Strong Lens Time Delay Challenge: II. Results of TDC1

Kavli Affiliate: Kaisey Mandel

| First 5 Authors: Kai Liao, Tommaso Treu, Phil Marshall, Christopher D. Fassnacht, Nick Rumbaugh

| Summary:

We present the results of the first strong lens time delay challenge. The
motivation, experimental design, and entry level challenge are described in a
companion paper. This paper presents the main challenge, TDC1, which consisted
of analyzing thousands of simulated light curves blindly. The observational
properties of the light curves cover the range in quality obtained for current
targeted efforts (e.g.,~COSMOGRAIL) and expected from future synoptic surveys
(e.g.,~LSST), and include simulated systematic errors. nteamsA teams
participated in TDC1, submitting results from nmethods different method
variants. After a describing each method, we compute and analyze basic
statistics measuring accuracy (or bias) $A$, goodness of fit $chi^2$,
precision $P$, and success rate $f$. For some methods we identify outliers as
an important issue. Other methods show that outliers can be controlled via
visual inspection or conservative quality control. Several methods are
competitive, i.e., give $|A|<0.03$, $P<0.03$, and $chi^2<1.5$, with some of
the methods already reaching sub-percent accuracy. The fraction of light curves
yielding a time delay measurement is typically in the range $f = $20–40%. It
depends strongly on the quality of the data: COSMOGRAIL-quality cadence and
light curve lengths yield significantly higher $f$ than does sparser sampling.
Taking the results of TDC1 at face value, we estimate that LSST should provide
around 400 robust time-delay measurements, each with $P<0.03$ and $|A|<0.01$,
comparable to current lens modeling uncertainties. In terms of observing
strategies, we find that $A$ and $f$ depend mostly on season length, while P
depends mostly on cadence and campaign duration.

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