A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. II. Quantifying morphological k-correction in the COSMOS field at 1<z<2: Ks band vs. I band
arXiv:0811.1045 · doi:10.1051/0004-6361/200811255
Abstract
We quantify the effects of \emph{morphological k-correction} at by comparing morphologies measured in the K and I-bands in the COSMOS area. Ks-band data have indeed the advantage of probing old stellar populations for , enabling a determination of galaxy morphological types unaffected by recent star formation. In paper I we presented a new non-parametric method to quantify morphologies of galaxies on seeing limited images based on support vector machines. Here we use this method to classify selected galaxies in the COSMOS area observed with WIRCam at CFHT. The obtained classification is used to investigate the redshift distributions and number counts per morphological type up to and to compare to the results obtained with HST/ACS in the I-band on the same objects from other works. We associate to every galaxy with and a probability between 0 and 1 of being late-type or early-type. The classification is found to be reliable up to . The mean probability is . It decreases with redshift and with size, especially for the early-type population but remains above . The classification is globally in good agreement with the one obtained using HST/ACS for . Above , the I-band classification tends to find less early-type galaxies than the Ks-band one by a factor 1.5 which might be a consequence of morphological k-correction effects. We argue therefore that studies based on I-band HST/ACS classifications at could be underestimating the elliptical population. [abridged]
accepted for publication in A&A, updated with referee comments, 12 pages, 10 figures
References in corpus (17)
- A New Non-Parametric Approach to Galaxy Morphological Classification
- The COSMOS Survey: Hubble Space Telescope / Advanced Camera for Surveys (HST/ACS) Observations and Data Processing
- The Mass Assembly History of Field Galaxies: Detection of an Evolving Mass Limit for Star Forming Galaxies
- A Direct Measurement of Major Galaxy Mergers at z < 3
- The Asymmetry of Galaxies: Physical Morphology for Nearby and High Redshift Galaxies
- The COMBO-17 Survey: Evolution of the Galaxy Luminosity Function from 25,000 Galaxies with 0.2<z<1.2
- A New Approach to Galaxy Morphology: I. Analysis of the Sloan Digital Sky Survey Early Data Release
- The 2dF Galaxy Redshift Survey: Galaxy luminosity functions per spectral type
- HST imaging of CFRS and LDSS galaxies - I: Morphological Properties
- The Vimos VLT Deep Survey: Stellar mass segregation and large-scale galaxy environment in the redshift range 0.2<z<1.4
- A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images. I Method description
- Subaru Deep Survey I. Near-Infrared Observations
- The Gemini Deep Deep Survey: VIII. When Did Early-type Galaxies Form?
- Galaxy luminosity function per morphological type up to z=1.2
- Galaxy number counts- IV. surveying the Herschel deep field in the near-infrared
- Optical-Infrared Colour Distribution of a Statistically-Complete Sample of Faint Field Spheroidal Galaxies
- The Morphological Demographics of galaxies in the ACS Hubble Ultra Deep Parallel Fields
Cited by in corpus (25)
- Data Mining and Machine Learning in Astronomy
- Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available bayesian automated classification
- The evolution of the mass-size relation for early type galaxies from z~1 to the present: dependence on environment, mass-range and detailed morphology
- Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging
- An automatic taxonomy of galaxy morphology using unsupervised machine learning
- Ganalyzer: A tool for automatic galaxy image analysis
- Early-type galaxies at z = 1.3. I. The Lynx supercluster: cluster and groups at z=1.3. Morphology and color-magnitude relation
- Star-galaxy separation strategies for WISE-2MASS all-sky infrared galaxy catalogs
- Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
- AGN-Host Galaxy Connection: Morphology and Colours of X-ray Selected AGN at z < 2
- Star-forming blue ETGs in two newly discovered galaxy overdensities in the HUDF at z=1.84 and 1.9: unveiling the progenitors of passive ETGs in cluster cores
- Morphological Parameters of Spitzer Survey of Stellar Structure in Galaxies (S4G)
- The ALHAMBRA survey: reliable morphological catalogue of 22,051 early- and late-type galaxies
- Support Vector Machine classification of strong gravitational lenses
- On the buildup of massive early-type galaxies at z<~1. I- Reconciling their hierarchical assembly with mass-downsizing
- The role of environment in the morphological transformation of galaxies in 9 intermediate redshift clusters
- Galaxy morphology prediction using capsule networks
- Effect of richness on AGN and star formation activities in SDSS galaxy groups
- Deep Extragalactic VIsible Legacy Survey (DEVILS): Stellar Mass Growth by Morphological Type since
- Soft clustering analysis of galaxy morphologies: A worked example with SDSS
- A morphological study of galaxies in ZwCl0024+1652, a galaxy cluster at redshift z 0.4
- The Morphological Transformation of Red-Sequence Galaxies in the Distant Cluster XMMU J1229+0151
- A machine learning approach for identifying the counterparts of submillimetre galaxies and applications to the GOODS-North field
- Automatic identification of outliers in Hubble Space Telescope galaxy images
- X-ray luminosity functions of different morphological and X-ray type AGN populations