Opening a Window of Discovery on the Dynamic Universe

Metrics Analysis Framework (MAF)

A sky map of the coadded depth in the r band after 10 years of simulated observations.

The Metrics Analysis Framework (MAF) is an open-source python framework developed to provide a user-friendly, customizable, easily-extensible set of tools for analyzing data sets. Its initial goal is to provide a tool to evaluate Operations Simulator (OpSim) simulated surveys to help understand the effects of telescope scheduling on survey performance, however, MAF can be applied to a much wider range of datasets. The building blocks of the framework are Metrics  (algorithms to analyze a given quantity of data), Slicers  (subdividing the overall data set into smaller data slices as relevant for each Metric ), and Database classes (to access the dataset and read data into memory).

The science performance of the LSST will in part, be driven by how it observes the sky as characterized by the footprint and cadence of the survey. Efforts are currently underway to train astronomers in the tools available to simulate the properties of the LSST; to revisit the science drivers for various LSST programs with an eye towards optimization of the baseline observing cadence; and to develop metrics that might be used to quantify the science performance of the LSST as a function of its cadence. Examples where further cadence optimization is likely to yield tangible improvements in the LSST science include the coverage of the Galactic plane, characterization of supernovae light curves, the reduction of systematics through dithering, deep drilling fields, and active area vs. sampling rate tradeoffs (also known as “rolling cadence”).

To install and use MAF
  • The codebase for MAF is accessible through Github
  • Tutorials for running and writing metrics are available here
  • MAF installation instructions are available here
  • To contribute your own metrics or see metrics others have written we have a Github repository that includes example metrics written as ipython notebooks (create a Github account and fork the repository)

For more details of MAF or if you have questions, they can be addressed using community.lsst.org.

 

Financial support for LSST comes from the National Science Foundation (NSF) through Cooperative Agreement No. 1258333, the Department of Energy (DOE) Office of Science under Contract No. DE-AC02-76SF00515, and private funding raised by the LSST Corporation. The NSF-funded LSST Project Office for construction was established as an operating center under management of the Association of Universities for Research in Astronomy (AURA).  The DOE-funded effort to build the LSST camera is managed by the SLAC National Accelerator Laboratory (SLAC).
The National Science Foundation (NSF) is an independent federal agency created by Congress in 1950 to promote the progress of science. NSF supports basic research and people to create knowledge that transforms the future.   




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