See the schedule for presentation times.
All focus demos will be presented in the Illinois Ballroom.
F2: Filtergraph: A Flexible Web Application for Instant Data Visualization of Astronomy Datasets
Filtergraph is a web application being developed by the Vanderbilt Initiative in Data-Intensive Astrophysics (VIDA) to flexibly handle a large variety of astronomy datasets. While current datasets at Vanderbilt are being used to search for eclipsing binaries and extrasolar planets, this system can be easily reconfigured for a wide variety of data sources. The user loads an ASCII dataset into Filtergraph which instantly generates an interactive data portal that can be easily shared with others. From this portal, the user can immediately generate scatter plots, histograms, and tables based on the dataset. Key features of the portal include the ability to filter the data in real time through user-specified criteria, the ability to select data by dragging on the screen, and the ability to perform arithmetic operations on the data in real time. The application is being optimized for speed in the context of very large datasets: for
instance, operations on a stellar database for 3.1 million entries can execute in less than 2 seconds on a standard web server platform. This web application has been created using the Web2py web framework based on the Python programming language.
|Burger, Dan M. || Vanderbilt University|
|Pepper, Joshua A. || Vanderbilt University|
|Siverd, Robert J. || Vanderbilt University|
|Stassun, Keivan G. || Vanderbilt University|
|Paegert, Martin A. || Vanderbilt University|
|De Lee, Nathan M. || Vanderbilt University|
F3: VO Desktop Tools: IRAF, Command-line Tasks and Python
The VOClient package is being developed by the VAO to provide a suite of client-side interfaces needed to build new VO-aware applications and integrate legacy systems such as IRAF and CASA. VOClient is C-based and can be used to provide multi-language interfaces via SWIG, however a custom 'Pythonic' interface is also being developed and packaged to make VOClient a standard Python module. This package has been used to successfully integrate VO features into IRAF and to build a growing set of high level command-line (CLI) tools that can be used from a variety of scripting languages. Beyond VO data access, VOClient provides tools to enable SAMP (i.e. desktop) messaging between applications, offers access to VO compute services such as cross-match and name resolution, and features are in development for VO cloud storage (VOSpace) and advanced data-access protocols.
|Fitzpatrick, Michael || NOAO|
|Tody, Doug || NRAO|
|Young, Wes || NRAO|
|Graham, Matthew || Caltech|
This focus demo will introduce the capabilities of the package with examples of its use within IRAF and the CLI tools. We hope to also have a discussion of the Python features being developed, what features are important to the ADASS community, and how VOClient might be used by other projects.
Astronomers mainly use the web for data retrieval. To create visualizations and conduct analyses requires installation of many external packages, often creating a difficult task for the astronomer. An ideal situation would move many of the common tasks to a browser - a homogenous solution for data access coupled with visualization and analyses in one application.
|Kapadia, Amit || Adler Planetarium / Zooniverse|
|Smith, Arfon || Adler Planetarium / Zooniverse|
CANFAR + Skytree: A Cloud Computing and Data Mining System for Astronomy
To-date, computing systems have allowed either sophisticated analysis of small datasets, as exemplified by most astronomy software, or simple analysis of large datasets, such as database queries. At the Canadian Astronomy Data Centre, we have combined our cloud computing system, the Canadian Advanced Network for Astronomical Research (CANFAR), with the world's most advanced machine learning software, Skytree, to create the world's first cloud computing system for data mining in astronomy. CANFAR provides a generic environment for the storage and processing of large datasets, removing the requirement for an individual or project to set up and maintain a computing system when implementing an extensive undertaking such as a survey pipeline. 500 processor cores and several hundred terabytes of persistent storage are currently available to users, and both the storage and processing infrastructure are expandable. The storage is implemented via the International Virtual Observatory Alliance's VOSpace protocol, and is available as a mounted filesystem accessible both interactively, and to all processing jobs. The user interacts with CANFAR by utilizing virtual machines, which appear to them as equivalent to a desktop. Each machine is replicated as desired to perform large-scale parallel processing. Such an arrangement carries far more flexibility than other cloud systems, because it enables the user to immediately install and run the same astronomy code that they already utilize, in the same way as on a desktop. Skytree is installed and run just as any other software on the system, and thus acts as a library of command line data mining functions that can be integrated into one's wider analysis. Thus we have created a generic environment for large-scale analysis by data mining, in the same way that CANFAR itself has done for storage and processing. Because Skytree scales to large data in linear runtime, this allows the full sophistication of the huge fields of data mining and machine learning to be applied to the hundreds of millions of objects that make up current large datasets. We demonstrate the utility of the CANFAR + Skytree system by showing science results obtained, including assigning photometric redshifts to the MegaPipe reductions of the Canada-France-Hawaii Telescope Legacy Wide and Deep surveys. This project involves producing, handling, and running data mining on, a catalog of over 13 billion object instances. This is comparable in size to those expected from next-generation surveys, such as the Large Synoptic Survey Telescope. The CANFAR + Skytree system is open for use by any interested member of the astronomical community.
|Ball, Nicholas || National Research Council Canada|