Proving that two variables are canonical

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In summary, the polar variables rho and phi, with their corresponding momenta p_rho and p_phi, are canonical as they satisfy the Poisson bracket conditions for canonical variables. The bracket is given by {rho, phi} = (d rho/dx * d phi/dy - d rho/dy * d phi/dx) + (d rho/dy * d phi/dx - d rho/dx * d phi/dy).
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quasar_4
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Homework Statement



Show that the polar variables [tex] \rho = \sqrt{x^2+y^2} [/tex] and [tex] \phi = tan^{-1}(y/x) [/tex], with [tex] p_{rho} = \frac{x p_x +yp_y}{\rho} [/tex] and [tex] p_{phi} = xp_y-yp_x [/tex], are canonical.

Homework Equations



To be canonical, a set of variables must satisfy {qi,qj}={pi,pj} = 0 and {qi, pj} = dij (the Kronecker delta)

The Attempt at a Solution



I'm just a wee bit confused about the brackets here. The thing is, the way I've learned Poisson brackets is that given functions w=w(p,q) and z=z(p,q), the bracket is [tex] \sum_{i} (\frac{dw}{dq_i}\frac{dz}{dp_i} - \frac{dw}{dp_i}\frac{dz}{dq_i}) [/tex]. But this definition gives w=w(p,q) and z=z(p,q). My problem gives me rho = rho (x,y) and phi =phi(x,y). But these are both positions. So, doesn't the bracket have to be

[tex] \{\rho,\phi\} = (\frac{d\rho}{dx}\frac{d\phi}{dy} - \frac{d\rho}{dy}\frac{d\phi}{dx}) + (\frac{d\rho}{dy}\frac{d\phi}{dx} - \frac{d\rho}{dx}\frac{d\phi}{dy}) [/tex]? Or am I supposed to have derivatives of rho and phi with respect to px and py, even though the positions aren't explicitly functions of momentum?? help!:yuck:
 
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"Or am I supposed to have derivatives of rho and phi with respect to px and py, even though the positions aren't explicitly functions of momentum??"

Dead right - in a way they are functions of momentum, just trivial functions. In the same way f(x)=1 is a function of x. You just want to follow your prescription exactly with your phase variables [tex]q_1=x, q_2=y, p_1=p_x,p_2=p_y[/tex]. See if you get the right numbers out.
 

1. What does it mean for two variables to be canonical?

When two variables are canonical, it means that they are highly correlated and have a linear relationship. This means that changes in one variable will result in predictable changes in the other variable.

2. How do you prove that two variables are canonical?

To prove that two variables are canonical, you can use a statistical measure called canonical correlation. This measures the degree of correlation between two sets of variables and determines if they are significantly associated.

3. What are the benefits of proving that two variables are canonical?

Proving that two variables are canonical can provide valuable insights into the relationship between the variables. It can also help in predicting one variable based on the other and identifying the most important variables in a dataset.

4. Can two variables be canonical in one dataset but not in another?

Yes, the canonical relationship between two variables can vary depending on the dataset. It is important to analyze each dataset separately to determine if the variables are canonical or not.

5. Are there any limitations to proving that two variables are canonical?

Yes, there are some limitations to proving that two variables are canonical. For example, it assumes that both variables have a linear relationship, and it may not accurately capture non-linear relationships. Additionally, it may not account for other factors that could influence the relationship between the variables.

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