We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg 2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320-1050 nm. The project is in the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg 2 region about 800 times (summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r∼27.5. These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system parameters, and we describe the expected data products and their characteristics.

Very large data sets often have a flat but regular structure and span multiple disks and machines. Examples include telephone call records, network logs, and web document repositories. These large data sets are not amenable to study using traditional database techniques, if only because they can be too large to fit in a single relational database. On the other hand, many of the analyses done on them can be expressed using simple, easily distributed computations: filtering, aggregation, extraction of statistics, and so on. We present a system for automating such analyses. A filtering phase, in which a query is expressed using a new procedural programming language, emits data to an aggregation phase. Both phases are distributed over hundreds or even thousands of computers. The results are then collated and saved to a file. The design – including the separation into two phases, the form of the programming language, and the properties of the aggregators – exploits the parallelism inherent in having data and computation distributed across many machines.

We compare the peculiar velocity field within 65 h À1 Mpc predicted from 2MASS photometry and public redshift data to three independent peculiar velocity surveys based on Type Ia supernovae, surface brightness fluctuations in elliptical galaxies, and Tully-Fisher distances to spiral galaxies. The three peculiar velocity samples are each in good agreement with the predicted velocities and produce consistent results for K ¼ 0:6 m /b K . Taken together, the best-fit K ¼ 0:49 AE 0:04. We explore the effects of morphology on the determination of by splitting the 2MASS sample into E+S0 and S+Irr density fields and find that both samples are equally good tracers of the underlying dark matter distribution, but that early types are more clustered, by a relative factor b E /b S $ 1:6. The density fluctuations of 2MASS galaxies in 8 h À1 Mpc spheres in the local volume is found to be 8;K ¼ 0:9. From this result and our value of K , we find 8 ( m /0:3) 0:6 ¼ 0:91 AE 0:12. This is in excellent agreement with results from the IRAS redshift surveys, as well as other cosmological probes. Combining the 2MASS and IRAS peculiar velocity results yields 8 ( m /0:3) 0:6 ¼ 0:85 AE 0:05. Subject headingg : large-scale structure of universe

No abstract

Released as open source in November 2009, Go has become the foundation for critical infrastructure at every major cloud provider. Its creators look back on how Go got here and why it has stuck around.