Predicting Resolved Dust Attenuation from Local Galaxy Properties Using MaNGA
Anilkumar Mailvaganam, Tayyaba Zafar, Pablo Corcho-Caballero, Tamal Mukherjee, Jahang Prathap, Kyle B. Westfall, Kevin Bundy

TL;DR
This paper develops an empirical model to predict spatially resolved dust attenuation in star-forming galaxies using MaNGA data, enabling more accurate dust corrections and insights into galaxy structure.
Contribution
The study introduces a novel, scalable model for predicting dust attenuation from local galaxy properties, validated across diverse galaxy types and orientations.
Findings
Model achieves R^2 = 0.69 and RMSE = 0.22 mag in predicting A_V.
Iterative application recovers dust-corrected SFR surface density with minimal bias.
Accurately reproduces radial A_V profiles and their dependence on galaxy mass and activity.
Abstract
Accurate spatially resolved dust corrections are critical for interpreting the structure and evolution of star-forming galaxies (SFGs). We present an empirical model for predicting spatially resolved dust attenuation () in SFGs using integral field spectroscopy from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Using a sample of 5,155 galaxies over and , we derive maps from the Balmer decrement across more than 1,898,954 star-forming spaxels. Using local star formation rate surface density () as a predictor, the model achieves and RMSE mag, with residuals that are approximately Gaussian and centred near zero. It predicts within a factor of 1.3 on kpc scales. We also demonstrate that the relation can be applied iteratively to recover dust-corrected…
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