Dissertations, Theses, and Capstone Projects
Date of Degree
6-2024
Document Type
Thesis
Degree Name
M.S.
Program
Astrophysics
Advisor
Ari Maller
Subject Categories
Stars, Interstellar Medium and the Galaxy
Keywords
Black holes, galaxies, Illustrs-TNG, machine learning, galaxy properties, SMBH
Abstract
Observationaly it is well established that the masses of central black holes are tightly correlated with galaxy properties, most notably the bulge’s velocity dispersion. Cosmolog- ical hydrodynamical simulations can capture most of these correlations, but it is yet not understood why this occurs. To gain greater insight into central black hole growth we use machine learning algorithms to study the relationship between central black hole mass(MBH) and other galaxy properties at z=0 in the TNG simulations. We find that the central black hole mass can be accurately predicted with just a few galaxy properties only if the central black hole mass is above a resolution dependent value. For those black holes we find a simple formula that predicts their mass within a mean absolute error of 1.14%, 0.95% and 0.68% for TNG 50, 100 and 300 respectfully. We are also able to construct intuitive equations for both 100 and 300 boxes that estimates MBH well when used in the box it was trained on.
Recommended Citation
Dindy, Imani L., "Illustris-TNG Simulated Central Black Mass(MBH) and Galaxy Properties Correlations with a Machine Learning Approach" (2024). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/5896