mathcal{L}=\frac{1}{2}g^{\mu\nu}\partial_{\mu}\varphi\partial_{\nu}\varphi-\frac{1}{2}m^2\varphi^2[/tex]. Differentiating wrt [itex]\partial_{\mu}\varphi[/itex] then yields [tex]\frac{1}{2}\left[\partial_{\nu}\varphi g^{\mu\nu}+\delta_{\mu\nu}\partial_{\mu}\varphi g^{\mu\nu}\left]=\frac{1}{2}\left[2\partial^{\mu}\varphi\left][/tex], which yields the result.

Does that make sense?

Edit: Looks like I was beaten to it!
 
  • #8
Thank you so much!

I never really liked the covariant picture, although it looks very elegant. It always leads to me missing out basic things.
I really have to dig into it now...
 

1. What is a gradient in science?

A gradient in science refers to a gradual change in a physical or chemical quantity across a distance. It can be represented as a slope or a rate of change.

2. How is a gradient calculated?

A gradient is calculated by dividing the change in the quantity (y) by the distance (x) over which the change occurs. This is also known as the slope formula: gradient = (y2-y1)/(x2-x1).

3. What is the significance of gradients in scientific research?

Gradients play a crucial role in understanding and analyzing various natural phenomena, such as temperature changes, growth rates, and chemical reactions. They also help in identifying patterns and making predictions.

4. Can gradients have different directions?

Yes, gradients can have different directions depending on the direction of the change in the quantity. For example, a positive gradient indicates an increase in the quantity, while a negative gradient indicates a decrease.

5. How are gradients used in practical applications?

Gradients are used in many practical applications, including engineering, environmental science, and medicine. For example, they are used to design efficient heating and cooling systems, monitor pollution levels, and analyze blood flow in the human body.

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