Using Machine Learning to constraint planet mass from gap profiles in protoplanetary disks

Sayantan Auddy^1*, Min-Kai Lin¹

¹Institute of Astronomy and Astrophysics, ASIAA, Taipei, Taiwan

* Presenter:Sayantan Auddy, email:sauddy@asiaa.sinica.edu.tw

The observed gaps in the protoplanetary disk are often considered an imprint of planets orbiting around the central star. The width and the depth of the gaps depend on the mass of the planet along with the disk properties, for example, aspect ratio, viscosity, and dust-to-gas ratio. We run a large number of two-dimensional hydrodynamic simulations in FARGO3D using GPU clusters to compute the gaps induced by planets in a dusty disk for a wide parameter range. The dataset is then used to train a Deep Neural Networks to predict the planet mass from an observed disk gap in a protoplanetary disk. This machine learning technique provides an edge over the existing empirical relations as our model can be trained for any number of relevant parameters and complex disk system. Our trained neural network provides an accurate prediction of the planet mass for an observed gap in a disk in much-reduced computing time.

Keywords: Planet Formation, Machine Learning, 3D simulations