# Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning

## TL;DR

This paper introduces Astro R-CNN, a deep learning method based on Mask R-CNN for detecting, classifying, and deblending astronomical sources in multi-band images, showing high precision and robustness.

## Contribution

The paper presents a novel application of Mask R-CNN to astronomical image analysis, demonstrating effective source detection, classification, and deblending with high accuracy.

## Key findings

- 92% precision at 80% recall for stars
- 98% precision at 80% recall for galaxies
- Robust deblending of blended sources

## Abstract

We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92% at 80% recall for stars and a precision of 98% at 80% recall for galaxies in a typical field with $\sim30$ galaxies/arcmin$^2$. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as LSST and WFIRST. Our code, Astro R-CNN, is publicly available at https://github.com/burke86/astro_rcnn.