Galaxy Zoo: a sample of blue early-type galaxies at low redshift

Schawinski, Kevin; Lintott, Chris; Thomas, Daniel; Sarzi, Marc; Andreescu, Dan; Bamford, Steven P.; Kaviraj, Sugata; Khochfar, Sadegh; Land, Kate; Murray, Phil; Nichol, R. C.; Raddick, M. Jordan; Slosar, Anže; Szalay, Alexander S.; Vandenberg, Jan; Yi, Sukyoung K.

doi:10.1111/j.1365-2966.2009.14793.x

Cited by 171 publications

(201 citation statements)

References 67 publications

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“…If we consider the 1σ inferred late-type alignment amplitude of Heymans et al (2013), which probes up to z = 1.3, we obtain a maximum contamination of 9.2%. This estimate includes the contribution of blue early-types (Schawinski et al 2009) up to z = 1.3, which would have been mis-classified as late-types based on their spectral energy distribution. Blue galaxy alignments contamination is shown in the middle panel of Figure 2.…”

Section: Resultsmentioning

confidence: 99%

“…It is known that a small fraction (∼ 6%) of low-redshift early-type galaxies display blue colors (Schawinski et al 2009). While it is possible that blue early-types are also subject to alignments, the overall alignment strength of blue galaxies is consistent with null up to z = 1.3 Heymans et al 2013) and so we do not consider them as contaminants in general.…”

Section: Introductionmentioning

confidence: 99%

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Contamination of early-type galaxy alignments to galaxy lensing–CMB lensing cross-correlation

Chisari¹,

Dunkley²,

Miller³

et al. 2015

Mon. Not. R. Astron. Soc.

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Galaxy shapes are subject to distortions due to the tidal field of the Universe. The cross-correlation of galaxy lensing with the lensing of the Cosmic Microwave Background (CMB) cannot easily be separated from the cross-correlation of galaxy intrinsic shapes with CMB lensing. Previous work suggested that the intrinsic alignment contamination can be 15% of this cross-spectrum for the CFHT Stripe 82 (CS82) and Atacama Cosmology Telescope surveys. Here we re-examine these estimates using upto-date observational constraints of intrinsic alignments at a redshift more similar to that of CS82 galaxies. We find a ≈ 10% contamination of the cross-spectrum from red galaxies, with ≈ 3% uncertainty due to uncertainties in the redshift distribution of source galaxies and the modelling of the spectral energy distribution. Blue galaxies are consistent with being unaligned, but could contaminate the cross-spectrum by an additional 9.5% within current 95% confidence levels. While our fiducial estimate of alignment contamination is similar to previous work, our work suggests that the relevance of alignments for CMB lensing-galaxy lensing cross-correlation remains largely unconstrained. Little information is currently available about alignments at z > 1.2. We consider the upper limiting case where all z > 1.2 galaxies are aligned with the same strength as low redshift luminous red galaxies, finding as much as ≈ 60% contamination.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Contamination of early-type galaxy alignments to galaxy lensing–CMB lensing cross-correlation

Chisari¹,

Dunkley²,

Miller³

et al. 2015

Mon. Not. R. Astron. Soc.

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“…Data from the Galaxy Zoo projects have already been used in a wide variety of studies on galaxy structure, formation, and evolution (Skibba et al 2009;Bamford et al 2009;Schawinski et al 2009;Darg et al 2010;Masters et al 2010Masters et al , 2011Simmons et al 2013;Melvin et al 2014;Willett et al 2015). Comparisons of Galaxy Zoo morphologies to smaller samples from both experts and automated classifications show high levels of agreement, testifying to the accuracy of the crowdsourced annotations .…”

Section: Galaxy Zoomentioning

confidence: 99%

Rotation-invariant convolutional neural networks for galaxy morphology prediction

Dieleman¹,

Willett²,

Dambre³

2015

Monthly Notices of the Royal Astronomical Society

728

604

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Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images, which have permitted population-wide analyses of galaxy morphology. Morphological analysis has traditionally been carried out mostly via visual inspection by trained experts, which is time-consuming and does not scale to large ( 10 4 ) numbers of images.Although attempts have been made to build automated classification systems, these have not been able to achieve the desired level of accuracy. The Galaxy Zoo project successfully applied a crowdsourcing strategy, inviting online users to classify images by answering a series of questions. Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.We present a deep neural network model for galaxy morphology classification which exploits translational and rotational symmetry. It was developed in the context of the Galaxy Challenge, an international competition to build the best model for morphology classification based on annotated images from the Galaxy Zoo project.For images with high agreement among the Galaxy Zoo participants, our model is able to reproduce their consensus with near-perfect accuracy (> 99%) for most questions. Confident model predictions are highly accurate, which makes the model suitable for filtering large collections of images and forwarding challenging images to experts for manual annotation. This approach greatly reduces the experts' workload without affecting accuracy. The application of these algorithms to larger sets of training data will be critical for analysing results from future surveys such as the LSST.

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“…4 the dashed line represents the separation between "red" and "blue" galaxies introduced by Schawinski et al (2009) for a volume-limited SDSS sample (0.02 < z < 0.05) of ETG, classified morphologically by visual inspection by the Galaxy Zoo project (Lintott et al 2008). This sample shares the luminosity range, M r < −20.7 with our radio-selected sample, straddling the M * for low-redshift ETG (M r, * = −21.15, Bernardi et al 2003).…”

Section: Spectro-photometric Diagnostic Diagramsmentioning

confidence: 99%

Spectro-photometric properties of the bulk of the radio-loud AGN population

Baldi¹,

Capetti²

2010

A&A

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In a previous paper we showed that the radio sources selected by combining large area radio and optical surveys, have a strong deficit of radio emission with respect to 3CR radio-galaxies matched in line emission luminosity. We argued that the prevalence of sources with luminous extended radio structures in high flux-limited samples is due to a selection bias. Sources with low radio power form the bulk of the radio-loud AGN population but are still virtually unexplored. We here analyze their photometric and spectroscopic properties. From the point of view of their emission lines, the majority of the sample are Low Excitation Galaxies (LEG), similar to the 3CR objects at the same level of line luminosity. The hosts of the LEG are red, massive (10.5 < ∼ log M * /M < ∼ 12) Early-Type Galaxies (ETG) with large black hole masses (7.7 < ∼ log M BH /M < ∼ 9), statistically indistinguishable from the hosts of low redshift 3CR/LEG sources. No genuine radio-loud LEG could be found associated with black holes with a mass substantially smaller than 10 8 M or with a late type host. The fraction of galaxies with signs of star formation (∼5%) is similar to what is found in both the quiescent ETG and 3CR/LEG hosts. We conclude that the deficit in radio emission cannot be ascribed to differences in the properties of their hosts. We argue that instead this could be due to a temporal evolution of the radio luminosity. A minority (∼10%) of the sample show rather different properties; these are associated with low black hole masses, with spiral galaxies, or with a high excitation spectrum. In general these outliers are the result of the contamination from Seyfert galaxies and from those where the radio emission is powered by star formation. For the objects with high excitation spectra there is no clear discontinuity in either the host or nuclear properties because they include radio-quiet as well as radio-loud AGN.

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Galaxy Zoo: a sample of blue early-type galaxies at low redshift

Cited by 171 publications

References 67 publications

Contamination of early-type galaxy alignments to galaxy lensing–CMB lensing cross-correlation

Contamination of early-type galaxy alignments to galaxy lensing–CMB lensing cross-correlation

Rotation-invariant convolutional neural networks for galaxy morphology prediction

Spectro-photometric properties of the bulk of the radio-loud AGN population

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