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| Paper: |
Knowledge Discovery in Mega-Spectra Archives |
| Volume: |
495, Astronomical Data Analysis Software and Systems XXIV (ADASS XXIV) |
| Page: |
87 |
| Authors: |
Škoda, P.; Bromová, P.; Lopatovsk'y, L.; Palička, A.; Vávzný, J. |
| Abstract: |
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. |
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