Detail publikace

Knowledge Discovery in Mega-Spectra Archives

Originální název

Knowledge Discovery in Mega-Spectra Archives

Anglický název

Knowledge Discovery in Mega-Spectra Archives

Jazyk

en

Originální abstrakt

The recent progress of astronomical instrumentation resulted in the constructionof multi-object spectrographs with hundreds to thousands of micro-slits or opticalfibers allowing the acquisition of tens of thousands of spectra of celestial objectsper observing night. Currently there are several spectroscopic surveys containingmillions of spectra and much larger are in preparation. Most of the large-scalesurveys are processed spectrum by spectrum in order to estimate physical param-eters of individual objects. The parameters obtained are then used to constructthe better models of space-kinematic structure and evolution of the Universe orits subsystems. Such surveys are, however, very good source of homogenized, pre-processed data for application of machine learning techniques and advanced statis-tical processing common in Astroinformatics. We present challenges of knowledgediscovery process applied to large spectroscopic surveys as well as memory spaceand processing speed demands of current machine learning methods, requiring BigData techniques.

Anglický abstrakt

The recent progress of astronomical instrumentation resulted in the constructionof multi-object spectrographs with hundreds to thousands of micro-slits or opticalfibers allowing the acquisition of tens of thousands of spectra of celestial objectsper observing night. Currently there are several spectroscopic surveys containingmillions of spectra and much larger are in preparation. Most of the large-scalesurveys are processed spectrum by spectrum in order to estimate physical param-eters of individual objects. The parameters obtained are then used to constructthe better models of space-kinematic structure and evolution of the Universe orits subsystems. Such surveys are, however, very good source of homogenized, pre-processed data for application of machine learning techniques and advanced statis-tical processing common in Astroinformatics. We present challenges of knowledgediscovery process applied to large spectroscopic surveys as well as memory spaceand processing speed demands of current machine learning methods, requiring BigData techniques.

BibTex


@inproceedings{BUT163431,
  author="Petr {Škoda} and Jaroslav {Vážný} and Pavla {Vrábelová}",
  title="Knowledge Discovery in Mega-Spectra Archives",
  annote="The recent progress of astronomical instrumentation resulted in the
constructionof multi-object spectrographs with hundreds to thousands of
micro-slits or opticalfibers allowing the acquisition of tens of thousands of
spectra of celestial objectsper observing night. Currently there are several
spectroscopic surveys containingmillions of spectra and much larger are in
preparation. Most of the large-scalesurveys are processed spectrum by spectrum in
order to estimate physical param-eters of individual objects. The parameters
obtained are then used to constructthe better models of space-kinematic structure
and evolution of the Universe orits subsystems. Such surveys are, however, very
good source of homogenized, pre-processed data for application of machine
learning techniques and advanced statis-tical processing common in
Astroinformatics. We present challenges of knowledgediscovery process applied to
large spectroscopic surveys as well as memory spaceand processing speed demands
of current machine learning methods, requiring BigData techniques.",
  address="Astronomical Society of the Pacific",
  booktitle="ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS: XXIV",
  chapter="163431",
  edition="Astronomical Society of the Pacific Conference Series",
  howpublished="print",
  institution="Astronomical Society of the Pacific",
  year="2014",
  month="december",
  pages="87--90",
  publisher="Astronomical Society of the Pacific",
  type="conference paper"
}