Constrained massless Gaussian laws [fdip-003V]
✍️sourceAGENTDRAFTED
- October 30, 2026
Constrained massless Gaussian laws [fdip-003V]
✍️sourceAGENTDRAFTED
- October 30, 2026
Removing the mass from the four-cycle action leaves constant fields unpenalized, so the full-space integral diverges. Restricting to mean-zero fields removes this direction, but requires the subspace's own volume. The zero-mass cycle calculation gives the resulting normalizer and a finite variance for the difference of opposite field values.
The positive Gaussian law on the orthogonal complement of the kernel yields this constrained probability and its pseudoinverse covariance. Kernel factorization and projected limits distinguish the divergent full normalizer from finite difference observables. Pinning a vertex gives another constraint, with a generally different coordinate covariance; the two-vertex comparison makes the distinction explicit. These choices also matter when eliminating singular directions.
Definition 1. Kernel complement and pseudodeterminant [fdip-0031]AGENTDRAFTED
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Definition 1. Kernel complement and pseudodeterminant [fdip-0031]AGENTDRAFTED
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Let \(L:E\to E\) be a self-adjoint positive semidefinite operator on a finite real inner-product space \((E,h)\). Set \[\begin {aligned} K&=\ker L,& H&=K^\perp ,\\ k&=\dim K,& r&=\dim H. \end {aligned}\] Write \(i:H\to E\) for inclusion, \(p_H:E\to H\) for orthogonal projection, \(P_H=ip_H\) and \(P_K=I-P_H\) for the endomorphism projections.
The spectral theorem gives \(E=K\mathbin \perp H\). The positive-eigenvalue vectors span \(H\), so \(L(H)\subset H\) and \(L_H=L|_H:H\to H\) is positive definite. Define the pseudodeterminant by \[\operatorname {pdet}L=\det L_H =\prod _{\lambda _a>0}\lambda _a.\] Its empty product is one, including \(L=0\). These definitions use the specified pairing and its induced subspace volume.
For a weighted incidence Laplacian with positive real edge weights, the kernel theorem identifies \(K\) with fields constant on each connected component. Indeed its energy is a sum of positive weights times squared edge differences; zero energy means every difference is zero, and propagation along undirected paths gives those constants. Loops contribute zero and isolated vertices are components. Thus \(H\) imposes zero sum separately on each component. For a connected nonempty graph, \(n=|V|\) gives the mean-zero field space \[\begin {aligned} u_0&=\mathbf 1_V/\sqrt n,\\ H&=\{\phi :\sum _v\phi (v)=0\},\\ P_H\phi &=\phi -\frac {\sum _v\phi (v)}{n}\mathbf 1_V. \end {aligned}\] The empty graph has \(E=K=H=0\); no division by \(n\) is used there.
The kernel complement and determinant product are direct consequences of the spectral decomposition in [boyd2004convex, Section A.5.2, pp. 646-647]; the graph kernel uses the incidence-energy identity proved in Theorem [fdip-0008].
The massless four-cycle calculation makes this kernel complement and pseudodeterminant explicit.
Proposition 2. The Laplacian Moore-Penrose inverse [fdip-0032]AGENTDRAFTED
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Proposition 2. The Laplacian Moore-Penrose inverse [fdip-0032]AGENTDRAFTED
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With the kernel decomposition, define the Laplacian pseudoinverse by \[L^+=iL_H^{-1}p_H.\] This is the unique endomorphism \(A\) satisfying the 4 Moore-Penrose identities: \[\begin {aligned} LAL&=L,& ALA&=A,\\ (LA)^\dagger &=LA,&(AL)^\dagger &=AL. \end {aligned}\] It vanishes on \(K\), is the inverse of \(L_H\) on \(H\), and has spectral expression \(L^+=\sum _{\lambda _a>0}\lambda _a^{-1}P_{u_a}\), where \(P_u x=h(u,x)u\) for a unit eigenvector.
Proof.
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Proof.
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On the orthogonal decomposition \(K\mathbin \perp H\), the operators \(L,L^+\) have blocks \((0,L_H)\) and \((0,L_H^{-1})\). Hence \[LL^+=L^+L=P_H.\] The inverse on \(H\) is self-adjoint by the inverse-response lemma. Thus \(L^+\) is self-adjoint, both products are self-adjoint, and multiplication once more gives \(LL^+L=L\) and \(L^+LL^+=L^+\).
