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Lens Discovery in the Era of Wide-area Surveys

Published online by Cambridge University Press:  04 March 2024

Philip Holloway Open the ORCID record for Philip Holloway [Opens in a new window] ,
Philip J Marshall  and
Aprajita Verma
Philip Holloway*
Affiliation:
Sub-department of Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK.
Philip J Marshall
Affiliation:
Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics, Stanford University, Stanford, CA 94305, USA SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
Aprajita Verma
Affiliation:
Sub-department of Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford, OX1 3RH, UK.
*
email: [email protected]
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Abstract

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Forthcoming data from the Vera Rubin Observatory, Euclid and Roman telescopes are expected to increase the number of strong lenses by two orders of magnitude. With current discovery methods these would be accompanied by an even greater number of false positives. In that context we find that using an ensemble of classifiers would provide a more complete sample of high-purity lenses and present methods to post-process the outputs of such classifiers to give reliable probabilities that a given image contains a lens.

Keywords

gravitational lensing: stronggalaxies: generalmethods: data analysis
Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 18 , Symposium S381: Strong Gravitational Lensing in the Era of Big Data , December 2022 , pp. 35 - 37
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Astronomical Union

References

Collett, T. E. 2015, ApJ, 811, 20 CrossRefGoogle Scholar
Holloway, P., Verma, A., Marshall, P. J., More, A., Tecza, M. 2023, MNRAS, 525, 2341–2354 CrossRefGoogle Scholar
Cañameras, R., Schuldt, S., Shu, Y., et al. 2021 A&A, 653, L6 Google Scholar
Sonnenfeld, A., Verma, A., More, A., et al. 2020 A&A, 642, A148 Google Scholar
Zadrozny, B., Elkan, C. 2002, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 694–699Google Scholar
Sugiyama, M., Suzuki, T., Nakajima, S. et al. 2008, Annals of the Institute of Statistical Mathematics, 60, 699 CrossRefGoogle Scholar
Pirš, G., Štrumbelj, E. 2019, Journal of Machine Learning Research, 20, 1892–1909 Google Scholar
Supplementary material: PDF

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Holloway et al. supplementary material

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