Search for young stellar objects within 4XMM-DR13 using CatBoost and SPE

TL;DR

This study employs machine learning techniques, specifically CatBoost and SPE, to classify and identify young stellar objects within a large multi-wavelength astronomical dataset, discovering new YSO candidates and providing a comprehensive classification catalog.

Contribution

The paper introduces a novel application of SPE and CatBoost for YSO classification, achieving high accuracy and identifying new YSO candidates in a large sky survey dataset.

Findings

Identified 1102 YSO candidates, including 258 known YSOs.

Verified candidates using spectra and database cross-matching.

Provided a comprehensive classification catalog for the dataset.

Abstract

Classifying and summarizing large data sets from different sky survey projects is essential for various subsequent scientific research. By combining data from 4XMM-DR13, SDSS DR18, and CatWISE, we formed an XMM-WISE-SDSS sample that included information in the X-ray, optical, and infrared bands. By cross-matching this sample with datasets from known spectral classifications from SDSS and LAMOST, we obtained a training dataset containing stars, galaxies, quasars, and Young Stellar Objects (YSOs). Two machine learning methods, CatBoost and Self-Paced Ensemble (SPE), were used to train and construct machine learning models through training sets to classify the XMM-WISE-SDSS sample. Notably, the SPE classifier showed excellent performance in YSO classification, identifying 1102 YSO candidates from 160,545 sources, including 258 known YSOs. Then we further verify whether these candidates are YSOs by the spectra in LAMOST and the identification in the SIMBAD and VizieR dabtabases. Finally there are 412 unidentified YSO candidates. The discovery of these new YSOs is an important addition to existing YSO samples and will deepen our understanding of star formation and evolution. Moreover we provided a classification catalog for the whole XMM-WISE-SDSS sample.