activity
20192021
most citedUpper boundaries of AGN regions in optical diagnostic diagrams

21 citations · 21 across the 1 of their papers we have counts for

collaborators

7 papers

astro-ph.GA2021

SDSS-IV MaNGA: the physical origin of off-galaxy H blobs in the local Universe

Xihan Ji, Cheng Li, Renbin Yan +9

H blobs are off-galaxy emission-line regions with weak or no optical counterparts. They are mostly visible in H line, appearing as concentrated blobs. Such unusual objects ha…

astro-ph.GA2020

SDSS-IV MaNGA: Refining Strong Line Diagnostic Classifications Using Spatially Resolved Gas Dynamics

David R. Law, Xihan Ji, Francesco Belfiore +8

We use the statistical power of the MaNGA integral-field spectroscopic galaxy survey to improve the definition of strong line diagnostic boundaries used to classify gas ionization…

astro-ph.GA2020

Constraining Photoionization Models With a Reprojected Optical Diagnostic Diagram

Xihan Ji, Renbin Yan

Optical diagnostic diagrams are powerful tools to separate different ionizing sources in galaxies. However, the model-constraining power of the most widely-used diagrams is very li…

astro-ph.GA2020

Swift/UVOT+MaNGA (SwiM) Value-added Catalog

M. Molina, N. Ajgaonkar, R. Yan +5

We introduce the Swift/UVOT+MaNGA (SwiM) value added catalog, which comprises 150 galaxies that have both SDSS/MaNGA integral field spectroscopy and archival Swift/UVOT near-UV (NU…

astro-ph.GA202021 cited

Upper boundaries of AGN regions in optical diagnostic diagrams

Xihan Ji, Renbin Yan, Rogerio Riffel +2

The distribution of galaxies in optical diagnostic diagrams can provide information about their physical parameters when compared with ionization models under proper assumptions. B…

astro-ph.GA2019

The Data Analysis Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey: Emission-Line Modeling

Francesco Belfiore, Kyle B. Westfall, Adam Schaefer +12

SDSS-IV MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is the largest integral-field spectroscopy survey to date, aiming to observe a statistically representative samp…