896 research outputs found
Virtual Astronomy, Information Technology, and the New Scientific Methodology
All sciences, including astronomy, are now entering the era of information abundance. The exponentially increasing volume and complexity of modern data sets promises to transform the scientific practice, but also poses a number of common technological challenges. The Virtual Observatory concept is the astronomical community's response to these challenges: it aims to harness the progress in information technology in the service of astronomy, and at the same time provide a valuable testbed for information technology and applied computer science. Challenges broadly fall into two categories: data handling (or "data farming"), including issues such as archives, intelligent storage, databases, interoperability, fast networks, etc., and data mining, data understanding, and knowledge discovery, which include issues such as automated clustering and classification, multivariate correlation searches, pattern recognition, visualization in highly hyperdimensional parameter spaces, etc., as well as various applications of machine learning in these contexts. Such techniques are forming a methodological foundation for science with massive and complex data sets in general, and are likely to have a much broather impact on the modern society, commerce, information economy, security, etc. There is a powerful emerging synergy between the
computationally enabled science and the science-driven computing, which will drive the progress in science, scholarship, and many other venues in the 21st century
Some statistical and computational challenges, and opportunities in astronomy
The data complexity and volume of astronomical findings have increased in recent decades due to major technological improvements in instrumentation and data collection methods. The contemporary astronomer is flooded with terabytes of raw data that produce enormous multidimensional catalogs of objects (stars, galaxies, quasars, etc.) numbering in the billions, with hundreds of measured numbers for each object. The astronomical community thus faces a key task: to enable efficient and objective scientific exploitation of enormous multifaceted data sets and the complex links between data and astrophysical theory. In recognition of this task, the National Virtual Observatory (NVO) initiative recently emerged to federate numerous large digital sky archives, and to develop tools to explore and understand these vast volumes of data. The effective use of such integrated massive data sets presents a variety of new challenging statistical and algorithmic problems that require methodological advances. An interdisciplinary team of statisticians, astronomers and computer scientists from The Pennsylvania State University, California Institute of Technology and Carnegie Mellon University is developing statistical methodology for the NVO. A brief glimpse into the Virtual Observatory and the work of the Penn State-led team is provided here
Exploring the Time Domain With Synoptic Sky Surveys
Synoptic sky surveys are becoming the largest data generators in astronomy,
and they are opening a new research frontier, that touches essentially every
field of astronomy. Opening of the time domain to a systematic exploration will
strengthen our understanding of a number of interesting known phenomena, and
may lead to the discoveries of as yet unknown ones. We describe some lessons
learned over the past decade, and offer some ideas that may guide strategic
considerations in planning and execution of the future synoptic sky surveys.Comment: Invited talk, to appear in proc. IAU SYmp. 285, "New Horizons in Time
Domain Astronomy", eds. E. Griffin et al., Cambridge Univ. Press (2012).
Latex file, 6 pages, style files include
Data Driven Discovery in Astrophysics
We review some aspects of the current state of data-intensive astronomy, its
methods, and some outstanding data analysis challenges. Astronomy is at the
forefront of "big data" science, with exponentially growing data volumes and
data rates, and an ever-increasing complexity, now entering the Petascale
regime. Telescopes and observatories from both ground and space, covering a
full range of wavelengths, feed the data via processing pipelines into
dedicated archives, where they can be accessed for scientific analysis. Most of
the large archives are connected through the Virtual Observatory framework,
that provides interoperability standards and services, and effectively
constitutes a global data grid of astronomy. Making discoveries in this
overabundance of data requires applications of novel, machine learning tools.
We describe some of the recent examples of such applications.Comment: Keynote talk in the proceedings of ESA-ESRIN Conference: Big Data
from Space 2014, Frascati, Italy, November 12-14, 2014, 8 pages, 2 figure
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