1. MeerCRAB: MeerLICHT classification of real and bogus transients using deep learning
- Author
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Kerry Paterson, Zafiirah Hosenie, B. W. Stappers, Paul Vreeswijk, Rudolf S. Le Poole, Robert Lyon, Simon De Wet, Elmar Körding, Bart Scheers, Paul J. Groot, Steven Bloemen, Marc Klein Wolt, Vanessa McBride, Fiorenzo Stoppa, Patrick A. Woudt, and D. L. A. Pieterse
- Subjects
FOS: Computer and information sciences ,Astrophysics - instrumentation and methods for astrophysics ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Astronomy ,Astrophysics - astrophysics of galaxies ,Computer science - artificial intelligence ,FOS: Physical sciences ,Techniques: image processing ,Surveys ,Bogus ,01 natural sciences ,Convolutional neural network ,Machine Learning (cs.LG) ,Methods: data analysis ,0103 physical sciences ,Real [Transients] ,Instrumentation and Methods for Astrophysics (astro-ph.IM) ,010303 astronomy & astrophysics ,Ground truth ,Network architecture ,010308 nuclear & particles physics ,business.industry ,Deep learning ,Astronomy and Astrophysics ,Pattern recognition ,Deep learning [Methods] ,Methods: deep learning ,Matthews correlation coefficient ,Pipeline (software) ,Thresholding ,Computer science - machine learning ,Data analysis [Methods] ,General [Stars] ,Computer science - computer vision and pattern recognition ,Artificial Intelligence (cs.AI) ,Space and Planetary Science ,Filter (video) ,Image processing [Techniques] ,Stars: general ,Astrophysics of Galaxies (astro-ph.GA) ,Transients: real ,Artificial intelligence ,business - Abstract
Astronomers require efficient automated detection and classification pipelines when conducting large-scale surveys of the (optical) sky for variable and transient sources. Such pipelines are fundamentally important, as they permit rapid follow-up and analysis of those detections most likely to be of scientific value. We therefore present a deep learning pipeline based on the convolutional neural network architecture called $\texttt{MeerCRAB}$. It is designed to filter out the so called 'bogus' detections from true astrophysical sources in the transient detection pipeline of the MeerLICHT telescope. Optical candidates are described using a variety of 2D images and numerical features extracted from those images. The relationship between the input images and the target classes is unclear, since the ground truth is poorly defined and often the subject of debate. This makes it difficult to determine which source of information should be used to train a classification algorithm. We therefore used two methods for labelling our data (i) thresholding and (ii) latent class model approaches. We deployed variants of $\texttt{MeerCRAB}$ that employed different network architectures trained using different combinations of input images and training set choices, based on classification labels provided by volunteers. The deepest network worked best with an accuracy of 99.5$\%$ and Matthews correlation coefficient (MCC) value of 0.989. The best model was integrated to the MeerLICHT transient vetting pipeline, enabling the accurate and efficient classification of detected transients that allows researchers to select the most promising candidates for their research goals., 15 pages, 13 figures, Accepted for publication in Experimental Astronomy and appeared in the 3rd Workshop on Machine Learning and the Physical Sciences, NeurIPS 2020
- Published
- 2021
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