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| META TOPICPARENT |
name="InterOpApr2022" |
Knowledge Discovery 1 |
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<
< | Time: Thursday Apr 28 22:00 UTC |
>
> | Time: Thursday Apr 28 22:30 UTC |
| |
| Raffaele D'Abrusco |
Introduction |
5' |
|
| Rafael Martinez Galarza |
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15' |
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<
< |
| Petr Skoda |
The Role of VO Technology in Astronomical Machine
Learning |
15' |
The VO infrastructure has already increased the efficiency of
traditional astronomical data analysis in many ways. We try to identify
the points where the IVOA standards (current or after some modifications)
can make easier the preparation of machine learning experiments or even
introduce new KDD methodology. |
| Yihan Tao |
Classification of Galaxy Spectra based on Convolutional Neural
Network |
15' |
It is important to classify galaxies by their spectral data
in astronomy. The widely used spectral classification method for galaxy
is the BPT diagram, which classifies emission line galaxies by flux
ratios of the Balmer and forbidden lines. A convolutional neural network
(CNN) is a deep learning algorithm which achieves outstanding
performance on feature extraction and classification tasks. We build a
one-dimensional CNN model to classify galaxy spectra into star-forming,
composite, AGN, and normal galaxies. We preprocessed the galaxy spectra
selected from SDSS DR16 with spectral interval constraint and flux
standardization and conducted experiments with the preprocessed data.
The dataset labels are derived from the intersection of the BPT
classification given by MPA-JHU and Portsmouth catalog. The results
showed that the classification accuracy of our network was over 92%
without relying on redshifts. |
|
>
> |
| Petr Skoda |
The Role of VO Technology in Astronomical Machine
Learning |
15' |
The VO infrastructure has already increased the efficiency of traditional astronomical data analysis in many ways. We try to identify the points where the IVOA standards (current or after some modifications) can make easier the preparation of machine learning experiments or even
introduce new KDD methodology. |
| Yihan Tao |
Classification of Galaxy Spectra based on Convolutional Neural Network |
15' |
It is important to classify galaxies by their spectral data in astronomy. The widely used spectral classification method for galaxy is the BPT diagram, which classifies emission line galaxies by flux ratios of the Balmer and forbidden lines. A convolutional neural network (CNN) is a deep learning algorithm which achieves outstanding performance on feature extraction and classification tasks. We build a one-dimensional CNN model to classify galaxy spectra into star-forming, composite, AGN, and normal galaxies. We preprocessed the galaxy spectra selected from SDSS DR16 with spectral interval constraint and flux standardization and conducted experiments with the preprocessed data. The dataset labels are derived from the intersection of the BPT classification given by MPA-JHU and Portsmouth catalog. The results showed that the classification accuracy of our network was over 92% without relying on redshifts. |
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Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link |
