Machine learning for fast transients for the Deeper, Wider, Faster programme with the Removal Of BOgus Transients (ROBOT) pipeline
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).
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.Attached Files
Published - stac983.pdf
Files
Name | Size | Download all |
---|---|---|
md5:2b7c85fc81f211c0181b2ca43f723e95
|
1.6 MB | Preview Download |
Additional details
- Eprint ID
- 114920
- Resolver ID
- CaltechAUTHORS:20220525-91441000
- CE170100004
- Australian Research Council
- DP200102102
- Australian Research Council
- Created
-
2022-05-31Created from EPrint's datestamp field
- Updated
-
2022-05-31Created from EPrint's last_modified field