Recovering Weighted Tangent Geometry from a Single-Scale Score Field

• URL: http://arxiv.org/abs/2608.22334v1
• Date: Sun, 23 Aug 2026 10:07:12 GMT
• Title: Recovering Weighted Tangent Geometry from a Single-Scale Score Field
• Abstract summary: We show that a score field at one noise level determines a weighted tangent geometry when the branch center and homogeneity degree $d$ are unknown.<n>We also prove that for finite planar $C1,$ branches with positive $C0,$ densities, we prove $O()$ convergence from the finite-noise score to its tangent model.
• Score: 5.081899241065076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Near a smooth data manifold, one tangent space summarizes local geometry. At a branch point, the corresponding first-order object is instead a measure over tangent directions, whose normalized masses record the local share of each branch under the chosen data measure. We ask whether a score field at one noise level determines this weighted tangent geometry when the branch center and homogeneity degree $d$ are unknown. In this tangent-measure model, $d$ is the local measure dimension. Gaussian smoothing of a homogeneous tangent measure satisfies an Ornstein--Uhlenbeck eigenfunction equation. Its weak form turns score values---without score derivatives---into a linear system for the center and homogeneity degree, with an explicit rank condition and perturbation bound. After this calibration, the tangential score on one sphere is the spherical log-gradient of a scalar Gaussian--cone transform. Integration recovers that transform up to scale, and all its spherical-harmonic multipliers are positive. Thus one exact shell identifies the normalized angular measure in every ambient dimension $D\geq2$. For at most $K$ positive rays, moments through degree $2K-1$ constructively recover count, directions, and weights in arbitrary dimension. Any fixed observation scheme needs at least $KD-1$ scalar tangential components. In the plane, degree $K$ is both sufficient and necessary, and we give quantitative finite-query certificates. For finite planar $C^{1,β}$ branches with positive $C^{0,β}$ densities, we prove $O(σ^β)$ convergence from the finite-noise score to its tangent model. In controlled experiments, 50k-step training lowers validation normalized-score error across four geometries yet raises angular-moment error, separating ordinary score fit from geometry recovery.

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