Knowledge Discovery in Mega-Spectra Archives

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Knowledge Discovery in Mega-Spectra Archives

Popis výsledku v původním jazyce

The recent progress of astronomical instrumentation resulted in the construction of multi-object spectrographs with hundreds to thousands of micro-slits or optical fibres allowing the acquisition of tens of thousands of spectra of celestial objects per observing night. Currently there are two spectroscopic surveys containing millions of spectra. These surveys are being processed by automatic pipelines, spectrum by spectrum, in order to estimate physical parameters of individual objects resulting in extensive catalogues, used typically to construct the better models of space-kinematic structure and evolution of the Universe or its subsystems. Such surveys are, however, very good source of homogenised, pre-processed data for application of machine learning techniques common in Astroinformatics. We present challenges of knowledge discovery in such surveys as well as practical examples of machine learning based on specific shapes of spectral features used in searching for new candidates of interesting astronomical objects, namely Be and B [e] stars and quasars.

Název v anglickém jazyce

Knowledge Discovery in Mega-Spectra Archives

Popis výsledku anglicky

The recent progress of astronomical instrumentation resulted in the construction of multi-object spectrographs with hundreds to thousands of micro-slits or optical fibres allowing the acquisition of tens of thousands of spectra of celestial objects per observing night. Currently there are two spectroscopic surveys containing millions of spectra. These surveys are being processed by automatic pipelines, spectrum by spectrum, in order to estimate physical parameters of individual objects resulting in extensive catalogues, used typically to construct the better models of space-kinematic structure and evolution of the Universe or its subsystems. Such surveys are, however, very good source of homogenised, pre-processed data for application of machine learning techniques common in Astroinformatics. We present challenges of knowledge discovery in such surveys as well as practical examples of machine learning based on specific shapes of spectral features used in searching for new candidates of interesting astronomical objects, namely Be and B [e] stars and quasars.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS: XXIV

ISBN

9781583818756

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

87-90

Název nakladatele

ASTRONOMICAL SOC PACIFIC

Místo vydání

SAN FRANCISCO

Místo konání akce

Univ Calgary, Calgary, CANADA

Datum konání akce

5. 10. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000371098000016