Thesis defence in the University of Oulu

Doctoral Candidate

Master of Science Aku Venhola

Faculty and research unit

University of Oulu Graduate School, Faculty of Science, Astronomy Research Unit

Field of study

Astronomy

Date and time of the thesis defence

29.3.2019 11:00

Place of the thesis defence

The auditorium of the main building of the University of Groningen, Groningen, the Netherlands

Topic of the dissertation

Evolution of dwarf galaxies in the Fornax-cluster

Opponent

Professor Sven de Rijcke, University of Ghent

Custos

Evolution of dwarf galaxies in the Fornax cluster

In my thesis we analyzed deep optical images in order to study faint galaxies and stellar steams in the nearby Fornax-cluster. We obtained the images using the VST-telesope of the European Southern Observatory (ESO), located in Chile. From those deep images, we found hundreds of previously unknown faint galaxies from which we generated the most comprehensive galaxy catalog of the Fornax-cluster area. This work will be a basis for various follow-up studies, of which several have already been started.

Based on our analysis of structure and colors of the dwarf galaxies in the different parts of the Fornax-cluster, we also obtained new knowledge about how the cluster environment affects the evolution of those galaxies. Due to our large galaxy sample, we were also able to show that the environment effects the evolution of galaxies in differernt ways dependending on the galaxy's mass.

Deep and wide high-resolution images, such as used in our study, are an essential part of the present day's astronomy. Those images also set up a problem of identifying the thousands of previously unknown faint galaxies that they reveal. In my thesis, I showed that a new max-tree based identification algorithm outperforms the standard algorithm used by astronomers, by identifying more galaxies from the images and by estimating their structure more accurately. This algorithm, developed in the University of Groningen, has been previously used for identifying structures from medical imaging. Based on our tests, also the upcoming deep sky surveys could benefit significantly from this algorithm.

 

Last updated: 5.4.2019