Nondegeneracy and regularity of polynomial pushforwards

Abstract

Let $\hat{I}¼$ be a log-concave probability measure on ${\mathbb{R}}^n$ and let $f:{\mathbb{R}}^n\to {\mathbb{R}}^k$ be a polynomial mapping of degree at most $d$. We show that \[ μ(f\in A) \le C\bigl(λ_k(A)\bigr)^{\frac{1}{k(d-1)+1}} \] for every Borel set $A\subset {\mathbb{R}}^k$ whenever the image measure $\hat{I}¼\circ f^{-1}$ is absolutely continuous. The constant $C$ is independent of the dimension $n$, and the exponent $\frac{1}{k(d-1)+1}$ is sharp. This extends the scalar Carbery--Wright inequality and answers, in the log-concave setting, a question raised by Avni, Glazer, and Larsen. In addition, we show that the density of $\hat{I}¼\circ f^{-1}$, whenever it exists, belongs to the Nikolskii--Besov space $B_{1,\infty }^{\frac{1}{k(d-1)+1}}({\mathbb{R}}^k)$, with a dimension-free bound for the corresponding norm. A central difficulty in passing from scalar polynomials to vector-valued polynomial mappings is the lack of a suitable nondegeneracy parameter quantifying absolute continuity of $\hat{I}¼\circ f^{-1}$, as the variance does in the scalar case. Natural candidates such as the covariance matrix or the Jacobian matrix either fail to characterize this property or do not lead to dimension-free estimates. We identify such a parameter and define it to be the covariance matrix of the vector formed by the monomials of degree up to $d^{k-1}$ in the normalized components of $f$. The dimension-free nature of our results allows us to extend Kusuoka's absolute continuity criterion for Gaussian polynomial random vectors to the log-concave setting. Moreover, in this setting, we obtain estimates relating convergence in distribution to convergence in total variation for polynomial random vectors.