InterOpApr2022KDIG < IVOA

IVOA Web>WebPreferences>ProgramPrepVirtualApr2022>InterOpApr2022>InterOpApr2022KDIG (2022-04-28, PetrSkoda) (raw view)

---++ 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> | X-ray datasets: A Machine Learning Perspective | 12'+3' | <p>As the volume of astronomical X-ray datasets increases with the advent of dedicated catalogs and new facilities, data science and machine learning methods are becoming a useful tool for discovery in high energy astrophysics, in particular in the context of time-domain anomalies. Due to the specific nature of X-ray datasets, which are lists of individual photon detections called event files, the pre-processing practices of the data for the purpose of classification, regression, or anomaly detection need to be carefully reviewed before any existing methods designed for optical or infrared datasets is used. Here I provide an overview of our experience pre-processing X-ray datasets for machine learning applications, and adapting methods of classification and anomaly detections for X-ray catalogs (in particular, the Chandra Source Catalog, CSC). I will discuss what CSC interfaces are most useful for the seamless use of machine learning for discovery, and provide recommendations as to what other data providers should do to make these methods easier to use. Finally, I will highlight some results of our investigations.</p> |
| _Petr Skoda_ | <p>The Role of VO Technology in Astronomical Machine<br />Learning</p> | 12'+3' | 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. |
| Ying Wu | <p>Classification of Galaxy Spectra based on Convolutional Neural Network</p> | 12'+3' | 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]]

Attachments

Topic attachments
I	Attachment	History	Action	Size	Date	Who
pptx	IVOA2022InterOp_KDIG_Talk_Ying_Wu_220428.pptx	r1	manage	1839.9 K	2022-04-28 - 11:25	YihanTao
pdf	ivoa_interop_2022_martinez.pdf	r1	manage	15108.5 K	2022-04-28 - 21:38	RaffaeleDAbrusco
pdf	skoda-MLVO.pdf	r1	manage	2151.8 K	2022-04-28 - 21:51	PetrSkoda

Topic revision: r10 - 2022-04-28 - PetrSkoda

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