Investigating the bar-AGN connection using deep learning

Asselt, Marloes van (2022) Investigating the bar-AGN connection using deep learning. Bachelor's Thesis, Astronomy.

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Abstract

This thesis aims to re-evaluate the connection between the presence of bars and AGN activity. We make use of machine learning to identify the galaxy morphology of HSC galaxies with redshift 0.1 ≤ z ≤ 0.55. We made use of a model with an accuracy of 86.9% to identify our most confident bars and a model with an accuracy of 81.8% to identify our most confident non-bars. We make use of a mid-infrared WISE colour criterion and SDSS optical line emission to identify AGNs in our sample. We found an overall AGN fraction of 1% ± 0.1% and 1.2% ± 0.1% for bars and non bars, respectively for the WISE data and 13.2% ± 1.8% in bars and 14.0%± 1.0% in non-bars for the SDSS data. We also evaluated the AGN fraction as functions of redshift and r-band magnitude. These plots did not show any evidence that bars are a prominent mechanism in triggering AGNs.

Item Type:	Thesis (Bachelor's Thesis)
Supervisor name:	Wang, L. and Margalef Bentabol, B.
Degree programme:	Astronomy
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	16 Aug 2022 09:09
Last Modified:	16 Aug 2022 09:09
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/28400

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