Astronomy Seminar: Using Machine Learning to infer mass of unseen exoplanet from gap profiles in protoplanetary disks
| Date/Time: | Friday, 05 Mar 2021 from 4:10 pm to 5:10 pm |
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| Location: | Online |
| Cost: | 0.00 |
| Contact: | |
| Phone: | 515-294-5440 |
| Channel: | College of Liberal Arts and Sciences |
| Actions: | Download iCal/vCal | Email Reminder |
Observations of bright protoplanetary disks often show annular gaps in their dust emission. One interpretation of these gaps is disk-planet interaction. If so, fitting models of planetary gaps to observed protoplanetary disk gaps can reveal the presence of hidden planets. However, future surveys are expected to produce an ever-increasing number of protoplanetary disks with gaps. In this case, performing a customized fitting for each target becomes impractical owing to the complexity of disk-planet interaction. In this talk, we introduce DPNNet (Disk Planet Neural Network), an efficient model of planetary gaps by exploiting the power of machine learning. We train a deep neural network with a large number of dusty disk-planet hydrodynamic simulations across a range of planet masses, disk temperatures, disk viscosities, disk surface density profiles, particle Stokes numbers, and dust abundances. The network is then be deployed to extract the planet mass for a given gap morphology. We demonstrate its utility by applying it to the dust gaps observed in the protoplanetary disk around HL Tau at 10 au, 30 au, and 80 au. Our network predict planet masses of 80MEarth, 63MEarth, and 70MEarth, respectively, which are comparable to other studies based on specialized simulations. Additionally, I will discuss an advanced approach to using Convolutional Neural Network to initiate image based characterization from disk images.