Data & Computation

Astronomy today is not the same as in the last decade; new facilities are observing the transient sky with increasing frequency, covering all-wavelengths and producing petabytes of data. This represents a great opportunity but also a challenge, as it is not trivial to manage and connect big and heterogeneous datasets as well as maximise scientific output through identification and filtering algorithms based in AI or machine learning. This requires a focus on technologies for the future, such as state-of-the-art AI to optimise our analyses, with a direct link to the translation theme to create technologies for society.

Core researchers

• Prof Linqing Wen  (University of Western Australia) - 

• Prof Andreas Wicenec (University of Western Australia) -

• Dr Anais Möller (Swinburne University of Technology) - Rubin Fink broker co-PI, AI and ML on cosmology and astrophysics.

• Prof Lilia Ferrario (Australian National University) - 

• A/Prof David Coward (University of Western Australia) - 

• Prof Amitava Datta (University of Western Australia) - 

• A/Prof Katharine Turner (Australian National University) - 

• A/Prof Bradley Tucker (Australian National University) - 

• Prof Jeff Cooke (Swinburne University of Technology) -

• Prof Jarrod Hurley (Swinburne University of Technology) - 

• A/Prof Bernhard Müller (Monash University) - Supernova model, light curve and spectra data base from numerical simulations.  

• Prof Orsola De Marco (Macquarie University) - 

• Dr Karelle Siellez (University of Tasmania) - 

Partner researchers

• TBN

Associate researchers

• Prof Dan Kasen  (University of California at Berkeley) - Supernova data comparison methods for theory and observation.

• Dr. Emille Ishida (CNRS/LPCA) - Rubin broker Fink co-PI, supernova and machine learning