Project description
Improved simulations for stargazing
Our interest in astronomy dates back to ancient times. Boosted with developments and improvements as well as the use of more advanced equipment and in-depth knowledge of the sciences, astronomy remains popular to this day. Today, astronomical research greatly depends on simulations. Unfortunately, there is no way to practically measure the realism factor in simulation, and numerical simulations tend to be too slow and expensive for prototyping new techniques or improving statistical significance. The EU-funded RISING project will address these issues by developing a framework comprising machine learning tools for a number of uses, which will find instantaneous application on dynamic simulations of star clusters and hydrodynamical simulations of their parent clouds.
Objective
Contemporary astronomical research relies heavily on simulations. However, the current state of the art has no objective way to measure how `realistic’ a simulation is, nor how informative it is with respect to the scientific questions it was designed to address. Comparison between simulation and observation is left to the subjective judgment of the individual researcher. The set up of simulation sets, the choice of parameters and ingredients to include, and the number of runs to execute are all also left to the researcher’s preferences, given hardware constraints. Numerical astronomy has, as of now, no shared standard of experiment design. Additionally, numerical simulations are often so slow and expensive that it is impossible to quickly and cheaply produce new outputs to improve statistical significance or for rapid prototyping new techniques. To address these issues, I will develop the RISING framework. RISING (Realistic and Informative Simulations with machine learnING) is a bundle of machine learning tools: anomaly detection tools to measure the realism of simulations, active learning tools to plan optimal sets of simulations under resource constraints, and generative modeling tools to obtain credible simulation outputs without running the underlying simulation. RISING will find immediate application on dynamical simulations of star clusters and hydrodynamical simulations of their parent clouds, which are being run in large numbers by the ERC-funded DEMOBLACK group led by my host, Prof. Michela Mapelli. RISING will be written in Python 3.7 using the Keras API on top of Tensorflow, integrated with frameworks for multi-scale, multi-physics simulations, such as AMUSE , whose author is Prof. Portegies-Zwart (Leiden Univ.) with which Prof. Mapelli has a current ongoing collaboration. The source code of RISING and selected data products will be made freely available to the numerical astronomy community.
Fields of science
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Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
35122 Padova
Italy