Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens Survey Article Swipe
Keerthi Vasan G. C.
,
Stephen Sheng
,
Tucker Jones
,
Chi Po Choi
,
James Sharpnack
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.00047
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2211.00047
Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and unsupervised learning algorithms trained with the ResNetV2 neural network architecture on their ability to efficiently find strong gravitational lenses in the Deep Lens Survey (DLS). We use galaxy images from the survey, combined with simulated lensed sources, as labeled data in our training datasets. We find that models using semi-supervised learning along with data augmentations (transformations applied to an image during training, e.g., rotation) and Generative Adversarial Network (GAN) generated images yield the best performance. They offer 5--10 times better precision across all recall values compared to supervised algorithms. Applying the best performing models to the full 20 deg$^2$ DLS survey, we find 3 Grade-A lens candidates within the top 17 image predictions from the model. This increases to 9 Grade-A and 13 Grade-B candidates when $1$% ($\sim2500$ images) of the model predictions are visually inspected. This is $\gtrsim10\times$ the sky density of lens candidates compared to current shallower wide-area surveys (such as the Dark Energy Survey), indicating a trove of lenses awaiting discovery in upcoming deeper all-sky surveys. These results suggest that pipelines tasked with finding strong lens systems can be highly efficient, minimizing human effort. We additionally report spectroscopic confirmation of the lensing nature of two Grade-A candidates identified by our model, further validating our methods.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2211.00047
- https://arxiv.org/pdf/2211.00047
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4308013610Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2211.00047Digital Object Identifier
- Title
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Optimizing machine learning methods to discover strong gravitational lenses in the Deep Lens SurveyWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2022Year of publication
- Publication date
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2022-10-31Full publication date if available
- Authors
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Keerthi Vasan G. C., Stephen Sheng, Tucker Jones, Chi Po Choi, James SharpnackList of authors in order
- Landing page
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https://arxiv.org/abs/2211.00047Publisher landing page
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https://arxiv.org/pdf/2211.00047Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2211.00047Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Lens (geology), Artificial neural network, Machine learning, Deep learning, Pattern recognition (psychology), Computer vision, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.algorithms | 36 |
| abstract_inverted_index.candidates | 140, 158, 178, 233 |
| abstract_inverted_index.efficient, | 217 |
| abstract_inverted_index.identified | 234 |
| abstract_inverted_index.indicating | 191 |
| abstract_inverted_index.inspected. | 169 |
| abstract_inverted_index.inspection | 21 |
| abstract_inverted_index.minimizing | 218 |
| abstract_inverted_index.performing | 126 |
| abstract_inverted_index.supervised | 121 |
| abstract_inverted_index.validating | 239 |
| abstract_inverted_index.($\sim2500$ | 161 |
| abstract_inverted_index.Adversarial | 100 |
| abstract_inverted_index.algorithms. | 122 |
| abstract_inverted_index.efficiently | 48 |
| abstract_inverted_index.performance | 29 |
| abstract_inverted_index.predictions | 146, 166 |
| abstract_inverted_index.supervised, | 31 |
| abstract_inverted_index.additionally | 222 |
| abstract_inverted_index.architecture | 43 |
| abstract_inverted_index.confirmation | 225 |
| abstract_inverted_index.performance. | 108 |
| abstract_inverted_index.unsupervised | 34 |
| abstract_inverted_index.augmentations | 88 |
| abstract_inverted_index.gravitational | 10, 51 |
| abstract_inverted_index.spectroscopic | 224 |
| abstract_inverted_index.semi-supervised | 83 |
| abstract_inverted_index.(transformations | 89 |
| abstract_inverted_index.semi-supervised, | 32 |
| abstract_inverted_index.$\gtrsim10\times$ | 172 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.14917226 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |