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Machine learning for fast transients for the Deeper, Wider, Faster programme with the Removal Of BOgus Transients (ROBOT) pipeline

Goode, Simon and Cooke, Jeff and Zhang, Jielai and Mahabal, Ashish and Webb, Sara and Hegarty, Sarah (2022) Machine learning for fast transients for the Deeper, Wider, Faster programme with the Removal Of BOgus Transients (ROBOT) pipeline. Monthly Notices of the Royal Astronomical Society, 513 (2). pp. 1742-1754. ISSN 0035-8711. doi:10.1093/mnras/stac983. https://resolver.caltech.edu/CaltechAUTHORS:20220525-91441000

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Abstract

The Deeper, Wider, Faster (DWF) programme is optimized to detect fast transients that show luminosity changes on time-scales of sub-second to days using fast cadence simultaneous observations and rapid response follow up. One of the significant bottlenecks in DWF is the time required to assess candidates for rapid follow up and to manually inspect candidates prior to triggering space-based or large ground-based telescopes. In this paper, we present the Removal Of BOgus Transients (ROBOTs) pipeline that uses a combination of machine learning methods, a Convolutional Neural Network (CNN), and Decision Tree (CART), to analyse source quality and to filter in promising candidates. The ROBOT pipeline is optimized for ‘lossy’ compressed data required by DWF for fast data transfer to find these candidates within minutes of the light hitting the telescopes. Preliminary testing of the ROBOT pipeline on archival data showed to reduce the number of candidates that require a manual inspection from 69 628 to 3327 (a factor of ∼21 times), whilst simultaneously sorting candidates into categories of priority, with potential for further improvement. Recent real-time operation of the ROBOT pipeline in DWF-O10 showed to further reduce manual inspections from ∼155 000 to ∼5000 (a factor of ∼31 times).


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/mnras/stac983DOIArticle
https://github.com/simongoode/ROBOT-pipelineRelated ItemSource code
ORCID:
AuthorORCID
Goode, Simon0000-0002-9575-5152
Cooke, Jeff0000-0001-5703-2108
Zhang, Jielai0000-0001-5310-4186
Mahabal, Ashish0000-0003-2242-0244
Webb, Sara0000-0003-2601-1472
Additional Information:© 2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted 2022 April 5. Received 2022 April 4; in original form 2022 February 7. Thank you to all of the anonymous participants that got involved in past DWF runs, and to those who assisted in the crucial step of data labelling – without your help this project would not have been possible. Part of this research was funded by the Australian Research Council Centre of Excellence for Gravitational Wave Discovery (OzGrav), CE170100004. JC acknowledges funding by the Australian Research Council Discovery Project, DP200102102. DATA AVAILABILITY. The data and source code underlying this article will be shared on reasonable request to the corresponding author. The source code is available on GitHub at https://github.com/simongoode/ROBOT-pipeline.
Funders:
Funding AgencyGrant Number
Australian Research CouncilCE170100004
Australian Research CouncilDP200102102
Subject Keywords:methods: data analysis, techniques: image processing, stars: variables: general
Issue or Number:2
DOI:10.1093/mnras/stac983
Record Number:CaltechAUTHORS:20220525-91441000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220525-91441000
Official Citation:Simon Goode, Jeff Cooke, Jielai Zhang, Ashish Mahabal, Sara Webb, Sarah Hegarty, Machine learning for fast transients for the Deeper, Wider, Faster programme with the Removal Of BOgus Transients (ROBOT) pipeline, Monthly Notices of the Royal Astronomical Society, Volume 513, Issue 2, June 2022, Pages 1742–1754, https://doi.org/10.1093/mnras/stac983
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114920
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:31 May 2022 17:13
Last Modified:31 May 2022 17:13

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