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The time delays themselves have been proposed as tools to study massive substructures within lens galaxies \citep{KeetonAndMoustakas2009}, and for measuring cosmological parameters, primarily the Hubble constant, $H_0$ \citep[see, e.g.,][for a recent example]{SuyuEtal2013}, a method first proposed by \citet{Refsdal1964}. In the future, we aspire to measure further cosmological parameters (e.g., dark energy) by combining large samples of measured time delay distances \citep[e.g.,][]{Linder2012}. It is clearly of great interest to develop to maturity the powers of time delay lens analysis for probing the dark universe.   New wide area imaging surveys that repeatedly scan the sky to gather time-domain information on variable sources are coming online. Dedicated follow-up monitoring campaigns are obtaining tens of time-delays (REF).  This pursuit will reach a new height when the \emph{Large Synoptic Survey Telescope} (LSST) enables the first long baseline multi-epoch observational campaign on $\sim$1000 lensed quasars \citep{LSSTSciBook}. However, to use the measured LSST lightcurves to extract time delays for accurate cosmology will require detailed understanding of how, and how well, time delays can be reconstructed from data with real world properties of noise, gaps, and additional systematic variations. For example, to what accuracy can time delays between the multiple image intensity patterns be measured from individual double- or quadruply-imaged systems for which the sampling rate and campaign length are given by LSST? In order for time delay errors to be small compared to errors from the gravitational potential, we will need the precision of time delays on an individual system to be better than 3\%, and those estimates will need to be robust to systematic error. Simple techniques such as the ``dispersion'' method \citep{PeltEtal1994,PeltEtal1996} or spline interpolation through the sparsely sampled data \citep[e.g.,][]{TewesEtal2013a} yield time delays which {\it may} be insufficiently accurate for a Stage IV dark energy experiment. More complex algorithms such as Gaussian Process modeling \citep[e.g.,][]{TewesEtal2013a,Hojjati+Linder2013} may hold more promise. None of these methods have been tested on large scale data sets.At present, it is unclear whether the baseline ``universal cadence'' LSST sampling frequency of $\sim10$ days in a given filter and $\sim 4$ days on average across all filters \citep{LSSTSciBook,LSSTpaper} will enable sufficiently accurate time delay measurements, despite the long campaign length ($\sim10$ years). While ``follow up'' monitoring observations to supplement the LSST lightcurves may not be feasible at the 1000-lens sample scale, it may be possible to design a survey strategy that has a variety of cadences and campaign lengths (thus keeping the total number of exposures constant). {\bf (GGD: This last sentence is confusing... Do you mean, within LSST? Or follow up? And why is total number of exposures = constant useful?)} In order to maximize the capability of LSST to probe the universe through strong lensing time delays, we must understand the interaction between the time delay estimation algorithms and the anticipated data properties.  At present, it is unclear whether the baseline ``universal cadence'' LSST sampling frequency of $\sim10$ days in a given filter and $\sim 4$ days on average across all filters \citep{LSSTSciBook,LSSTpaper} will enable sufficiently accurate time delay measurements, despite the long campaign length ($\sim10$ years). While ``follow up'' monitoring observations to supplement the LSST lightcurves may not be feasible at the 1000-lens sample scale, it may be possible to design a survey strategy that optimizes cadence and monitoring at least for some fields. In order to maximize the capability of LSST to probe the universe through strong lensing time delays, we must understand the interaction between the time delay estimation algorithms and the anticipated data properties.   1  While optimizing the accuracy of LSST time delays is our long term objective, improving the present-day algorithms will benefit the current and planned lens monitoring projects as well. Exploring the impact of cadences and campaign lengths spanning the range between today's monitoring campaigns and that expected from a baseline LSST survey will allow us to simultaneously provide input to current projects looking to expand their sample sizes (and hence monitor more cheaply) as well as the LSST project, whose exact survey strategy is not yet decided.  The goal of this work then is to enable realistic estimates of feasible time delay measurement accuracy to be made with LSST. We will achieve this via a ``Time Delay Challenge'' (TDC) to the community. Independent, blind analysis of plausibly realistic LSST-like lightcurves will allow the accuracy of current time series analysis algorithms to be assessed which will lead to simple cosmographic forecasts for the anticipated LSST dataset. This work can be seen as a first step towards a full understanding of all systematic uncertainties present in the LSST strong lens dataset and will also provide valuable insight into the survey strategy needs of both Stage III and Stage IV time delay lens cosmography programs. Blind analysis, where the true value of the quantity being reconstructed is not known by the researchers, is a key tool for robustly testing the analysis procedure without biasing the results by continuing to look for errors until the right answer is reached, and then stopping.