Galaxy And Mass Assembly: the G02 field, Herschel–ATLAS target selection and data release 3

Baldry, I. K.; Liske, J.; Brown, M. J. I.; Robotham, Aaron S. G.; Driver, Simon P.; Dunne, L.; Alpaslan, Mehmet; Brough, Sarah; Cluver, Michelle; Eardley, Elizabeth; Farrow, Daniel J.; Heymans, Catherine; Hildebrandt, Hendrik; Hopkins, A. M.; Kelvin, Lee S.; Loveday, Jon; Moffett, Amanda J.; Norberg, Peder; Owers, Matt S.; Taylor, Edward N.; Wright, Angus H.; Bamford, Steven P.; Bland-Hawthorn, Joss; Bourne, N.; Bremer, M. N.; Colless, Matthew; Conselice, C. J.; Croom, S. M.; Davies, Luke J. M.; Foster, Caroline; Grootes, Meiert W.; Holwerda, Benne W.; Jones, D. Heath; Kafle, Prajwal R.; Kuijken, Konrad; Lara-López, M. A.; López-Sánchez, Á. R.; Meyer, M.; Phillipps, S.; Sutherland, William J.; Kampen, E. van; Wilkins, Stephen M.

doi:10.1093/mnras/stx3042

Cited by 230 publications

(151 citation statements)

References 83 publications

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“…In this section we summarise the main characteristics of the LinKS sample. To enable this analysis, we rely on candidates with known spectroscopic redshift publicly available from SDSS DR14, GAMA DR3 and 2dFLenS (Abolfathi et al 2018;Baldry et al 2018;Blake et al 2016). We also incorporate accurate multi-band colours as measured by the Gaussian Aperture and PSF (GAaP) code.…”

Section: Candidate Propertiesmentioning

confidence: 99%

LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks

Petrillo

Tortora

Vernardos

et al. 2019

Monthly Notices of the Royal Astronomical Society

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112

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We present a new sample of galaxy-scale strong gravitational-lens candidates, selected from 904 square degrees of Data Release 4 of the Kilo-Degree Survey (KiDS), i.e., the "Lenses in the Kilo-Degree Survey" (LinKS) sample. We apply two Convolutional Neural Networks (ConvNets) to ∼ 88 000 colour-magnitude selected luminous red galaxies yielding a list of 3500 strong-lens candidates. This list is further down-selected via human inspection. The resulting LinKS sample is composed of 1983 rank-ordered targets classified as "potential lens candidates" by at least one inspector. Of these, a high-grade subsample of 89 targets is identified with potential strong lenses by all inspectors. Additionally, we present a collection of another 200 strong lens candidates discovered serendipitously from various previous ConvNet runs. A straightforward application of our procedure to future Euclid or LSST data can select a sample of ∼ 3000 lens candidates with less than 10 per cent expected false positives and requiring minimal human intervention.

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Section: Candidate Propertiesmentioning

confidence: 99%

LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks

Petrillo

Tortora

Vernardos

et al. 2019

Monthly Notices of the Royal Astronomical Society

Self Cite

112

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“…This is rather small, and is a reflection of the strong bias of the Gaia observing and data processing against retaining galaxies with 5-parameter astrometry. We investigated supplementing this with other galaxy catalogues, in particular the 2dF galaxy redshift survey (Colless et al 2001), the 6dF galaxy survey (Jones et al 2009), or the Galaxy And Mass Assembly (GAMA) survey (Baldry et al 2018). However, these did not add more than a couple of thousand galaxies.…”

Section: Galaxiesmentioning

confidence: 99%

Quasar and galaxy classification in Gaia Data Release 2

Bailer‐Jones

Fouesneau

Andrae

2019

Monthly Notices of the Royal Astronomical Society

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We construct a supervised classifier based on Gaussian Mixture Models to probabilistically classify objects in Gaia data release 2 (GDR2) using only photometric and astrometric data in that release. The model is trained empirically to classify objects into three classes -star, quasar, galaxy -for G ≥ 14.5 mag down to the Gaia magnitude limit of G = 21.0 mag. Galaxies and quasars are identified for the training set by a cross-match to objects with spectroscopic classifications from the Sloan Digital Sky Survey. Stars are defined directly from GDR2. When allowing for the expectation that quasars are 500 times rarer than stars, and galaxies 7500 times rarer than stars (the class imbalance problem), samples classified with a threshold probability of 0.5 are predicted to have purities of 0.43 for quasars and 0.28 for galaxies, and completenesses of 0.58 and 0.72 respectively. The purities can be increased up to 0.60 by adopting a higher threshold. Not accounting for this expected low frequency of extragalactic objects (the class prior) would give both erroneously optimistic performance predictions and severely impure samples. Applying our model to all 1.20 billion objects in GDR2 with the required features, we classify 2.3 million objects as quasars and 0.37 million objects as galaxies (with individual probabilities above 0.5). The small number of galaxies is due to the strong bias of the satellite detection algorithm and on-ground data selection against extended objects. We infer the true number of quasars and galaxies -as these classes are defined by our training set -to be 690 000 and 110 000 respectively (±50%). The aim of this work is to see how well extragalactic objects can be classified using only GDR2 data. Better classifications should be possible with the low resolution spectroscopy (BP/RP) planned for GDR3.

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“…• restrictions on the quality of z measurements ware taken into account by adopting NQ>2 and PROB>0.8, as recommended by Baldry et al (2018).…”

Section: Data Selectionmentioning

confidence: 99%

UV bright red-sequence galaxies: how do UV upturn systems evolve in redshift and stellar mass?

Dantas

Coelho

Gonçalves

2019

Monthly Notices of the Royal Astronomical Society

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The so-called ultraviolet (UV) upturn of elliptical galaxies is a phenomenon characterised by the up-rise of their fluxes in bluer wavelengths, typically in the 1,200-2,500 A range. This work aims at estimating the rate of occurrence of the UV upturn over the entire red-sequence population of galaxies that show significant UV emission. This assessment is made considering it as function of three parameters: redshift, stellar mass, and -what may seem counter-intuitive at first -emission-line classification. We built a multiwavelength spectro-photometric catalogue from the Galaxy Mass Assembly survey, together with aperture-matched data from Galaxy Evolution Explorer and Sloan Digital Sky Survey, covering the redshift range between 0.06 and 0.40. From this sample, we analyse the UV emission among UV bright galaxies, by selecting those that occupy the red-sequence locus in the (NUV-r) × (FUV-NUV) chart; then, we stratify the sample by their emission-line classes. To that end, we make use of emission-line diagnostic diagrams, focusing the analysis in retired/passive lineless galaxies. Then, a Bayesian logistic model was built to simultaneously deal with the effects of all galaxy properties (including emission-line classification or lack thereof). The main results show that retired/passive systems host an up-rise in the fraction of UV upturn for redshifts between 0.06 and 0.25, followed by an in-fall up to 0.35. Additionally, we show that the fraction of UV upturn hosts rises with increasing stellar mass.

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Galaxy And Mass Assembly: the G02 field, Herschel–ATLAS target selection and data release 3

Cited by 230 publications

References 83 publications

LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks

LinKS: discovering galaxy-scale strong lenses in the Kilo-Degree Survey using convolutional neural networks

Quasar and galaxy classification in Gaia Data Release 2

UV bright red-sequence galaxies: how do UV upturn systems evolve in redshift and stellar mass?

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