Multi-colour detection of gravitational arcs

Maturi, M.; Mizera, Sebastian; Seidel, G.

doi:10.1051/0004-6361/201321634

Cited by 31 publications

(41 citation statements)

References 57 publications

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“…To the best of our knowledge, there are four lens searches similar to ours in CFHTLS (Gavazzi et al 2014;More et al 2012More et al , 2016Maturi et al 2014). More et al (2012) built a sample of lenses using ArcFinder with a setting such that only systems with arc radii larger than 2 are kept in the sample.…”

Section: Comparison With Previous Searchesmentioning

confidence: 74%

“…They apply their method to the CFHTLS Archive Research Survey (CARS; Erben et al 2009) data, which covers 37 square degrees, to verify its efficiency and to detect new gravitational arcs. Table 2 lists the lenses found both by the PCA-finder and by Maturi et al (2014). Gavazzi et al (2014) use their RingFinder tool to search for galaxies lensed by massive foreground early-type galaxies.…”

Section: Comparison With Previous Searchesmentioning

confidence: 99%

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The PCA Lens-Finder: application to CFHTLS

Paraficz

Courbin

Tramacere

et al. 2016

A&A

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We present the results of a new search for galaxy-scale strong lensing systems in CFHTLS Wide. Our lens-finding technique involves a preselection of potential lens galaxies, applying simple cuts in size and magnitude. We then perform a Principal Component Analysis of the galaxy images, ensuring a clean removal of the light profile. Lensed features are searched for in the residual images using the clustering topometric algorithm DBSCAN. We find 1098 lens candidates that we inspect visually, leading to a cleaned sample of 109 new lens candidates. Using realistic image simulations we estimate the completeness of our sample and show that it is independent of source surface brightness, Einstein ring size (image separation) or lens redshift. We compare the properties of our sample to previous lens searches in CFHTLS. Including the present search, the total number of lenses found in CFHTLS amounts to 678, which corresponds to ∼4 lenses per square degree down to i(AB) = 24.8. This is equivalent to ∼60.000 lenses in total in a survey as wide as Euclid, but at the CFHTLS resolution and depth.

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Section: Comparison With Previous Searchesmentioning

confidence: 74%

Section: Comparison With Previous Searchesmentioning

confidence: 99%

The PCA Lens-Finder: application to CFHTLS

Paraficz

Courbin

Tramacere

et al. 2016

A&A

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“…Although SExtractor does not deal with all these issues in an optimal way for arc detection, it has been used in many arc finders for their object identification to some degree (e.g., Horesh et al 2005;Estrada et al 2007;Kubo & Dell'Antonio 2008;Marshall et al 2009;Joseph et al 2014;Maturi et al 2014). A better performance than running SExtractor in a straight way, as in the current work, has been obtained by carrying out multiple runs of this code with different thresholds (Horesh et al 2005), or by using SExtractor alone for pixel thresholding (Kubo & Dell'Antonio 2008).…”

Section: Discussionmentioning

confidence: 87%

“…Another possibility is to use the growing number of strong lensing systems detected in wide-field surveys and HST images to perform the training on real data sets. For example, over 600 candidate systems have been detected in recent studies using CFHTLS data (Maturi et al 2014;Gavazzi et al 2014;Brault & Gavazzi 2015;More et al 2016;Paraficz et al 2016), which could be used to train and better characterize the AMA and other arc finders. By training and validating the ANN with more realistic simulated arcs or with real data, we expect to reach a better agreement for c in comparison to applications to other data sets (and therefore to achieve a higher completeness), which is different from what we found when applying our trained ANN to the HST data.…”

Section: Discussionmentioning

confidence: 99%

A neural network gravitational arc finder based on the Mediatrix filamentation method

Makler

Albuquerque

Brandt

2017

A&A

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Context. Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. Aims. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements that are derived from the Mediatrix filamentation method as entries to an artificial neural network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. Methods. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. Results. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input into the ANN after a preselection. However, this completeness drops to ∼70% on the HST images. The false detections are on the order of 3% of the objects detected in these images. Conclusions. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern-recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.

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“…There will be even more data with the upcoming long-term big survey projects such as LSST (LSST Dark Energy Science Collaboration 2012), the Dark Energy Survey (DES 3 ), and EUCLID (Boldrin et al 2012). Even though automated software is used to look for strong lensing candidate detection (e.g., Alard 2006;Seidel & Bartelmann 2007;Marshall et al 2009;Sygnet et al 2010;Maturi et al 2014;Joseph et al 2014;Gavazzi et al 2014), we still lack crucial information for accurate lens modeling. For instance, the impossibility (in most cases) of spectroscopically confirming the lensing nature of arcs in groups, or even dynamically confirming that the members of the group-lensing candidates are gravitationally-bound structures (e.g., Thanjavur et al 2010;Muñoz et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Characterizing SL2S galaxy groups using the Einstein radius

Verdugo¹,

Motta²,

Foëx³

et al. 2014

A&A

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Aims. We aim to study the reliability of R A (the distance from the arcs to the center of the lens) as a measure of the Einstein radius in galaxy groups. In addition, we want to analyze the possibility of using R A as a proxy to characterize some properties of galaxy groups, such as luminosity (L) and richness (N). Methods. We analyzed the Einstein radius, θ E , in our sample of Strong Lensing Legacy Survey (SL2S) galaxy groups, and compared it with R A , using three different approaches: 1) the velocity dispersion obtained from weak lensing assuming a singular isothermal sphere profile (θ E,I ); 2) a strong lensing analytical method (θ E,II ) combined with a velocity dispersion-concentration relation derived from numerical simulations designed to mimic our group sample; and 3) strong lensing modeling (θ E,III ) of eleven groups (with four new models presented in this work) using Hubble Space Telescope (HST) and Canada-France-Hawaii Telescope (CFHT) images. Finally, R A was analyzed as a function of redshift z to investigate possible correlations with L, N, and the richness-to-luminosity ratio (N/L). Results. We found a correlation between θ E and R A , but with large scatter. We estimate θ E,I = (2.2 ± 0.9) + (0.7 ± 0.2)R A , θ E,II = (0.4 ± 1.5) + (1.1 ± 0.4)R A , and θ E,III = (0.4 ± 1.5) + (0.9 ± 0.3)R A for each method respectively. We found weak evidence of anticorrelation between R A and z, with Log R A = (0.58 ± 0.06) − (0.04 ± 0.1)z, suggesting a possible evolution of the Einstein radius with z, as reported previously by other authors. Our results also show that R A is correlated with L and N (more luminous and richer groups have greater R A ), and a possible correlation between R A and the N/L ratio. Conclusions. Our analysis indicates that R A is correlated with θ E in our sample, making R A useful for characterizing properties like L and N (and possibly N/L) in galaxy groups. Additionally, we present evidence suggesting that the Einstein radius evolves with z.

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Multi-colour detection of gravitational arcs

Cited by 31 publications

References 57 publications

The PCA Lens-Finder: application to CFHTLS

The PCA Lens-Finder: application to CFHTLS

A neural network gravitational arc finder based on the Mediatrix filamentation method

Characterizing SL2S galaxy groups using the Einstein radius

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