This is the gateway for information about the Operations
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Simulator
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including documentation
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on how the simulator works, how to install and run the software, downloading simulated survey data and viewing the analysis.
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Operations SimulatorThe Simulator is capable of balancing cadence goals from multiple science programs, and attempts to minimize time spent slewing as it carries out these goals. Ten years of LSST operations can be simulated using realistic seeing distributions, historical weather data, scheduled engineering downtime and current telescope and camera parameters. The Simulator has a sophisticated model of the telescope and dome to properly constrain potential observing cadences. This model has also proven useful for investigating various engineering issues ranging from sizing of slew motors, to design of cryogen lines to the camera. The Simulator is capable of balancing cadence goals fromThe LSST Project developed the Operations Simulator to verify that the LSST Science Requirements could be met with the telescope design. It was used to demonstrated the capability of the LSST to deliver a 27,000 square degree survey probing the time domain and with 20,000 square degrees for the Wide-Fast-Deep survey, while effectively surveying for NEOs over the same area. Currently, the Operations Simulation Team is investigating how to optimally observe the sky to obtain a single 10-year dataset that can be used to accomplish multiple science goals. Operations SimulatorThe Simulator has a sophisticated model of the telescope and dome to properly constrain potential observing cadences. This model has also proven useful for investigating various engineering issues ranging from sizing of slew motors, to design of cryogen lines to the camera. multiple science programs, and attempts to minimize time spent slewing as it carries out thesegoals. Ten years of LSST operations can be simulated using realistic seeing distributions, historical weather data, scheduled engineering downtime and current telescope and camera parameters.
Achievements
Future Work
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SoftwareThe LSST Operations Simulator a software tool is an application created primarily with an open-source simulation package--SimPy. SimPy is an object-oriented, process-based, discrete-event simulation package based on standard Python and released under the GNU GPL. Modeling the Telescope and the SkyThe simulator uses a sophisticated model of the sky. It computes the sky brightness using the Krisciunas and Schaeffer (1991) model, and it tracks the positions of the Sun and the Moon using SLALIB routines. A detailed telescope model tracks the movements of all the components: mount, dome, optics, instrument rotator, cable wraps and filter changer. The velocities and accelerations for these motions are all settable parameters. There are open-loop optics alignments for all moves and closed-loop alignments for moves in altitude greater than a settable parameter (currently 9 degrees).The telescope model is used to calculate a penalty for the time it takes to slew to a proposed next field which factors into the scheduling decision-making process. The simulator employs models for atmospheric seeing and cloud conditions. Available DIMM data has been used to determine the power spectrum of the seeing throughout the year, and a model data sets having that power spectrum was generated. The cloud model is derived from 10 years of nightly observations of the sky by the CTIO night assistant. The simulator currently assumes an alternating one week and two week shutdown per year for scheduled maintenance, and can generate random periods of downtime. Observing ModesThe simulator is modular in design and can accept multiple, distinct observing modes which are used to specify the observing requirements for the science programs. Each observing mode ranks potential observations based upon user-specified parameters, which control the hard-coded algorithms. Rankings are evaluated using the current seeing, sky brightness, and progress towards completing an observing mode based on previous observations. For observing modes with a specified cadence, the algorithm increases rankings for observations useful for that cadence. Currently, the algorithm does not look ahead to determine future events such as when a field will set, when the sun will rise, or if the field will otherwise become unavailable in the middle of a sequence. Before an observation is scheduled, each of the observing modes rank potential target fields according to criteria such as timing, sky brightness, seeing, airmass, and progress toward survey goals. These rankings are then merged, penalties are applied for slew time, filter change times and other operational considerations, and ranked again. A visit is made to the best field, and the process repeats. We have found that surveys using the first four of these observing modes are sufficient to ensure meeting the Science Requirement Document goals. The fifth mode is representative of a mini-survey or target of opportunity observing.
The number of visits obtained in each field in the r-filter for the first year of a survey is indicated by the shaded areas. Each of the areas of interest (labeled) has a specific cadence definition. It should be noted that this is the spatial distribution of the number of visits in the first year of a survey, and will not be as uniform as for the full 10-year survey. |
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Once a simulated survey is planned, designed and executed, it is useful to evaluate whether that particular survey met the LSST project science goals. Quantifying how well a simulated survey achieves a science objective or whether one simulation is "better than" another is a complex and open-ended problem. A software tool The Metrics Analysis Framework (MAF) has been created which executes a series of queries on the simulated survey history and creates a printable standard report that contains statistics, distributions, and sky maps designed to characterize the survey. This set of analyses is by no means comprehensive because of the broad range of science the survey enables. The standard report is a useful initial characterization of a simulated survey and contains analyses which compare to the design and stretch specifications from the SRD. To more fully assess how well a survey meets a particular science goal, the development of a variety of scientific figures of merit is needed. Also, the process of making sense of the data requires the ability to explore and analyze it in an interactive way, and to communicate and collaborate about the results. to to make it easy to read the simulation database, calculate sets of metrics, and visualize the results. This tool has been designed so you can easily write your own metrics to evaluate the OpSim pointing history for your own science. We To this end we are
Here This is an example of a diagnostic plot produced in the standard report. An by the Metrics Analysis Framework. This is an inventory of the time spent observing during the night color-coded by filter for a 10-year survey. The enclosing curves indicate the time of civil (−6°), nautical (−12°), and astronomical (−18°) twilight. Note that only z- and y-filters are used between astronomical and nautical twilight. The Moon’s illumination (in percent) is indicated by the arbitrarily scaled white curve at the bottom of the plot.
This is an example of a science metric. It calculates how well a particular simulated survey can recover sources that vary on a particular timescale. The blue area in the right hand plot shows that objects which vary on periods of 2-10 days can be recovered quite well. |
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How to install and run the Operations /Observations Simulator codebaseSimulator Summary Table Column Descriptions A Set of Simulated Surveys for LSST2015 (Aug 2015) - Data and Analysis |