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.

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.