Abstract

We present our study on the spatially resolved Hα and M* relation for 536 star-forming and 424 quiescent galaxies taken from the MaNGA survey. We show that the star formation rate surface density (ΣSFR), derived based on the Hα emissions, is strongly correlated with the M* surface density (Σ*) on kiloparsec scales for star-forming galaxies and can be directly connected to the global star-forming sequence. This suggests that the global main sequence may be a consequence of a more fundamental relation on small scales. On the other hand, our result suggests that ~20% of quiescent galaxies in our sample still have star formation activities in the outer region with lower specific star formation rate (SSFR) than typical star-forming galaxies. Meanwhile, we also find a tight correlation between ΣHα and Σ* for LI(N)ER regions, named the resolved "LI(N)ER" sequence, in quiescent galaxies, which is consistent with the scenario that LI(N)ER emissions are primarily powered by the hot, evolved stars as suggested in the literature.

Document Type

Article

Publication Date

12-11-2017

Notes/Citation Information

Published in The Astrophysical Journal Letters, v. 851, no. 2, L24, p. 1-6.

© 2017. The American Astronomical Society. All rights reserved.

The copyright holder has granted the permission for posting the article here.

Digital Object Identifier (DOI)

https://doi.org/10.3847/2041-8213/aa9d80

Funding Information

The work is supported by the Ministry of Science & Technology of Taiwan under grants MOST 103-2112-M-001-031-MY3 and 106-2112-M-001-034-. This project also makes use of the MaNGA-Pipe3D data products. We thank the IA-UNAM MaNGA team for creating it, and the ConaCyt-180125 project for supporting them. M.B. was supported by MINEDUC-UA project, code ANT 1655. R.R. thanks CNPq and FAPERGS for partial financial support.

Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah.

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