CFHTLS-SL2S Software

Search of Strong Lens candidates

Large ground- and space-based surveys require automated software to detect and help users identify lensing candidates. We are currently developing and testing three types of algorithms. Once tested and debugged, beta versions of the software will be released on this page.

Arclets: (authors: Mireille Dantel-Fort , Bernard Fort & Jean-François Sygnet ) is aimed at detecting small multiple arcs in the center of clusters and groups. These systems (probable optical depth 10^-4 to 10^-5) are difficult to spot with the eye because of bluring by seeing, and because Einstein radii of such systems are small (4-10 arcsec), hence this subgroup is the less complete in all strong gravitational samples, and will greatly benefit from an automated method. One of the challenging idea is to uncover underluminous Halos shared by more than one galaxy.
- Ring (author: R. Gavazzi is aimed at detecting Einstein rings with R_E > 1 ″ (optical depth of the order of 10^-3) surrounding isolated massive ellipticals at z>0.4. Their predicted number is more than 10 deg^-2, on high-resolution images from space telescopes. Because of seeing and very small Einstein radii (0.5 to 3 arcsec), Ring only detect a few per deg². With the CFHTLS WIDE, we shall build a sample of few hundreds, hence much more than the multiple QSOs based sample.

GiantArc (author Christophe Alard ) is aimed at detecting giant luminous arcs. It is another type on strong lens where completeness, robust statistical characterization of the sample will greatly benefit from an automated software, allowing us to perform objective selection based on arc length, surface brightness, exact field area, etc... in order to compare with numerical predictions.

Sample characterization

The sample characterization represents a crucial component of the CFHTLS-SL2S. The tasks include:

A set of MC simulations, assessing the efficiency of the detection algorithms with regards to CFHTLS characteristics and lens properties.
A lens model using the CFHTLS imaging and HyperZ photometric redshifts.
A selection based on quantitative criteria, such as surface brightness, total brightness, quality of photometric redshifts, as well as qualitative criteria; strong lens type, number of candidates, completeness of the sample.
A spectroscopic follow-up of the best candidates. Candidates with a strong potential to address ligering astrophysical questions will be given priority, but ultimately as many candidates as possible shall be observed.
A high-resolution imaging follow-up with HST is expected for the most outstanding confirmed lenses.

Strong Lens Modelling

Modelling lenses is possible through a variety of techniques and public software. The Lensing Sites link offers links to many of them.
On cluster and group lenses, we will use the modelling codes and expertise of Jean-Paul Kneib and Raphael Gavazzi. The new codes yield a rapid convergence on complex systems, including pertubative contributions of sub-halos, using Markov-Chain Monte-Carlo algorithms. They also have the capability to invert the lensing system and reconstruct the source image (cf. the review of Brewer & Lewis 2005).
The case of isolated massive elliptical lenses is much simpler and can greatly benefit from slower optimisation algorithms more robust to the problem of local minima. David Valls-Gabaud's Genetic evolution algorithm offers an excellent option.