Poster Presentation

Star Formation Relation in Nearby Molecular Clouds: Using Machine Learning to select Young Stellar Object candidates from IR All Sky Survey

Author(s): Ying-Chi Hu (NTHU); Shih-Ping Lai (NTHU)

Presenter: Ying-Chi Hu (NTHU)

The relation between star formation rate surface density (ΣSFR)and gas surface density (Σgas) in the galactic region is important to determine the mechanism of star formation in the interstellar medium. Heiderman et al.(2010) use data from Spitzer cores to disks (c2d) and Gould’s Belt (GB) surveys to estimate the ΣSFR- Σgas relation of molecular clouds. In this research, we use machine learning to select young stellar object candidates from infrared all sky survey and investigate weather the ΣSFR–Σgas relation from all-sky survey consists with previous work. We then
calculate the ΣSFR– Σgas relation of more clouds to extend the data points in lager range of Σgas and test if the ΣSFR– Σgas relation is different in high-density and low-density region.