Catalog Service: COSMOS lens candidates with LensFlow
Description
In this work, we present our machine learning classification algorithm for identifying strong gravitational lenses from wide-area surveys using convolutional neural networks; LensFlow. We train and test the algorithm using a wide variety of strong gravitational lens configurations from simulations of lensing events. Images are processed through multiple convolutional layers that extract feature maps necessary to assign a lens probability to each image. LensFlow provides a ranking scheme for all sources that could be used to identify potential gravitational lens candidates by significantly reducing the number of images that have to be visually inspected. We apply our algorithm to the HST/ACS i-band observations of the COSMOS field and present our sample of identified lensing candidates. The developed machine learning algorithm is more computationally efficient and complimentary to classical lens identification algorithms and is ideal for discovering such events across wide areas from current and future surveys such as LSST and WFIRST.
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Publisher: CDSivo://CDS[Pub. ID]
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This resource was registered on: 2019 Mar 14 12:01:48ZThis resource description was last updated on: 2022 Mar 08 14:20:03Z
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This is service that does not comply with any IVOA standard but instead provides access to special capabilities specific to this resource.
This is a standard IVOA service that takes as input an ADQL or PQL query and returns tabular data.
This is a standard IVOA service that takes as input a position in the sky and a radius and returns catalog records with positions within that radius.
Cone search capability for table J/ApJ/856/68/table2 (Catalog of identified lenses by LensFlow)
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Developed with the support of the National Science Foundation under Cooperative Agreement AST0122449 with the Johns Hopkins University The NAVO project is a member of the International Virtual Observatory Alliance
This NAVO Application is hosted by the Space Telescope Science Institute