Temporal Matrix Scale Invariance and the Classification of Tipping Points

Abstract

We introduce temporal matrix scale invariance (tMSI), a mathematical structure for the two-time correlation kernel of a multivariate observable. A kernel $C(t,t&#39;)$ satisfies tMSI of order $α$ if $C(kt, kt&#39;) = k^{-α}C(t,t&#39;)$ for all $k>0$; this condition holds near a tipping point, where the divergence of the coherence time produces temporal scale freedom. By a kernel factorization theorem, every tMSI kernel separates into a power-law envelope $(tt&#39;)^{-α/2}$ and a shape function $F(t/t&#39;)$ diagonalized by the Mellin transform. This reveals a decoupling of two independent exponents: the dynamical exponent $α$, carried by the envelope, and the spectral relaxation exponent $β$, determined by the eigenvalue decay of the finite-dimensional truncation. Their equality $α= β$ characterizes a simple critical point; their inequality $α\neq β$ is the signature of temporal multicriticality. We provide a classification of tipping points. The Landau quartic coefficient $a_4$ is given exactly by $a_4 = p^2 + q^2 - 2λpq - g^2_{ααβ}Γ(σ_α, σ_β)$, where $λ= 2\sqrt{σ_ασ_β}/(σ_α+σ_β) \in (0, 1]$, $g_{ααβ}$ is the three-point structure constant, and $Γ> 0$ is in explicit closed form. The transition is continuous for $a_4 > 0$, tricritical for $a_4 = 0$, and discontinuous for $a_4 < 0$. The simple critical point $α= β$ is maximally fragile: any nonzero operator mixing drives $a_4 < 0$, placing the synchronized state generically at the edge of catastrophe. The framework yields a matrix-valued early warning diagnostic, computable from a multivariate time series without knowledge of the underlying equations, that classifies an approaching tipping point as recoverable or catastrophic. Applications to epilepsy and acute myocardial infarction are discussed.