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ZENODO
Dataset . 2019
License: CC BY SA
Data sources: Datacite
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ZENODO
Dataset . 2019
License: CC BY SA
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY SA
Data sources: ZENODO
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CANDELS isolated galaxy images

Authors: Alexandre Boucaud; Marc Huertas-Company; Emille E. O. Ishida;

CANDELS isolated galaxy images

Abstract

CANDELS galaxy blender dataset Dataset to be used to create realistic galaxy blends with candels-blender Content This dataset is based on the CANDELS bulge/disk decomposition catalogue and images from Dimauro et al. (2018). Our main addition to this dataset, was to perform a visual inspection of all the 2 823 stamps and rejects all those for which the central galaxy is possibly blended the neighbouring sources are too close or too diffuse the segmentation map does not cover well the sources in the stamp weird artefacts are present in the stamp. This process removed around 800 stamps to leave 2 001 entries, available in this tarball as candels_img.npy : binary numpy array of shape (2001, 128, 128) containing 2 001 stamps (128 x 128 pixels) extracted from CANDELS F160W images, centered around isolated galaxies with a well defined morphology. candels_seg.npy : binary numpy array of shape (2001, 128, 128) containing 2 001 segmentation maps (128 x 128 pixels) associated with the above stamps and obtained via SExtractor (Bertin et al. 1996). candels_cat.csv : the catalogue of corresponding central sources, based on the catalogue obtained via SExtractor, containing the CANDELS ID, FIELD and (RA, DEC) position, the F160W magnitude and its estimated error, the F160W estimated radius, the spectroscopic redshift of the galaxy, and augmented with the galaxy type and the segmentation value of the central galaxy. Usage Load the array files in Python using numpy as: import numpy as np stamps = np.load("candels_img.npy") segmaps = np.load("candels_seg.npy") The catalogue is standard comma-separated CSV which can be conveniently parsed by tools like e.g. pandas import pandas as pd cat = pd.read_csv("candels_cat.csv") or astropy from astropy.io import ascii table = ascii.read("candels_cat.csv") Reference The original CANDELS bulge/disk decomposition catalogue can be obtained at lerma.obspm.fr/huertas/form_CANDELS.

Keywords

machine learning, astrophysics, deep learning, cosmology

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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