Quantitative and qualitative Kac's chaos on the Boltzmann's sphere

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Abstract

We investigate the construction of chaotic probability measures on the Boltzmann's sphere, which is the state space of the stochastic process of a many-particle system undergoing a dynamics preserving energy and momentum. Firstly, based on a version of the local Central Limit Theorem (or Berry-Esseen theorem), we construct a sequence of probabilities that is Kac chaotic and we prove a quantitative rate of convergence. Then, we investigate a stronger notion of chaos, namely entropic chaos introduced in (Kinet. Relat. Models 3 (2010) 85-122), and we prove, with quantitative rate, that this same sequence is also entropically chaotic. Furthermore, we investigate more general class of probability measures on the Boltzmann's sphere. Using the HWI inequality we prove that a Kac chaotic probability with bounded Fisher's information is entropically chaotic and we give a quantitative rate. We also link different notions of chaos, proving that Fisher's information chaos, introduced in (On Kac's chaos and related problems (2012) Preprint), is stronger than entropic chaos, which is stronger than Kac's chaos. We give a possible answer to (Kinet. Relat. Models 3 (2010) 85-122), Open Problem 11, in the Boltzmann's sphere's framework. Finally, applying our previous results to the recent results on propagation of chaos for the Boltzmann equation (Invent. Math. 193 (2013) 1-147), we prove a quantitative rate for the propagation of entropic chaos for the Boltzmann equation with Maxwellian molecules.

Original languageEnglish
Pages (from-to)993-1039
Number of pages47
JournalAnnales de l'institut Henri Poincare (B) Probability and Statistics
Volume51
Issue number3
DOIs
Publication statusPublished - 1 Aug 2015
Externally publishedYes

Keywords

  • Berry-Esseen
  • Boltzmann equation
  • Central Limit Theorem
  • Entropic chaos
  • Entropy
  • Fisher's information
  • Fisher's information chaos
  • HWI inequality
  • Kac's chaos
  • Many-particle jump process
  • Mean-field limit

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