1,161 research outputs found
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
Some Pattern Recognition Challenges in Data-Intensive Astronomy
We review some of the recent developments and challenges posed by the data
analysis in modern digital sky surveys, which are representative of the
information-rich astronomy in the context of Virtual Observatory. Illustrative
examples include the problems of an automated star-galaxy classification in
complex and heterogeneous panoramic imaging data sets, and an automated,
iterative, dynamical classification of transient events detected in synoptic
sky surveys. These problems offer good opportunities for productive
collaborations between astronomers and applied computer scientists and
statisticians, and are representative of the kind of challenges now present in
all data-intensive fields. We discuss briefly some emergent types of scalable
scientific data analysis systems with a broad applicability.Comment: 8 pages, compressed pdf file, figures downgraded in quality in order
to match the arXiv size limi
Connecting the time domain community with the Virtual Astronomical Observatory
The time domain has been identified as one of the most important areas of
astronomical research for the next decade. The Virtual Observatory is in the
vanguard with dedicated tools and services that enable and facilitate the
discovery, dissemination and analysis of time domain data. These range in scope
from rapid notifications of time-critical astronomical transients to annotating
long-term variables with the latest modeling results. In this paper, we will
review the prior art in these areas and focus on the capabilities that the VAO
is bringing to bear in support of time domain science. In particular, we will
focus on the issues involved with the heterogeneous collections of (ancillary)
data associated with astronomical transients, and the time series
characterization and classification tools required by the next generation of
sky surveys, such as LSST and SKA.Comment: Submitted to Proceedings of SPIE Observatory Operations: Strategies,
Processes and Systems IV, Amsterdam, 2012 July 2-
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
Grids and the Virtual Observatory
We consider several projects from astronomy that benefit from the Grid paradigm and
associated technology, many of which involve either massive datasets or the federation
of multiple datasets. We cover image computation (mosaicking, multi-wavelength
images, and synoptic surveys); database computation (representation through XML,
data mining, and visualization); and semantic interoperability (publishing, ontologies,
directories, and service descriptions)
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
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