English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Context. Several methods have been proposed to build 3D extinction maps ofthe Milky Way (MW), most often based on Bayesian approaches. Although somestudies employed machine learning (ML) methods in part of their procedure, orto specific targets, no 3D extinction map of a large volume of the MW solelybased on a Neural Network method has been reported so far. Aims. We aim toapply deep learning as a solution to build 3D extinction maps of the MW.Methods. We built a convolutional neural network (CNN) using the CIANNAframework, and trained it with synthetic 2MASS data. We used the Besan\c{c}onGalaxy model to generate mock star catalogs, and 1D Gaussian random fields tosimulate the extinction profiles. From these data we computed color-magnitudediagrams (CMDs) to train the network, using the corresponding extinctionprofiles as targets. A forward pass with observed 2MASS CMDs providedextinction profile estimates for a grid of lines of sight. Results. We trainedour network with data simulating lines of sight in the area of the Carinaspiral arm tangent and obtained a 3D extinction map for a large sector in thisregion ($l = 257 - 303$ deg, $|b| \le 5$ deg), with distance and angularresolutions of $100$ pc and $30$ arcmin, respectively, and reaching up to $\sim10$ kpc. Although each sightline is computed independently in the forwardphase, the so-called fingers-of-God artifacts are weaker than in many other 3Dextinction maps. We found that our CNN was efficient in taking advantage ofredundancy across lines of sight, enabling us to train it with only 9sightlines simultaneously to build the whole map. Conclusions. We found deeplearning to be a reliable approach to produce 3D extinction maps from largesurveys. With this methodology, we expect to easily combine heterogeneoussurveys without cross-matching, and therefore to exploit several surveys in acomplementary fashion.

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