Population mixtures and searches of lensed and extended quasars across photometric surveys
Peter Williams, Adriano Agnello, Tommaso Treu

TL;DR
This paper evaluates a Gaussian Mixture Model approach to identify strongly lensed and extended quasars in wide-field photometric surveys, aiming to improve selection accuracy over traditional methods.
Contribution
It introduces a new method using Gaussian Mixture Models with optical and infrared data to better distinguish quasar types in survey data.
Findings
Successfully identified 43 candidate objects with high lens scores.
Confirmed 2 known lenses and found 5 objects in archival Hubble data.
Demonstrated the method's potential for efficient lens candidate selection.
Abstract
Wide-field photometric surveys enable searches of rare yet interesting objects, such as strongly lensed quasars or quasars with a bright host galaxy. Past searches for lensed quasars based on their optical and near infrared properties have relied on photometric cuts and spectroscopic pre-selection (as in the Sloan Quasar Lens Search), or neural networks applied to photometric samples. These methods rely on cuts in morphology and colours, with the risk of losing many interesting objects due to scatter in their population properties, restrictive training sets, systematic uncertainties in catalog-based magnitudes, and survey-to-survey photometric variations. Here, we explore the performance of a Gaussian Mixture Model to separate point-like quasars, quasars with an extended host, and strongly lensed quasars using griz psf and model magnitudes and WISE W1, W2. The choice of optical…
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