Searching of New Emission-Line Stars using the Astroinformatics Approach

TL;DR

This paper applies data mining and astroinformatics techniques to identify new Be star candidates in SDSS spectra by analyzing emission line features, demonstrating the effectiveness of machine learning in astronomical object classification.

Contribution

It introduces a novel astroinformatics approach combining spectral data transformation and supervised learning to discover new emission-line star candidates.

Findings

Several new Be star candidates identified in SDSS data.

Validation of astroinformatics as a viable method for stellar classification.

Spectral feature analysis effectively distinguishes emission-line star profiles.

Abstract

Using data mining techniques applied on emission line characteristics of Be stars spectra we attempted to find new Be stars candidates in SDSS SEGUE survey. The mid-resolution spectra of confirmed Be stars obtained from VO-compatible archive of Ond\v{r}ejov observatory 2m telescope were transformed to the spectral resolution of SDSS and important characteristics of emission line profiles were estimated, to be used as a training base of supervised learning methods. The obtained knowledge base of the characteristic shapes and sizes of Be emission lines was finally used to identify new potential candidates in SDSS spectral survey. The several newly found Be stars candidates justify our approach and approve Astroinformatics as a viable research methodology.