2018
DOI: 10.48550/arxiv.1804.02942
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The distribution of Gaussian multiplicative chaos on the unit interval

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Cited by 7 publications
(26 citation statements)
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“…The BPZ equations are essential for proving integrability of LCFT. They were used in the proof of the DOZZ-formula [14,15] for the 3-point function of LCFT on the sphere, and after this similar methods were used for obtaining integrability results for one dimensional GMC measures on the unit circle [19] and on the unit interval [20]. The unit circle computation was based on a boundary LCFT, which is defined in [11].…”
Section: Introduction and Main Resultsmentioning
confidence: 99%
“…The BPZ equations are essential for proving integrability of LCFT. They were used in the proof of the DOZZ-formula [14,15] for the 3-point function of LCFT on the sphere, and after this similar methods were used for obtaining integrability results for one dimensional GMC measures on the unit circle [19] and on the unit interval [20]. The unit circle computation was based on a boundary LCFT, which is defined in [11].…”
Section: Introduction and Main Resultsmentioning
confidence: 99%
“…Other results on GMC are also proved in different geometries based on the BPZ equations, such as the Fyodorov Bouchaud's formula [21], the probabilistic distribution of GMC on the unit interval [22] and exact formulas for the boundary Liouville structure constants [11,20] in an upcoming work. All these series of projects prove the BPZ equations of order (2,1) and (1,2) in a different setting and use this to deduce non trivial shift equations of the object in question, which corresponds to the conformal bootstrap method in physics.…”
Section: Introductionmentioning
confidence: 91%
“…In spite of the importance of the theory, not much is known about the distributional properties of GMC. For instance, given a bounded open set A ⊂ D, one may ask what the exact distribution of M γ (A) is, but nothing is known except in very specific cases where specialised LCFT tools are applicable [28,32,33]. Indeed even the regularity of the distribution (e.g.…”
Section: Introductionmentioning
confidence: 99%