NCCR PlanetS | University of Bern | 2019 Mar 13
To find out how planets form, astrophysicists run complicated and time consuming computer calculations. Members of the NCCR PlanetS in Bern have now developed a totally novel approach to speed up this process dramatically. They use deep learning based on artificial neural networks, a method that is well known in image recognition.
Planets grow in stellar disks accreting solid material and gas. Whether they become bodies like Earth or Jupiter depends on different factors like the properties of the solids, the pressure and temperature in the disk and the already accumulated material. With computer models the astrophysicists try to simulate the growth process and determine the interior planetary structure. For given boundary conditions they calculate the masses of the gas envelope of a planet. “This requires solving a set of differential equations”, explains Yann Alibert, science officer of the NCCR PlanetS at the University of Bern: “Solving these equations has been a specialty of the astrophysicists here in Bern for the past 15 years, but it is a complicated and time consuming process.”
To speed up the calculations Yann Alibert and PlanetS associate Julia Venturini of the International Space Science Institute (ISSI) in Bern adopted a method that has already captured many other fields including the smartphone in our hand: deep learning. It is for instance used for face and image recognition. But this branch of artificial intelligence and machine learning has also improved automatic language translation and will be crucial for self-driving cars. “There is a big hype also in astronomy,” says Alibert: “Machine learning has already been used to analyze observations, but to my knowledge we are the first to use deep learning for such a purpose.” ...
Using Deep Neural Networks to Compute the Mass of Forming Planets ~ Yann Alibert, Julia Venturini
- arXiv.org > astro-ph > arXiv:1903.00320 > 01 Mar 2019