Suppose \(A\) satisfies the 4 identities. Then \(LA\) is self-adjoint and idempotent, since \(LALA=LA\). Its range lies in \(\operatorname {ran}L=H\), and \(LAL=L\) shows it acts as the identity on \(H\). Thus it is the orthogonal projection \(P_H\). Similarly \(AL\) is self-adjoint and idempotent. Its kernel is \(K\): \(Lx=0\) implies \(ALx=0\), while \(ALx=0\) implies \(Lx=LALx=0\). A self-adjoint idempotent is the orthogonal projection onto its kernel complement, so \(AL=P_H\). For \(y=Lx\in H\), \[Ay=ALx=P_Hx=L_H^{-1}y.\] Finally \(A=ALA=A(LA)=AP_H\), so \(A\) vanishes on \(K\). These two restrictions force \(A=L^+\). The argument includes \(H=0\) and \(E=0\); the unique map on the zero space is its own inverse.
TauCeti's LinearMap.IsMoorePenroseInverse
uses exactly these 4 conditions for real inner-product spaces.
Its LinearMap.IsMoorePenroseInverse.unique
supports uniqueness, and the finite-dimensional characterization
LinearMap.isMoorePenroseInverse_iff_eq_moorePenroseInverse
identifies this construction with \(L.\mathrm {moorePenroseInverse}\).
The weaker inner-inverse identity \(LAL=L\) alone gives neither this
uniqueness nor a constrained probability law.
The construction is a direct specialization of the orthogonal spectral decomposition in [boyd2004convex, Section A.5.2, pp. 646-647]. The four-identity and uniqueness arguments above establish the additional Moore-Penrose conclusion; no Gaussian law follows from that conclusion alone.
Proposition 3. Massless fields with a kernel-orthogonal constraint [fdip-000C]AGENTDRAFTED
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Proposition 3. Massless fields with a kernel-orthogonal constraint [fdip-000C]AGENTDRAFTED
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Let \(L\) be self-adjoint positive semidefinite on a finite real inner-product space \((E,h)\), with the kernel decomposition \(K=\ker L\), \(H=K^\perp \), \(k=\dim K\), \(r=\dim H\). The induced volume \(\lambda _H\) gives the centered probability law \[\begin {aligned} Z_H&=\frac {(2\pi )^{r/2}}{\sqrt {\operatorname {pdet}L}},\\ d\nu _H(x)&=Z_H^{-1}e^{-h(x,Lx)/2}\,d\lambda _H(x). \end {aligned}\] Its pushforward to \(E\) has covariance \(L^+\). If \(k>0\), the full-space massless integral is infinite, and the restriction of ambient volume to \(H\) is zero; neither replaces this induced measure.
Proof.
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Proof.
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The operator \(L_H\) is positive definite by Definition 1. Apply normalization and inverse covariance on the inner-product space \(H\). The induced measure is independent of its orthonormal chart by isometric volume preservation. For ambient sources \(j,k'\in E\), restriction of their linear observables gives \[\begin {aligned} \operatorname {Cov}_{i_*\nu _H}(\ell _j,\ell _{k'}) &=h_H(p_Hj,L_H^{-1}p_Hk')\\ &=h(j,L^+k'), \end {aligned}\] using the pseudoinverse construction. Thus the ambient covariance is singular when \(K\ne 0\), although its restriction to \(H\) is an inverse.
Suppose \(k>0\). In an orthonormal chart \(H\) is \(\{0\}^k\times \mathbb R^r\). Each of its intersections with a finite box has product volume zero; a countable union of these boxes covers \(H\), proving zero ambient measure. For divergence, take a bounded box of positive induced volume in \(H\). Continuity gives the weight a strictly positive lower bound on its closure. On its product with \([-R,R]^k\subset K\), the weight is unchanged in the kernel direction, so the integral is bounded below by a positive constant times \((2R)^k\). This tends to infinity. When \(H=0\), its singleton has induced volume one, and the same argument applies.
When \(k=0\), \(H=E\) and the full-space law already exists. When \(E=0\), \(k=r=0\), both normalizers are one and the probability is Dirac. When \(L=0\) on a positive-dimensional space, \(H=0\) gives the constrained Dirac law while the full normalizer diverges. All determinant products over zero-dimensional spaces are one.
