THE EFFECT OF THE PARAMETERS OF DUSTY SAGE GALAXY EVOLUTION MODELLING ON STELLAR MASS FUNCTION (SMF) AND DUST MASS FUNCTION (DMF)

Developing a theory of galaxy formation is one of the main focuses of astronomy today. To study galaxy formation, we need to model it through a supercomputer simulation and compare the results with observations. Current models of galaxy formation show significantly different results compared to...

Full description

Saved in:
Bibliographic Details
Main Author: Hawaari Rifqi, Haru
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/75044
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Institut Teknologi Bandung
Language: Indonesia
Description
Summary:Developing a theory of galaxy formation is one of the main focuses of astronomy today. To study galaxy formation, we need to model it through a supercomputer simulation and compare the results with observations. Current models of galaxy formation show significantly different results compared to the observation, especially at high redshifts, thus needing to be improved. Testing the theory of galaxy formation is important to understand how stars, gas, and dust are formed in galaxies. In this Final Project, we tested the parameters of a galaxy formation model, Dusty SAGE. The parameters are varied, and we analyzed their effects on the Stellar Mass Function (SMF) and Dust Mass Function (DMF). The parameters are changed individually for each parameter to isolate the effect. This test is carried out on eight parameters that are considered to affect the formation of stars and dust in the galaxy, namely radio mode feedback efficiency (?R), quasar mode feedback efficiency (?Q), star formation efficiency (?SF), condensation efficiency for AGB stars and SN II (?AGBand ?SNII), velocity scale for gas reincorporation (?reinc), threshold subhalo-to-baryonic mass (ffriction), and analytic merger time (tfriction). Next, the effect on SMF and DMF was analyzed and compared with observational data. Comparisons are made to see which parameters can bring the modeling results closer to the observational data. This study’s results indicate several parameters that can bring the SMF and DMF model results closer to the observational data. For the SMF, there are four parameters, namely ?R and ffriction if the value is reduced, also ?SF and ?reinc if the value is increased. For the DMF, there are six parameters, namely ?Q, ?R, ?SF, and ?reinc if the value is decreased, also ?SNII and ?AGB if the value is increased.