13,960 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
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
High-energy Astrophysics and the Virtual Observatory
The Virtual Observatory (VO) will revolutionise the way we do Astronomy by
allowing easy access to all astronomical data and by making the handling and
analysis of datasets at various locations across the globe much simpler and
faster. I report here on the need for the VO and its status in Europe,
concentrating on the recently started EURO-VO project, and then give two
specific applications of VO tools to high-energy astrophysics.Comment: 12 pages, 3 figures, invited talk at the Workshop ``Multifrequency
Behaviour of High Energy Cosmic Sources'', Vulcano, Italy, May 2005, F.
Giovannelli et al., in pres
Exploration of Parameter Spaces in a Virtual Observatory
Like every other field of intellectual endeavor, astronomy is being
revolutionised by the advances in information technology. There is an ongoing
exponential growth in the volume, quality, and complexity of astronomical data
sets, mainly through large digital sky surveys and archives. The Virtual
Observatory (VO) concept represents a scientific and technological framework
needed to cope with this data flood. Systematic exploration of the observable
parameter spaces, covered by large digital sky surveys spanning a range of
wavelengths, will be one of the primary modes of research with a VO. This is
where the truly new discoveries will be made, and new insights be gained about
the already known astronomical objects and phenomena. We review some of the
methodological challenges posed by the analysis of large and complex data sets
expected in the VO-based research. The challenges are driven both by the size
and the complexity of the data sets (billions of data vectors in parameter
spaces of tens or hundreds of dimensions), by the heterogeneity of the data and
measurement errors, including differences in basic survey parameters for the
federated data sets (e.g., in the positional accuracy and resolution,
wavelength coverage, time baseline, etc.), various selection effects, as well
as the intrinsic clustering properties (functional form, topology) of the data
distributions in the parameter spaces of observed attributes. Answering these
challenges will require substantial collaborative efforts and partnerships
between astronomers, computer scientists, and statisticians.Comment: Invited review, 10 pages, Latex file with 4 eps figures, style files
included. To appear in Proc. SPIE, v. 4477 (2001
Mining Knowledge in Astrophysical Massive Data Sets
Modern scientific data mainly consist of huge datasets gathered by a very
large number of techniques and stored in very diversified and often
incompatible data repositories. More in general, in the e-science environment,
it is considered as a critical and urgent requirement to integrate services
across distributed, heterogeneous, dynamic "virtual organizations" formed by
different resources within a single enterprise. In the last decade, Astronomy
has become an immensely data rich field due to the evolution of detectors
(plates to digital to mosaics), telescopes and space instruments. The Virtual
Observatory approach consists into the federation under common standards of all
astronomical archives available worldwide, as well as data analysis, data
mining and data exploration applications. The main drive behind such effort
being that once the infrastructure will be completed, it will allow a new type
of multi-wavelength, multi-epoch science which can only be barely imagined.
Data Mining, or Knowledge Discovery in Databases, while being the main
methodology to extract the scientific information contained in such MDS
(Massive Data Sets), poses crucial problems since it has to orchestrate complex
problems posed by transparent access to different computing environments,
scalability of algorithms, reusability of resources, etc. In the present paper
we summarize the present status of the MDS in the Virtual Observatory and what
is currently done and planned to bring advanced Data Mining methodologies in
the case of the DAME (DAta Mining & Exploration) project.Comment: Pages 845-849 1rs International Conference on Frontiers in
Diagnostics Technologie
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
Iris: an Extensible Application for Building and Analyzing Spectral Energy Distributions
Iris is an extensible application that provides astronomers with a
user-friendly interface capable of ingesting broad-band data from many
different sources in order to build, explore, and model spectral energy
distributions (SEDs). Iris takes advantage of the standards defined by the
International Virtual Observatory Alliance, but hides the technicalities of
such standards by implementing different layers of abstraction on top of them.
Such intermediate layers provide hooks that users and developers can exploit in
order to extend the capabilities provided by Iris. For instance, custom Python
models can be combined in arbitrary ways with the Iris built-in models or with
other custom functions. As such, Iris offers a platform for the development and
integration of SED data, services, and applications, either from the user's
system or from the web. In this paper we describe the built-in features
provided by Iris for building and analyzing SEDs. We also explore in some
detail the Iris framework and software development kit, showing how astronomers
and software developers can plug their code into an integrated SED analysis
environment.Comment: 18 pages, 8 figures, accepted for publication in Astronomy &
Computin
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