e 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 ∼30 galaxies/arcmin2. 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
Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images
Wide-field astronomical surveys are often affected by the presence of undesirable
reflections (often known as “ghosting artifacts” or “ghosts”) and scattered-light
artifacts. The identification and mitigation of these artifacts is important for
rigorous astronomical analyses of faint and low-surface-brightness systems. In this
work, we use images from the Dark Energy Survey (DES) to train, validate, and test
a deep neural network (Mask R-CNN) to detect and localize ghosts and scatteredlight artifacts. We find that the ability of the Mask R-CNN model to identify
affected regions is superior to that of conventional algorithms that model the
physical processes that lead to such artifacts, thus providing a powerful technique
for the automated detection of ghosting and scattered-light artifacts in current and
near-future surveys.
- Award ID(s):
- 2006340
- Publication Date:
- NSF-PAR ID:
- 10340972
- Journal Name:
- Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS)
- Page Range or eLocation-ID:
- 4
- Sponsoring Org:
- National Science Foundation
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ABSTRACT 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.
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Accepted Manuscript1.0
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