A query on the (old) motivation for renormalizable theories

In summary: That's awesome - thanks very much. I had a suspicion that was the case but have only ever worked at such a miniscule order I wasn't sure if it was the case. Thanks mate!
  • #1
metroplex021
151
0
I've just realized I don't understand something pretty fundamental about the need to renormalize. Popular wisdom has it (or had it - forget the shift towards an effective framework) that theories that were not renormalizable had no predictive power, on account of the fact each n-point vertex function in such theories need to be renormalized anew, requiring new parameters to be measured at each n (see, e.g., Maggiore p139).

But can't one say the following: say I am interested in studying only 2->2 interactions. Then presumably I only need to renormalize the 2, 3 and 4-point functions in order to derive predictions for these sorts of interactions. The infinitely many parameters apparently needed for a renormalizable theory (and once again, forget about EFTs) would only arise in the case that we study n->m particle relations in the limit that n & m go to infinity, which we never do. So why *were* renormalizable theories regarded as non-predictive?

Any help much appreciated!
 
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  • #2
hi metroplex021! :smile:
metroplex021 said:
… But can't one say the following: say I am interested in studying only 2->2 interactions. Then presumably I only need to renormalize the 2, 3 and 4-point functions in order to derive predictions for these sorts of interactions. …

no, the number of input and output particles is irrelevant

even with 2->2, there are infinitely many terms in the Dyson expansion …

forget Feynman diagrams, it's those infinitely many terms that need to have a finite sum :wink:
 
  • #3
tiny-tim said:
hi metroplex021! :smile:


no, the number of input and output particles is irrelevant

even with 2->2, there are infinitely many terms in the Dyson expansion …

forget Feynman diagrams, it's those infinitely many terms that need to have a finite sum :wink:

Well, I think that's how I was thinking before, but isn't the Dyson series written out for a single n-point function? (So that if you were ignoring interactions with less than n external legs, you wouldn't need to worry about that series - or indeed any series for any n'-point vertex function with n'>n?) Thanks!
 
  • #4
hi metroplex021! :smile:
metroplex021 said:
… isn't the Dyson series written out for a single n-point function? (So that if you were ignoring interactions with less than n external legs, you wouldn't need to worry about that series - or indeed any series for any n'-point vertex function with n'>n?) Thanks!

sorry, I'm not understanding your terminology :redface:

by "external legs", i assume you mean eg 2->2 has 4 external legs?

but what do you mean by an "n-point function"? :confused:
 
  • #5
An n-point vertex function is a sum of one-particle-irreducible diagrams with external propagators removed. (There is also a nonperturbative definition in terms of Legendre transforms of the functional integral with a source, but I don't remember it precisely enough to quote it.)

For 2-2 scattering, you need the 4-point vertex function. But when you compute it, at high enough orders, you will have sub-diagrams that involve n-point vertices for arbitrarily high n. And these have to be renormalized, so you will need the corresponding parameters.
 
  • #6
That's awesome - thanks very much. I had a suspicion that was the case but have only ever worked at such a miniscule order I wasn't sure if it was the case. Thanks mate!

Avodyne said:
An n-point vertex function is a sum of one-particle-irreducible diagrams with external propagators removed. (There is also a nonperturbative definition in terms of Legendre transforms of the functional integral with a source, but I don't remember it precisely enough to quote it.)

For 2-2 scattering, you need the 4-point vertex function. But when you compute it, at high enough orders, you will have sub-diagrams that involve n-point vertices for arbitrarily high n. And these have to be renormalized, so you will need the corresponding parameters.
 

1. What is a renormalizable theory?

A renormalizable theory is a type of physical theory that can be used to describe and predict the behavior of a system at different energy scales. These theories are important because they allow us to make accurate predictions about the behavior of particles and forces at very small scales, such as those found in subatomic particles.

2. Why is the motivation for renormalizable theories important?

The motivation for renormalizable theories is important because it helps us understand the fundamental principles that govern the behavior of the universe at a microscopic level. By studying these principles, we can gain a deeper understanding of the laws of nature and make more accurate predictions about the behavior of physical systems.

3. What was the old motivation for renormalizable theories?

The old motivation for renormalizable theories was to reconcile the discrepancies between classical physics and quantum mechanics. It was believed that by creating a theory that could accurately describe both large-scale and small-scale phenomena, we could have a more complete understanding of the universe.

4. How do renormalizable theories differ from non-renormalizable theories?

Renormalizable theories are able to make accurate predictions at all energy scales, while non-renormalizable theories break down at high energies. This is because renormalizable theories are able to absorb and account for infinite quantities that arise in calculations, while non-renormalizable theories cannot.

5. What are some examples of renormalizable theories?

Some examples of renormalizable theories include the Standard Model of particle physics, which describes the behavior of subatomic particles and their interactions, and General Relativity, which describes the behavior of gravity. These theories have been extensively tested and have been shown to accurately predict the behavior of physical systems at different energy scales.

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