TY - JOUR
T1 - Deblending and classifying astronomical sources with Mask R-CNN deep learning
AU - Burke, Colin J.
AU - Aleo, Patrick D.
AU - Chen, Yu Ching
AU - Liu, Xin
AU - Peterson, John R.
AU - Sembroski, Glenn H.
AU - Lin, Joshua Yao Yu
N1 - Publisher Copyright:
© 2019 The Author(s)
PY - 2019/12/1
Y1 - 2019/12/1
N2 - We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network 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 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 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 Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, ASTRO R-CNN, is publicly available at https://github.com/burke86/astro rcnn.
AB - We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network 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 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 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 Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, ASTRO R-CNN, is publicly available at https://github.com/burke86/astro rcnn.
KW - Galaxies: general
KW - Methods: data analysis
KW - Techniques: image processing
UR - http://www.scopus.com/inward/record.url?scp=85079601659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079601659&partnerID=8YFLogxK
U2 - 10.1093/mnras/stz2845
DO - 10.1093/mnras/stz2845
M3 - Article
AN - SCOPUS:85079601659
SN - 0035-8711
VL - 490
SP - 3952
EP - 3965
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 3
ER -