Three-dimensional reddening maps of the Magellanic Clouds constructed by RR Lyrae stars

TL;DR

This paper presents the first three-dimensional reddening maps of the Magellanic Clouds using RR Lyrae stars, revealing dust structures and variations in dust properties across both galaxies.

Contribution

It introduces a novel method for constructing 3D reddening maps using RR Lyrae stars and adaptive quadtree modeling, providing detailed dust structure insights.

Findings

Resolved dust structures in LMC and SMC

Detection of steep reddening gradients in the LMC

Evidence of variable dust extinction laws

Abstract

We present the first three-dimensional reddening maps of the Large and Small Magellanic Clouds (LMC and SMC) constructed using fundamental-mode RR Lyrae stars from the OGLE-IV survey. By applying a period-amplitude-color relation and a period-luminosity-metallicity calibration in the OGLE photometric system, we derive intrinsic colors, color excess $E(V-I)$, and photometric distances for more than 20,000 RRab stars in the LMC and 3,000 in the SMC. Spatial variations in reddening are modeled using an adaptive quadtree scheme, where robust reddening-distance relations are fit within each partition and distances are iteratively updated to achieve self-consistency. The resulting maps reveal resolved dust structures across both galaxies, including steep reddening gradients in the central LMC and flatter profiles in the SMC. The construction of the three-dimensional reddening maps further reveals that high-extinction regions exhibit reddening behavior inconsistent with a uniform extinction law, implying localized variations in dust properties. The final maps comprise 205 partitions for the LMC and 67 partitions for the SMC, and are released together with a Python-based query tool and GeoJSON data products. These 3D maps provide a foundation for distance-dependent reddening corrections and for probing the structure and physical conditions of the Magellanic interstellar medium, and future high-precision and cadence RR Lyrae sample from Gaia DR4 will support higher-resolution mapping and deeper exploration of dust substructure.