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Formed in summer 2012, the LSST DESC sets out its plans for the next few years, preparing to try and measure the accelerating expansion of the Universe very accurately indeed. Most of the work between now and 2020 will be on trying to predict, understand and mitigate against systematic errors in this measurement - so analysis pipelines need defining, implementing (in pieces) and testing on simulations. The strong lensing case is primarily time delay distances from ~1000 lensed quasars and SNe, but multiple source plane systems could play a role as well.

Would be nice to make similar forecast for Euclid (~40 epochs in the Deep Caps)

Hojjati, Kim and Linder (LSST DESC TDC Good Team Number 1!) try out their Gaussian Processes time delay estimation, with promising initial results - albeit on just one blinded test dataset. interestingly, they find some benefit to including microlensing in the fit, even when none is input to the mock data - which suggests that measuring microlensing parameters might potentially be tougher than just estimating time delays.

We actually ran several blinded datasets, but just illustrated the results by one case. Chris Kochanek was pretty keen that we should use a gamma=1 exponential kernel, i.e. exp{-|r/L|}, which you also adopt. We're running that to see if it performs even better. Any updates on when the Time Delay Challenge 0 will commence?

Shortly! We have the data, we're just getting it on the web for you. BTW, Eric is referring to our Flotsam code - an independent implementation of GPs for time delays, with different assumptions (and bugs :-) https://github.com/eggplantbren/Flotsam

The state of the art in time delay estimation: Tewes at el test three different methods for interpolating noisy, sparse, microlensing-infected, gap-ridden optical lensed image lightcurve data, and illustrate the benefits of a) long light curves and b) accurate (not under-estimated) uncertainties. Will these methods be good enough for accurate cosmography with 100-1000 lenses though?

Furlanetto et al selected 50-odd clusters in SDSS stripe 82 by richness, in two redshift bins, and then re-imaged in g, r and i at SOAR. Image quality is comparable to median LSST, but depth is only 22-23mag. Interesting sample for algorithm validation, but they are also interested in arc statistics