Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Mining for Gravitationally Lensed Quasars   Gravitationally lensed (GL) quasars are brighter than their unlensed
counterparts and produce images with distinctive morphological signatures. Past
searches and target selection algorithms, in particular the Sloan Quasar Lens
Search (SQLS), have relied on basic morphological criteria, which were applied
to samples of bright, spectroscopically confirmed quasars. The SQLS techniques
are not sufficient for searching into new surveys (e.g. DES, PS1, LSST),
because spectroscopic information is not readily available and the large data
volume requires higher purity in target/candidate selection. We carry out a
systematic exploration of machine learning techniques and demonstrate that a
two step strategy can be highly effective. In the first step we use
catalog-level information ($griz$+WISE magnitudes, second moments) to preselect
targets, using artificial neural networks. The accepted targets are then
inspected with pixel-by-pixel pattern recognition algorithms (Gradient-Boosted
Trees), to form a final set of candidates. The results from this procedure can
be used to further refine the simpler SQLS algorithms, with a twofold (or
threefold) gain in purity and the same (or $80\%$) completeness at
target-selection stage, or a purity of $70\%$ and a completeness of $60\%$
after the candidate-selection step. Simpler photometric searches in $griz$+WISE
based on colour cuts would provide samples with $7\%$ purity or less. Our
technique is extremely fast, as a list of candidates can be obtained from a
stage III experiment (e.g. DES catalog/database) in {a few} CPU hours.
  The techniques are easily extendable to Stage IV experiments like LSST with
the addition of time domain information.

31
The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: stat