arXiv Instrumentation and Methods for Astrophysics
arXiv Instrumentation and Methods for Astrophysics
5 years ago

Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique. (arXiv:1704.02322v2 [astro-ph.IM] UPDATED)

Camille Avestruz, Nan Li, Hanjue Zhu, Matthew Lightman, Thomas E. Collett, Wentao Luo
Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy-galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small scale image features reduces the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image with other parameterization results equivalent to random performance.

Publisher URL: http://arxiv.org/abs/1704.02322

DOI: arXiv:1704.02322v2

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