For a connected nonempty graph with positive edge weights, put \(n=|V|\). Then \(H\) is exactly the mean-zero field space, \(r=n-1\), and the formula becomes \(Z_H=(2\pi )^{(n-1)/2}/\sqrt {\prod _{a=1}^{n-1}\lambda _a}\). At \(n=1\), \(H=0\), \(Z_H=1\), and the full one-dimensional integral still diverges. With \(k>1\) components, a single global sum-zero constraint removes only one dimension of the component-constant kernel: the sum functional is nonzero on that \(k\)-dimensional space, so its kernel has dimension \(k-1\). The restricted integral still diverges by the same kernel-box argument. Componentwise zero sums remove all these modes. The empty graph uses the zero-dimensional case above.
Massive factorization gives the exact divergent factor, while projected limits identify the finite limits of polynomial and difference observables. Vertex pinning uses another constraint and generally another coordinate covariance, as the two-vertex example shows. The distinction between free and fixed-boundary massless models also appears in [friedli2017statistical, Definition 8.1, Remark 8.2 and Exercise 8.2, p. 381]; the induced-volume and finite-graph proofs here specify the present law.
Proposition 4. Massive kernel factorization and its normalizer [fdip-0033]AGENTDRAFTED
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Proposition 4. Massive kernel factorization and its normalizer [fdip-0033]AGENTDRAFTED
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Let \(L\) have the kernel decomposition and let \(m>0\). The positive precision \(Q_m=L+m^2I\) has inverse and normalizer \[\begin {aligned} Q_m^{-1}&=m^{-2}P_K\\ &\quad +i(L_H+m^2I_H)^{-1}p_H,\\ Z_{Q_m}&=(\sqrt {2\pi }/m)^k Z_{H,m},\\ Z_{H,m}&=\frac {(2\pi )^{r/2}} {\sqrt {\prod _{\lambda _a>0}(\lambda _a+m^2)}}. \end {aligned}\] Under the addition isometry \(K\times H\to E\), its probability law is the product of the centered \(K\)-law with precision \(m^2I_K\) and the centered \(H\)-law with precision \(L_H+m^2I_H\). In particular its pushforward by \(p_H\) is exactly the second law.
Proof.
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Proof.
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On \(K\), \(Q_m=m^2I_K\); on \(H\), \(Q_m=L_H+m^2I_H\). Inverting these blocks gives the displayed inverse. The addition isometry preserves product volume, and the action splits: \[\begin {aligned} &\tfrac 12h(z+x,Q_m(z+x))\\ &\quad =\tfrac 12m^2\|z\|^2\\ &\qquad +\tfrac 12h(x,(L_H+m^2I_H)x). \end {aligned}\] Nonnegative Tonelli therefore factors the integral into the two normalizers. Positive normalization makes the first \((\sqrt {2\pi }/m)^k\) and the second the stated spectral product. Division by this product identifies the probability on every measurable rectangle with the product of the two probabilities, hence identifies the measures. Integrating out the \(K\) factor gives the projection law.
Every empty product is one. Thus these formulas hold when \(k=0\), \(r=0\), or \(E=0\). Since each positive eigenvalue stays positive, \[\begin {aligned} Z_{H,m}&\longrightarrow Z_H,\\ m^k Z_{Q_m}&\longrightarrow (2\pi )^{k/2}Z_H. \end {aligned}\] Here \(Z_H\) is the constrained normalizer. For \(k>0\), the full normalizer diverges. This is orthogonal kernel separation; it does not require a general block-elimination theorem.
This product calculation combines the orthogonal decomposition with the positive-covariance density of [friedli2017statistical, Proposition B.55, p. 505] after replacing covariance by inverse precision. The displayed kernel factor is derived here, including zero-dimensional factors.
Proposition 5. Projected Gaussian laws and the massless limit [fdip-0034]AGENTDRAFTED
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Proposition 5. Projected Gaussian laws and the massless limit [fdip-0034]AGENTDRAFTED
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For the kernel decomposition, let \(\nu _m\) be the projection onto \(H\) of the law with precision \(L+m^2I\), \(m>0\). Then \(\nu _m\) converges to the constrained law \(\nu _H\) in total variation, meaning \(\sup _{A\text { Borel}}|\nu _m(A)-\nu _H(A)|\to 0\). For every fixed polynomial \(P\) on \(H\), its expectations converge too.
Proof.
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Proof.
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By kernel factorization, the densities on \(H\) are \(f_m=w_m/Z_{H,m}\), where \[\begin {aligned} w_m(x)&=e^{-h(x,(L_H+m^2I_H)x)/2},\\ w_0(x)&=e^{-h(x,L_Hx)/2}. \end {aligned}\] They are continuous, \(w_m\to w_0\) pointwise, and \(0\leq w_m\leq w_0\), with \(\int _H w_0=Z_H<\infty \). Dominated convergence gives \(Z_{H,m}\to Z_H>0\). For sufficiently small \(m\), \(Z_{H,m}\geq Z_H/2\); hence \[|f_m-f_0|\leq 3w_0/Z_H.\] Another application gives \(\int _H|f_m-f_0|\to 0\). For each Borel \(A\), \(|\nu _m(A)-\nu _H(A)|\leq \int _H|f_m-f_0|\), proving the uniform claim. For a fixed polynomial, the dominating function \(3|P|w_0/Z_H\) is integrable by finite polynomial moments, so \(\int P f_m\to \int P f_0\). Each use of dominated convergence applies along every positive sequence \(m_j\to 0\); the sequential criterion gives the real-parameter limits as \(m\downarrow 0\). When \(H=0\), these laws are all the same point mass.
For a fixed source \(j\in E\) and \(\ell _j(x)=h(j,x)\), the inverse-covariance formula and the kernel split give \[\begin {aligned} \operatorname {Var}_{Q_m}(\ell _j) &=m^{-2}\|P_Kj\|^2\\ &\quad +\sum _{\lambda _a>0} \frac {h(j,u_a)^2}{\lambda _a+m^2}. \end {aligned}\] Every term is nonnegative. The finite sum converges; the first term is finite in the limit precisely when \(P_Kj=0\). Thus the variance has a finite limit if and only if \(j\in H\), in which case it is \(h(j,L^+j)\).
For a connected nonempty graph with positive edge weights, put \(n=|V|\). The source \(u_0=\mathbf 1_V/\sqrt n\) has variance \(m^{-2}\) as a source. Its contribution to every Green-kernel entry is \(1/(nm^2)\), and \(G_m(v,v)\geq 1/(nm^2)\). A field difference has source \(\delta _v-\delta _w\in H\), so its variance converges to the constrained variance. On a disconnected graph this holds for vertices in the same component; a difference between components has a nonzero kernel projection. These observable limits do not supply a normalized full-space massless density.
The analytic ingredient is [tao2011measure, Theorem 1.4.49, pp. 111-112, preliminary version]. The three dominators above verify its hypotheses for these Gaussian laws and their fixed polynomial observables.
Proposition 6. Pinned affine massless laws [fdip-0035]AGENTDRAFTED
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Proposition 6. Pinned affine massless laws [fdip-0035]AGENTDRAFTED
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Let a finite nonempty graph be connected, with positive edge weights. Put \(n=|V|\) and fix a vertex \(p\). Its massless action defines a probability law on \(F_p=\{\phi :\phi (p)=0\}\) with induced counting volume. For any \(b\in \mathbb R\), translation by \(b\mathbf 1_V\) defines the law on the affine constraint \(\phi (p)=b\); its mean is \(b\mathbf 1_V\) and its covariance is that of the zero-pinned law.
Proof.
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Proof.
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Write \(i_F:F_p\to \mathbb R^V\) for inclusion and \(p_F:\mathbb R^V\to F_p\) for orthogonal projection. The compressed precision is \[Q_F=p_F L i_F:F_p\to F_p.\] It is self-adjoint, since \(h_F(x,Q_Fy)=h_V(i_Fx,Li_Fy)\). A zero-energy vector is constant by the kernel theorem; vanishing at \(p\) makes it zero. Thus \(Q_F\) is positive definite. Normalization on the inner-product space \(F_p\) gives \[Z_F=(2\pi )^{(n-1)/2}/\sqrt {\det Q_F}.\] The centered law has covariance \(Q_F^{-1}\) on \(F_p\), and ambient covariance \(i_F Q_F^{-1}p_F\), by Theorem [fdip-000B]. The subspace need not be invariant under \(L\); the precision is the compressed quadratic form, not an endomorphism restriction of \(L\).
The affine constraint equals \(b\mathbf 1_V+F_p\). Define its induced volume by translating the volume on \(F_p\). Since \(D(b\mathbf 1_V+\psi )=D\psi \), this translation preserves the action and normalizer. Centeredness on \(F_p\) gives the stated mean; subtracting that mean gives exactly the zero-pinned covariance. For a single vertex \(F_p=0\), the normalizer is one and the law is concentrated at the value \(b\).
These are laws on their own constraint spaces. They are not defined by conditioning on an event in an unconstrained massless probability, whose normalizer diverges. Fixed external boundary values also remove zero modes in finite lattice regions, as in [friedli2017statistical, Definition 8.1, Remark 8.2 and Exercise 8.2, p. 381]. That source's lattice boundary and volume conventions remain part of its model; the induced-volume construction here proves the finite-graph claim.
Example 7. Pinning and mean zero on two vertices [fdip-0036]AGENTDRAFTED
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Example 7. Pinning and mean zero on two vertices [fdip-0036]AGENTDRAFTED
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Join vertices \(0,1\) by one edge of weight \(w>0\). In counting coordinates its Laplacian is \[L=w\begin {pmatrix}1&-1\\-1&1\end {pmatrix}.\] The unit mean-zero vector \(u=(1,-1)/\sqrt 2\) has eigenvalue \(2w\). Thus the mean-zero law has \[\begin {aligned} Z_H&=\sqrt {2\pi }/\sqrt {2w},\\ L^+&=\frac 1{4w}\begin {pmatrix}1&-1\\-1&1\end {pmatrix}. \end {aligned}\] The factor \(1/\sqrt 2\) is required because the reference measure is the induced orthonormal volume on \(H\).
Pinning \(\phi _0=0\) leaves the unit vector \(\delta _1\); its action is \(w\phi _1^2/2\). The pinned law therefore has \[\begin {aligned} Z_F&=\sqrt {2\pi }/\sqrt w,\\ C_F&=\begin {pmatrix}0&0\\0&1/w\end {pmatrix}. \end {aligned}\] These ambient coordinate covariances differ. But for the difference \(\phi _0-\phi _1\), applying the two covariance forms to \((1,-1)\) gives \(1/w\) in both cases. This agreement of one observable does not identify the laws. For a single vertex the zero-pinned and mean-zero spaces are both zero, so that special case has no such distinction.
These are direct one-dimensional Gaussian calculations using the positive density in [friedli2017statistical, Proposition B.55, p. 505] and the two explicitly specified induced volumes.
Example 8. The alternating cycle at zero mass [fdip-0038]AGENTDRAFTED
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Example 8. The alternating cycle at zero mass [fdip-0038]AGENTDRAFTED
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The alternating-cycle eigenbasis has constant unit vector \(u_0=(1,1,1,1)/2\); its positive eigenvalues are \(2,2t,2+2t\). Hence the mean-zero law has \[\begin {aligned} \operatorname {pdet}L&=8t(1+t),\\ Z_H&=\frac {(2\pi )^{3/2}}{\sqrt {8t(1+t)}},\\ L^+&=\tfrac 12P_{u_1}+\tfrac 1{2t}P_{u_2} +\tfrac 1{2+2t}P_{u_3}. \end {aligned}\] Its covariance rows follow from the 4 signed sums of Example [fdip-0037] by setting \(d_0=0\), \(d_1=1/2\), \(d_2=1/(2t)\), and \(d_3=1/(2+2t)\). This is the pseudoinverse, not an inverse on the full vertex space.
At \(t=1\), its first row is \[(5/16,-1/16,-3/16,-1/16).\] All diagonal entries are \(5/16\). Thus an opposite field difference has variance \(2(5/16)-2(-3/16)=1\), while an adjacent difference has variance \(2(5/16)-2(-1/16)=3/4\). Both are finite limits of the corresponding massive variances by Proposition 5.
The massive constant contribution to every covariance entry is \(1/(4m^2)\). Its divergence cancels in field differences, and kernel factorization gives the exact normalizer relation \[\begin {aligned} Z_{Q_m}&=\frac {\sqrt {2\pi }}{m}Z_{H,m},\\ mZ_{Q_m}&\longrightarrow \sqrt {2\pi }Z_H. \end {aligned}\] The full normalizer therefore does not tend to \(Z_H\); the constrained law uses its own three-dimensional reference volume.
This calculation applies the induced-volume construction of Proposition 3 to the positive spectral modes.
[friedli2017statistical, Proposition B.55, p. 505] supplies the positive Gaussian density. The zero-mode removal and its normalization factor are derived above.