(r5) InterOpApr2022KDIG < IVOA

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---++ Knowledge Discovery 1

Time: [[https://www.timeanddate.com/worldclock/fixedtime.html?msg=KD&iso=20220428T2230][Thursday Apr 28 22:30 UTC]]

| <p> _Raffaele D'Abrusco_ </p> | <p> *Introduction* </p> | 5' | |
| <p>Rafael Martinez Galarza</p> | | 15' | |
| _Petr Skoda_ | <p>The Role of VO Technology in Astronomical Machine<br />Learning</p> | 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<br />introduce new KDD methodology. |
| _Yihan Tao_ | <p>Classification of Galaxy Spectra based on Convolutional Neural Network</p> | 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. |

Moderator: [[Raffaele D'Abrusco][Raffaele D'Abrusco]], Notetaker: [[IVOA.TBD][TBD]], [[https://yopad.eu/p/IVOA_Nov3_KD][Etherpad link]]

Topic revision: r5 - 2022-04-12 - JanetEvans

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