Proc. SPIE. 9910, Observatory Operations: Strategies, Processes, and Systems VI
KEYWORDS: Observatories, Astronomy, Data modeling, Data modeling, Ultraviolet radiation, X-rays, Data archive systems, Data archive systems, Gamma radiation, Infrared telescopes, Field emission displays, Standards development
Since the turn of the millennium a constant concern of astronomical archives have begun providing data to the public through standardized protocols unifying data from disparate physical sources and wavebands across the electromagnetic spectrum into an astronomical virtual observatory (VO). In October 2014, NASA began support for the NASA Astronomical Virtual Observatories (NAVO) program to coordinate the efforts of NASA astronomy archives in providing data to users through implementation of protocols agreed within the International Virtual Observatory Alliance (IVOA). A major goal of the NAVO collaboration has been to step back from a piecemeal implementation of IVOA standards and define what the appropriate presence for the US and NASA astronomy archives in the VO should be. This includes evaluating what optional capabilities in the standards need to be supported, the specific versions of standards that should be used, and returning feedback to the IVOA, to support modifications as needed. <p> </p>We discuss a standard archive model developed by the NAVO for data archive presence in the virtual observatory built upon a consistent framework of standards defined by the IVOA. Our standard model provides for discovery of resources through the VO registries, access to observation and object data, downloads of image and spectral data and general access to archival datasets. It defines specific protocol versions, minimum capabilities, and all dependencies. The model will evolve as the capabilities of the virtual observatory and needs of the community change.
Operation of the US Virtual Astronomical Observatory shares some issues with modern physical observatories, e.g.,
intimidating data volumes and rapid technological change, and must also address unique concerns like the lack of direct
control of the underlying and scattered data resources, and the distributed nature of the observatory itself. In this paper
we discuss how the VAO has addressed these challenges to provide the astronomical community with a coherent set of
science-enabling tools and services. The distributed nature of our virtual observatory-with data and personnel
spanning geographic, institutional and regime boundaries-is simultaneously a major operational headache and the
primary science motivation for the VAO. Most astronomy today uses data from many resources. Facilitation of
matching heterogeneous datasets is a fundamental reason for the virtual observatory. Key aspects of our approach
include continuous monitoring and validation of VAO and VO services and the datasets provided by the community,
monitoring of user requests to optimize access, caching for large datasets, and providing distributed storage services that
allow user to collect results near large data repositories. Some elements are now fully implemented, while others are
planned for subsequent years. The distributed nature of the VAO requires careful attention to what can be a
straightforward operation at a conventional observatory, e.g., the organization of the web site or the collection and
combined analysis of logs. Many of these strategies use and extend protocols developed by the international virtual
observatory community. Our long-term challenge is working with the underlying data providers to ensure high quality
implementation of VO data access protocols (new and better 'telescopes'), assisting astronomical developers to build
robust integrating tools (new 'instruments'), and coordinating with the research community to maximize the science
Broad support for Virtual Observatory (VO) standards by astronomical archives is critical for the success of the
VO as a research platform. Indeed, a number of effective data discovery, visualization, and integration tools
have been created which rely on this broad support. Thus, to an archive, the motivation for supporting VO
standards is strong. However, we are now seeing a growing trend among archive developers towards leveraging
VO standards and technologies not just to provide interoperability with the VO, but also to support an archive's
internal needs and the needs of the archive's primary user base. We examine the motivation for choosing VO
technologies for implementing an archive's functionality and list several current examples, including from the
Hubble Legacy Archive, NASA HEASARC, NOAO, and NRAO. We will also speculate on the effect that VO
will have on some of the ambitious observatory projects planned for the near future.
Hera is a new experiment at the HEASARC (High Energy Astrophysics Science Archive Research Center) at the NASA Goddard Space Flight Center to provide a complete data analysis environment over the Internet for archival researchers. This new facility complements the existing Browse database search facility that is available on the Web. With Hera, users can search the HEASARC data archives with a Web browser and save any selected data set to their Hera disk space area. This only takes a few seconds compared to the many minutes or hours that it could take to down load large data sets to the user's local machine. The user can then immediately log into one of
the available Hera server machines and begin analyzing the data without having to install any local software except for a very small Hera client application program that runs on the user's local machine. Hera is currently most useful for expert users who are already familiar with analyzing high energy data sets with the HEASARC software. In the future we intend to make Hera more useful for the novice scientific user by providing more on-line help features to guide the user through the data analysis process.
Building an automated classifier for high-energy sources provides an opportunity to prototype approaches to building the Virtual Observatory with a substantial immediate scientific return. The ClassX collaboration is combining existing data resources with trainable classifiers to build a tool that classifies lists of objects presented to it. In our first year the collaboration has concentrated on developing pipeline software that finds and combines information of interest and in exploring the issues that will be needed for successful classification.
ClassX must deal with many key VO issues: automating access to remote data resources, combining heterogeneous data and dealing with large data volumes. While the VO must attempt to deal with these problems in a generic way, the clear science goals of ClassX allow us to act as a pathfinder exploring particular approaches to addressing these issues.