ner_tags: B-Wavelength B-Wavelength O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B-Wavelength B-Wavelength O B-Wavelength I-Wavelength B-Wavelength I-Wavelength O O O O O O O O O B-Wavelength I-Wavelength B-Wavelength I-Wavelength O O B-Wavelength B-Wavelength O O O O O O O O B-Wavelength I-Wavelength O O O O O O O O O O O O O B-Citation I-Citation I-Citation I-Citation I-Citation O O O O O O O O O O O O O O O O O O B-Wavelength O O O O O O O O O O O O B-Survey O O O O O O O O O O O O O O O O O O O O O O B-Survey I-Survey I-Survey I-Survey B-Survey O O O O O O O O O O O O O O O O O O O B-Survey O O O O O B-Wavelength O O B-Survey O O O O O B-Survey I-Survey I-Survey I-Survey I-Survey B-Survey O O B-Survey I-Survey I-Survey B-Survey O O O
tokens: u g as well, the sample increases to 114. We also see that as we impose even stricter cuts, we can decrease the number of SDSS objects by an order of magnitude, while keeping nearly all of the known lenses. We do not use the stricter cuts in this implementation, but they could be used in the future with larger data sets to keep things more tractable. 3.2.4 Choosing features We build our features from combinations of magnitudes in different bands. Fig. 1 shows the distribution of our training set data in various colour and magnitude spaces. As can be seen, different kinds of objects occupy specific locations in feature space. By choosing features that best distinguish between types of objects and combining them into one high-dimensional space, our model can more easily separate the different populations of objects. Figure 1. Colourcolour plots showing the locations of our training sets in feature space. The red, green, blue and orange points represent the point-like QSOs, extended QSOs, simulated lensed quasars and blue cloud galaxies, respectively. The black stars are known lensed quasars that lie in the SDSS footprint. The dashed lines show the colour cuts listed in equation ( 6 ). Dotted lines represent either cuts that are within or statements or other cuts often used to eliminate contaminants. The black stars represent 128 known lenses that lie in SDSS, found by various selection techniques. Many have similar colours to point-like quasars, indicative of the colour selection step described by Oguri etal. ( 2006 ). Others have redder colours, corresponding to either Type-II AGN sources or bright lensing galaxies. We are mainly concerned with finding blended, small-separation lenses, since the large separation counterparts are more likely to have been found already. Therefore, we are less interested in the objects that fall on the point-like QSO locus. The dashed lines in the plot show the typical cuts that we use when selecting objects. In order to include lenses that have larger contributions from the lensing galaxy, we use relatively loose cuts in u g and W 1 W 2. However, known lenses do not lie predominantly at low W 1 W 2 or high u g . Additional metrics, such as those using the K band, can be useful for trimming contaminants in these regimes, as is done by Ostrovski etal. ( 2017 ), but this introduces two issues for our purposes. First, many of the simulated lenses are particularly bright in K . This is because the lenses are constructed via sparse interpolation over 2MASS magnitudes that suffer from Malmquist bias. Secondly, we are introduced with a depth-versus-footprint trade-off that must be addressed. Surveys such as the Two Micron All-Sky Survey (2MASS) cover the whole sky, but are not always deep enough for our purposes. In fact, only 16percent of our SDSS training set objects have valid K magnitudes in 2MASS. Deeper surveys such as the UKIRT Infrared Deep Sky Survey (UKIDSS) and the VISTA Hemisphere Survey (VHS), on the other