3D extinction mapping of the Milky Way using Convolutional Neural Networks: Presentation of the method and demonstration in the Carina Arm region

TL;DR

This paper presents a novel deep learning approach using convolutional neural networks to generate 3D extinction maps of the Milky Way, demonstrating its effectiveness in the Carina Arm region with improved artifact reduction.

Contribution

It introduces a CNN-based method trained on synthetic data to produce large-scale 3D extinction maps without relying on traditional Bayesian techniques.

Findings

Produced a 3D extinction map for the Carina Arm region up to 10 kpc

Reduced fingers-of-God artifacts compared to other maps

Efficiently trained with only 9 sightlines to map large areas

Abstract

Context. Several methods have been proposed to build 3D extinction maps of the Milky Way (MW), most often based on Bayesian approaches. Although some studies employed machine learning (ML) methods in part of their procedure, or to specific targets, no 3D extinction map of a large volume of the MW solely based on a Neural Network method has been reported so far. Aims. We aim to apply deep learning as a solution to build 3D extinction maps of the MW. Methods. We built a convolutional neural network (CNN) using the CIANNA framework, and trained it with synthetic 2MASS data. We used the Besan\c{c}on Galaxy model to generate mock star catalogs, and 1D Gaussian random fields to simulate the extinction profiles. From these data we computed color-magnitude diagrams (CMDs) to train the network, using the corresponding extinction profiles as targets. A forward pass with observed 2MASS CMDs provided extinction profile estimates for a grid of lines of sight. Results. We trained our network with data simulating lines of sight in the area of the Carina spiral arm tangent and obtained a 3D extinction map for a large sector in this region ($l = 257 - 303$ deg, $|b| \le 5$ deg), with distance and angular resolutions of $100$ pc and $30$ arcmin, respectively, and reaching up to $\sim 10$ kpc. Although each sightline is computed independently in the forward phase, the so-called fingers-of-God artifacts are weaker than in many other 3D extinction maps. We found that our CNN was efficient in taking advantage of redundancy across lines of sight, enabling us to train it with only 9 sightlines simultaneously to build the whole map. Conclusions. We found deep learning to be a reliable approach to produce 3D extinction maps from large surveys. With this methodology, we expect to easily combine heterogeneous surveys without cross-matching, and therefore to exploit several surveys in a complementary fashion.