Ashlen Coburn
Research Mentor: Feige Wang
Mentor Department: Astronomy, LSA
Author(s): Feige Wang, Xiangyu Jin
Session: Session 1 (9:00 AM – 9:50 AM)
Presentation Type: Poster 137
Abstract
Characterizing active galactic nuclei (AGN) is essential for studying the processes that connect black hole growth to the evolution of their host galaxies. Determining accurate black hole masses is an important step in quantifying this relationship. This project estimates supermassive black hole masses for a sample of three AGN using JWST NIRSpec spectroscopy and broad emission-line mass estimators. The NIRSpec spectra were reduced using the msaexp pipeline. Redshifts were determined and the observed wavelengths were converted to the rest frame. Emission features, including Hß and Paß, were identified and modeled using Python scripts that fit a power-law continuum and decompose the emission lines into narrow and broad components. Black hole masses were then estimated using virial scaling relations based on the broad Paß and Ha emission lines. The full width at half maximum (FWHM) of the broad-line components and their luminosities were used to derive the mass estimates. The resulting black hole masses are 6.64 × 10^7 Msun, 9.88 × 10^8 Msun, and 9.75 × 10^6 Msn for AGN at redshifts z = 3.58, z = 2.005, and z = 6.5425, respectively. This work highlights the use of JWST NIRSpec spectroscopy and Python-based spectral modeling to study black hole growth in AGN, and provides a foundation for future analyses incorporating imaging data to further investigate AGN emission.
