Smoothness of random self-similar measures on the line and the existence of interior points

Abstract In this paper, we study the smoothness of the density function of absolutely continuous measures supported on random self-similar sets on the line. We show that the natural projection of a measure with symbolic local dimension greater than 1 at every point is absolutely continuous with Hölder continuous density almost surely. In particular, if the similarity dimension is greater than 1 then the random self-similar set on the line contains an interior point almost surely.

Authors

Institutions

Publication Details

Journal
Probability Theory and Related Fields
Published
2025-06-11
DOI
https://doi.org/10.1007/s00440-025-01389-2
Primary Topic
Mathematical Dynamics and Fractals
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smoothness of random self-similar measures on the line and the existence of interior points

Balázs Bárány, Michał Rams
Probability Theory and Related Fields
Mathematical Dynamics and Fractals
article

Smoothness of random self-similar measures on the line and the existence of interior points

Balázs Bárány, Michał Rams
article en

Abstract

Abstract In this paper, we study the smoothness of the density function of absolutely continuous measures supported on random self-similar sets on the line. We show that the natural projection of a measure with symbolic local dimension greater than 1 at every point is absolutely continuous with Hölder continuous density almost surely. In particular, if the similarity dimension is greater than 1 then the random self-similar set on the line contains an interior point almost surely.

Probability Theory and Related Fields
HUN-REN Alfréd Rényi Institute of Mathematics (HU), Budapest University of Technology and Economics (HU), Institute of Mathematics (PL), Polish Academy of Sciences (PL)
Budapesti Műszaki és Gazdaságtudományi Egyetem, Narodowym Centrum Nauki, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap
Openalex Percentile: Top 81%
Mathematical Dynamics and Fractals
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.