ABSTRACT: The time delays between point-like images in gravitational lens systems can be used to measure cosmological parameters as well as probe the dark matter (sub-)structure within the lens galaxy. The number of lenses with measuring time delays is growing rapidly due to dedicated efforts. In the near future, the upcoming _Large Synoptic Survey Telescope_ (LSST), will monitor ∼10³ lens systems consisting of a foreground elliptical galaxy producing multiple images of a background quasar. In an effort to assess the present capabilities of the community to accurately measure the time delays in strong gravitational lens systems, and to provide input to dedicated monitoring campaigns and future LSST cosmology feasibility studies, we pose a “Time Delay Challenge” (TDC). The challenge is organized as a set of “ladders,” each containing a group of simulated datasets to be analyzed blindly by participating independent analysis teams. Each rung on a ladder consists of a set of realistic mock observed lensed quasar light curves, with the rungs’ datasets increasing in complexity and realism to incorporate a variety of anticipated physical and experimental effects. The initial challenge described here has two ladders, TDC0 and TDC1. TDC0 has a small number of datasets, and is designed to be used as a practice set by the participating teams as they set up their analysis pipelines. The non mondatory deadline for completion of TDC0 will be December 1 2013. The teams that perform sufficiently well on TDC0 will then be able to participate in the much more demanding TDC1. TDC1 will consists of 10³ lightcurves, a sample designed to provide the statistical power to make meaningful statements about the sub-percent accuracy that will be required to provide competitive Dark Energy constraints in the LSST era. In this paper we describe the simulated datasets in general terms, lay out the structure of the challenge and define a minimal set of metrics that will be used to quantify the goodness-of-fit, efficiency, precision, and accuracy of the algorithms. The results for TDC1 from the participating teams will be presented in a companion paper to be submitted after the closing of TDC1, with all TDC1 participants as co-authors.
As part of the urban metabolism, city buildings consume resources and use energy, producing environmental impacts on the surrounding air by emitting plumes of pollution. Plumes that have been observed in Manhattan range from water vapor emitted from heating and cooling systems’ steam vents to CO2 and dangerous chemical compounds (e.g. ammonia, methane). City agencies are interested in detecting and tracking these plumes as they provide evidence for signs of urban activity, cultivation of living and working spaces and can support the provision of services whilst monitoring environmental impacts. The Urban Observatory at New York University’s Center for Urban Science and Progress (CUSP-UO) continuously images the Manhattan skyline at 0.1 Hz, and day-time images can be used to detect and characterize plumes from buildings in the scene. This project built and trained a deep convolutional neural network for detection and tracking of these plumes in near real-time. The project created a large training set of over 1,100 actual plumes as well as sources of contamination such as clouds, shadows and lights, and applied the relevant network architecture for training of the model. The trained convolutional neural network was applied to the archival Urban Observatory data between two time periods: 26th October-31st December 2013 and 1st January-13th March 2015 to generate detections of building plume activity during those time periods. Buildings with high plume ejection rates were identified, and all plumes could be classified by their color (i.e. carbon vs water vapor). The final result was a detection of plumes emitted during the time periods that the dataset spans.