advanced27 min studyLesson 665 in Physics

Trade the source for the mean field

State convexity and inverse-response assumptions before identifying irreducible vertices.

On this page

What you’ll understand

  • Construct the finite Euclidean Legendre transform.
  • Relate its Hessian to the inverse connected covariance.
  • Distinguish exact effective action from a truncated or saddle approximation.

A source describes how a system is driven; the mean field describes how it responds. The effective action trades the first description for the second through a Legendre transform. Locally this exchange requires an invertible source-response map, so convexity and zero modes are part of the construction. In perturbative field theory the resulting functional generates one-particle-irreducible vertices. It is not obtained merely by taking a logarithm, and it is not generally the original classical action with a renamed argument.

Define the Euclidean transform first

For a finite positive Euclidean measure, use W(J)=log Z normalized at zero source and ℏ=1. Define the mean φ as the derivative of W. Where its covariance Hessian is positive definite, solve this relation for J as a function of φ. Define Γ as J dotted into φ minus W. Differentiating Γ cancels the terms containing the derivative of J, leaving its first derivative equal to J in this Euclidean sign convention.

A global Legendre-Fenchel transform takes a supremum over sources and remains meaningful when an ordinary inverse is unavailable. It need not be differentiable everywhere. Bounded variables restrict the attainable mean-field range, while thermodynamic limits can produce flat portions or nondifferentiable behavior. At finite volume a positive measure often smooths phenomena that become sharp only after taking an infinite-volume limit. The choice and order of these limits matter when interpreting spontaneous symmetry breaking.

ϕ=∇JWE(J),ΓE(ϕ)=sup⁡J{J⋅ϕ−WE(J)},∇ϕΓE=J\phi=\nabla_JW_E(J),\qquad \Gamma_E(\phi)=\sup_J\{J\cdot\phi-W_E(J)\},\qquad \nabla_\phi\Gamma_E=J
This is the dimensionless Euclidean convention with ℏ=1. The last derivative identity requires an attained differentiable transform and a locally invertible source-response map.↔ Scroll wide equations horizontally.

Invert response rather than a raw moment

Differentiate the relation between the source and mean once more. The Hessian of Γ is the matrix inverse of the Hessian of W, hence the inverse connected covariance in the declared units. In a field regulator this is an inverse kernel with an explicit integration measure and boundary conditions. A raw second moment containing a mean-squared contribution is not the object to invert. Null response directions require restriction or a more careful transform, just as quadratic zero modes did earlier.

Diagrammatically the transform removes the reducible chains that connect irreducible pieces through a single full propagator. Derivatives of Γ supply the proper vertices; connected correlations are reconstructed by joining these vertices in trees with full propagators. The exact Γ already includes loop information. Integrating it again as though it were a fresh bare action and keeping all loops would double count that information rather than reproduce the original quantum theory.

ΓE(2)=(WE(2))−1=CJ−1,CJ,ij=⟨xixj⟩J−ϕiϕj\Gamma_E^{(2)}=\big(W_E^{(2)}\big)^{-1}=C_J^{-1},\qquad C_{J,ij}=\langle x_ix_j\rangle_J-\phi_i\phi_j
All matrices refer to the same finite regulator and source point. In continuum notation the inverse means a convolution inverse on the stated domain, not elementwise reciprocals.↔ Scroll wide equations horizontally.

Keep Minkowski signs and approximations separate

For the real-time convention ZM=exp(iWM) with source plus Jφ, the commonly used effective action is ΓM=WM−∫Jφ. Its derivative is minus J. This sign differs from the positive Euclidean Legendre transform above, and the second derivative of WM itself contains a factor i times the connected correlator. Copying only the name Γ between conventions does not preserve these derivative formulas. Define the convention whenever comparing references.

The optimization lab illustrates a local iterative issue rather than constructing Γ. Reset and set step size to 0.5 and initial position to 1, then Step once; predict position 0.5. Reset and set step size to 2.5 and initial position to 1, then Step once; predict −1.5, an increased magnitude. The objective is fixed at x²/2, so these runs test step stability. They do not invert a covariance, evaluate a Legendre transform, or establish convergence of a quantum effective-action truncation.

WE(J)=J22a⟹ϕ=Ja,ΓE(ϕ)=a2ϕ2,ΓE′′=a(a>0)W_E(J)=\frac{J^2}{2a}\quad\Longrightarrow\quad \phi=\frac Ja,\quad \Gamma_E(\phi)=\frac a2\phi^2,\quad \Gamma_E''=a\qquad(a>0)
This exact finite Gaussian example has W normalized so W(0)=0. The omitted additive constant has no effect on response or proper vertices, but absolute vacuum energies require their own normalization.↔ Scroll wide equations horizontally.

Invert a Gaussian response

Let W(J)=J²/8 in dimensionless Euclidean units. For mean φ=1/2 find the source, effective action, connected variance, and curvature of Γ.

  1. The mean is W′=J/4, so the source producing φ=1/2 is J=2. This inversion is unique because W″=1/4 is positive.

  2. At this source W=4/8=1/2 and Jφ=1. Their difference gives Γ=1/2, also obtained from Γ(φ)=2φ².

  3. The connected variance is W″=1/4. The effective-action curvature is Γ″=4, its inverse, not the reciprocal of the full second moment 1/4+(1/2)².

The result

The required source is 2, Γ(1/2)=1/2, covariance 1/4, and effective-action curvature 4.

Explore the model

Interactive lab

Interactive lab · optimization

Separate curvature from step choice

Reset and set η=0.5,x₀=1, then Step once; predict x₁=0.5. Reset and set η=2.5,x₀=1, then Step once; predict x₁=−1.5.

Separate curvature from step choiceGradient descent on f(x) = x²/2: xₙ = (1 − η)ⁿx₀. Playback advances two discrete iterations per second. Values outside |x| ≤ 4 are explicitly reported. Current readings: Position x0: 2; Objective x²/2: 2; Iteration behavior: Stable step. All parameters are adjustable with the labeled controls below.−4−202402.174.356.528.7Parameter xf(x) = x²/2n = 0
ObjectiveDiscrete iterate
Position x0
2
Objective x²/2
2
Iteration behavior
Stable step
n = 0 · t = 0 s

Gradient descent on f(x) = x²/2: xₙ = (1 − η)ⁿx₀. Playback advances two discrete iterations per second. Values outside |x| ≤ 4 are explicitly reported. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.

Look forThe fixed quadratic descent illustrates stability of an iteration only. It does not compute a Legendre transform or an effective action.

Check your understanding

In the declared Euclidean convention, which Hessian is inverted to obtain Γ’s Hessian?

Try it yourself

For W(J)=log cosh J, find the source producing φ=1/2, the value of Γ there, and Γ″ there. State the allowed range of finite-source means.

Take a moment to make a prediction or work it out on paper. Open a hint when you need one.

Give me a hint

Use φ=tanh J and W″=1−tanh²J.

Compare with the solution

The source is J=atanh(1/2)=(ln3)/2. Then Γ=J/2−log cosh J=(3/4)ln3−ln2≈0.1308. Its curvature is 1/(1−φ²)=4/3. Finite real sources give −1<φ<1; the endpoint means require source limits. An unconstrained inversion over arbitrary real φ would therefore be invalid.

Take it with you

  • A Legendre transform exchanges source and response under declared convexity assumptions.
  • Proper two-point vertices invert connected covariance kernels.
  • Euclidean and Minkowski effective-action signs must be translated consistently.

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