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MeerCRAB: MeerLICHT classification of real and bogus transients using deep learning

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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 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 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.

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Code Availability

MeerCRAB code and pre-trained models are available on Github at https://github.com/Zafiirah13/meercrab and on Zenodo at https://doi.org/10.5281/zenodo.4049943.

Notes

  1. see https://github.com/pmvreeswijk/BlackBOX and https://github.com/pmvreeswijk/ZOGY

  2. see https://www.idia.ac.za/

  3. see https://github.com/astropy/astroscrappy

  4. see https://acstools.readthedocs.io/en/latest/satdet.html

  5. 5 vetters labelled them as bogus and the other 5 as real.

  6. https://github.com/tensorflow/tensorflow

  7. See https://github.com/Zafiirah13/multi_input_frbid and https://github.com/Zafiirah13/FRBID

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Acknowledgements

We thank the referee for useful comments and suggestions for improving this paper. We would like to thank the people who gave up their time to do the vetting of the sample: Laura Driessen, Naomi titus, Mark Beijer, Nadia Blagorodnova, Joris Kersten, David Modiano and Roque Ruiz Carmona, without whose effort this work would not have been possible. We would like to also thank Arrykrishna Mootoovaloo and Fabian Gieseke for useful discussion. The MeerLICHT consortium is a partnership between Radboud University, the University of Cape Town, the Netherlands Organisation for Scientific Research (NWO), the South African Astronomical Observatory (SAAO), the University of Oxford, the University of Manchester and the University of Amsterdam, in association with and, partly supported by, the South African Radio Astronomy Observatory (SARAO), the European Research Council and the Netherlands Research School for Astronomy (NOVA).

Funding

ZH acknowledges support from the UK Newton Fund as part of the Development in Africa with Radio Astronomy (DARA) Big Data project delivered via the Science & Technology Facilities Council (STFC). BWS acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 694745). PJG and SDW are supported by NRF SARChI Grant 111692.

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Authors and Affiliations

  1. Jodrell Bank Centre for Astrophysics, Department of Physics and Astronomy, The University of Manchester, Manchester, M13 9PL, UK

    Zafiirah Hosenie & Benjamin Stappers

  2. Department of Astrophysics/IMAPP, Radboud University, 9010,6500 GL, Nijmegen, The Netherlands

    Steven Bloemen, Paul Groot, Fiorenzo Stoppa, Paul Vreeswijk, Marc Klein Wolt, Elmar Körding & Daniëlle L. A. Pieterse

  3. Inter-University Institute for Data Intensive Astronomy, Department of Astronomy, University of Cape Town, Private Bag X3, Rondebosch, 7701, South Africa

    Paul Groot, Simon De Wet, Vanessa McBride & Patrick Woudt

  4. South African Astronomical Observatory, P.O. Box 9, 7935, Observatory, South Africa

    Paul Groot

  5. Department of Computer Science, Edge Hill University, Ormskirk Lancashire, L39 4QP, UK

    Robert Lyon

  6. Dataspex B.V., c/o Centrum Wiskunde, Informatica, PO Box 94079, 1090 GB, Amsterdam, The Netherlands

    Bart Scheers

  7. IAU-Office For Astronomy for Development, P.O. Box 9, 7935, Observatory, South Africa

    Vanessa McBride

  8. Leiden Observatory, Leiden University, P.O. Box 9513, NL-2300 RA, Leiden, The Netherlands

    Rudolf Le Poole

  9. Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA) and Department of Physics and Astronomy, Northwestern University, Evanston, IL, 60208, USA

    Kerry Paterson

Authors
  1. Zafiirah Hosenie
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  2. Steven Bloemen
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Correspondence to Zafiirah Hosenie.

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Hosenie, Z., Bloemen, S., Groot, P. et al. MeerCRAB: MeerLICHT classification of real and bogus transients using deep learning. Exp Astron 51, 319–344 (2021). https://doi.org/10.1007/s10686-021-09757-1

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