Sundial

I was part of the Innovative Training Network SUNDIAL, which combines training in computer science and astrophysics. The young scientists in computer science will study topics such as detecting ultrafaint galaxy signals, developing automated models for galaxy recognition and classification, and developing new methods to compare observations and galaxy simulations as well as visualization. My project will focus on galaxy simulations and visualization. With galaxy simulations continuously becoming larger and more detailed, comparison with observation data is far from trivial. Failing to reproduce one ore more observable properties may be indicative that an important astrophysical process is not taken into account in the models. The ability to compare simulations with observations in a well-defined and well-understood quantitative way is a crucial step in calibrating simulations with observed data, thus allowing for a much deeper understanding of galaxy formation.

This Early Stage Researcher (ESR12) will be embedded in the efforts relating simulations to observations - characterization and visualization of simulations in the context of observations and vice versa. The scientific development will be guided by the following questions: How do we visualize the essence of complex astrophysical simulations to differentiate classes of dynamical systems? And given different modalities, how can visualizations be used to investigate and interpret simulations in the light of observations and vice versa.

The main objective is to develop visual data analytic tools based on qualitative measures and latent representation models for simulations of evolving galaxies. The ESR will investigate how to find and present essential characterizations in which different environmental modalities affect the galaxy evolution, which is necessary to interpret simulations beyond comparison of macro scale properties. Therefore, the candidate will strongly collaborate with ESR06, ESR08, ESR10 and ESR11, complementing the developments in galaxy morphology classification, latent space probabilistic modelling and the knowledge gained with respect to astronomical simulation. In particular the PhD candidate is planned to spend several month in seconding nodes including the Astronomy research group under supervision of Dr. Laurikainen in Oulu and internships at the company CLEVER. Since scientific visualisations can benefit greatly from successful tools for visual data analytics, interaction and design as demonstrated by the industrial sector. Close collaboration will ensure that those techniques are incorporated and developments are commercially exploited into coming years products.

Secondary node: Oulu - 3 months, and CLEVER - 3 months.