Machine Learning in Astronomy
Name: Erik Zackrisson
Project title: Machine Learning in Astronomy
Department: Department of Physics and Astronomy
Area of research: Astronomy
The night sky is like a time machine – the light from the most distant astronomical light sources has been travelling for more than 13 billion years to reach us. By studying such objects, we can learn more about the formation of the first generations of galaxies, at a time when the Universe was just a few percent of its current age. In 2021, the James Webb Space Telescope will open a new window on this epoch in the history of the Universe, but since galaxies at extreme distances also appear very faint, the data will inevitably be noisy. Through the use of machine learning techniques, we hope to increase the science return of these new observations of the early Universe.
While the study of the very distant Universe can be said to be data-starved, the opposite is true for the nearby Universe. Thanks to large sky surveys, we have access to rich sets of data for billions of light sources in our own cosmic backyard. Here, machine learning can help us find anomalous objects, potentially leading to the discovery of previously unknown astronomical phenomena.
What do you look forward to the most during your sabbatical?
Focusing on the development of new research methods and getting influences from other fields.