177 research outputs found
Euclid Preparation:XIV. The Complete Calibration of the Color-Redshift Relation (C3R2) Survey: Data Release 3
The Complete Calibration of the Color-Redshift Relation (C3R2) survey is obtaining spectroscopic redshifts in order to map the relation between galaxy color and redshift to a depth of i ~ 24.5 (AB). The primary goal is to enable sufficiently accurate photometric redshifts for Stage IV dark energy projects, particularly Euclid and the Nancy Grace Roman Space Telescope (Roman), which are designed to constrain cosmological parameters through weak lensing. We present 676 new high-confidence spectroscopic redshifts obtained by the C3R2 survey in the 2017B-2019B semesters using the DEIMOS, LRIS, and MOSFIRE multiobject spectrographs on the Keck telescopes. Combined with the 4454 redshifts previously published by this project, the C3R2 survey has now obtained and published 5130 high-quality galaxy spectra and redshifts. If we restrict consideration to only the 0.2 < zp < 2.6 range of interest for the Euclid cosmological goals, then with the current data release, C3R2 has increased the spectroscopic redshift coverage of the Euclid color space from 51% (as reported by Masters et al.) to the current 91%. Once completed and combined with extensive data collected by other spectroscopic surveys, C3R2 should provide the spectroscopic calibration set needed to enable photometric redshifts to meet the cosmology requirements for Euclid, and make significant headway toward solving the problem for Roman
Euclid preparation: XV. Forecasting cosmological constraints for the and CMB joint analysis
The combination and cross-correlation of the upcoming Euclid data with cosmic microwave background (CMB) measurements is a source of great expectation since it will provide the largest lever arm of epochs, ranging from recombination to structure formation across the entire past light cone. In this work, we present forecasts for the joint analysis of Euclid and CMB data on the cosmological parameters of the standard cosmological model and some of its extensions. This work expands and complements the recently published forecasts based on Euclid-specific probes, namely galaxy clustering, weak lensing, and their cross-correlation. With some assumptions on the specifications of current and future CMB experiments, the predicted constraints are obtained from both a standard Fisher formalism and a posterior-fitting approach based on actual CMB data. Compared to a Euclid-only analysis, the addition of CMB data leads to a substantial impact on constraints for all cosmological parameters of the standard Λ-cold-dark-matter model, with improvements reaching up to a factor of ten. For the parameters of extended models, which include a redshift-dependent dark energy equation of state, non-zero curvature, and a phenomenological modification of gravity, improvements can be of the order of two to three, reaching higher than ten in some cases. The results highlight the crucial importance for cosmological constraints of the combination and cross-correlation of Euclid probes with CMB data
Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
Convolutional Neural Networks (ConvNets) are one of the most promising
methods for identifying strong gravitational lens candidates in survey data. We
present two ConvNet lens-finders which we have trained with a dataset composed
of real galaxies from the Kilo Degree Survey (KiDS) and simulated lensed
sources. One ConvNet is trained with single \textit{r}-band galaxy images,
hence basing the classification mostly on the morphology. While the other
ConvNet is trained on \textit{g-r-i} composite images, relying mostly on
colours and morphology. We have tested the ConvNet lens-finders on a sample of
21789 Luminous Red Galaxies (LRGs) selected from KiDS and we have analyzed and
compared the results with our previous ConvNet lens-finder on the same sample.
The new lens-finders achieve a higher accuracy and completeness in identifying
gravitational lens candidates, especially the single-band ConvNet. Our analysis
indicates that this is mainly due to improved simulations of the lensed
sources. In particular, the single-band ConvNet can select a sample of lens
candidates with purity, retrieving 3 out of 4 of the confirmed
gravitational lenses in the LRG sample. With this particular setup and limited
human intervention, it will be possible to retrieve, in future surveys such as
Euclid, a sample of lenses exceeding in size the total number of currently
known gravitational lenses.Comment: 16 pages, 10 figures. Accepted for publication in MNRA
Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
The volume of data that will be produced by new-generation surveys requires
automatic classification methods to select and analyze sources. Indeed, this is
the case for the search for strong gravitational lenses, where the population
of the detectable lensed sources is only a very small fraction of the full
source population. We apply for the first time a morphological classification
method based on a Convolutional Neural Network (CNN) for recognizing strong
gravitational lenses in square degrees of the Kilo Degree Survey (KiDS),
one of the current-generation optical wide surveys. The CNN is currently
optimized to recognize lenses with Einstein radii arcsec, about
twice the -band seeing in KiDS. In a sample of colour-magnitude
selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN
retrieves 761 strong-lens candidates and correctly classifies two out of three
of the known lenses. The misclassified lens has an Einstein radius below the
range on which the algorithm is trained. We down-select the most reliable 56
candidates by a joint visual inspection. This final sample is presented and
discussed. A conservative estimate based on our results shows that with our
proposed method it should be possible to find massive LRG-galaxy
lenses at z\lsim 0.4 in KiDS when completed. In the most optimistic scenario
this number can grow considerably (to maximally 2400 lenses), when
widening the colour-magnitude selection and training the CNN to recognize
smaller image-separation lens systems.Comment: 24 pages, 17 figures. Published in MNRA
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