Publication Details
Knowledge Discovery in Mega-Spectra Archives
PALIČKA, A.
Škoda Petr, RNDr., Ph.D.
Vážný Jaroslav
Vrábelová Pavla, Ing.
multi-object spectrographs, machine learning techniques, astroinformatics
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.
@inproceedings{BUT163431,
author="LOPATOVSKÝ, L. and PALIČKA, A. and ŠKODA, P. and VÁŽNÝ, J. and VRÁBELOVÁ, P.",
title="Knowledge Discovery in Mega-Spectra Archives",
booktitle="ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS: XXIV",
year="2014",
series="Astronomical Society of the Pacific Conference Series",
pages="87--90",
publisher="Astronomical Society of the Pacific",
address="Calgary",
isbn="978-1-58381-874-9",
url="http://www.gothard.hu/gao-mkk/memorabilia/bigdataconf-2014/proceedings/pdf/BigDataConf-proceedings.021-026.pdf"
}