DBNets: A publicly available deep learning tool to measure the masses of young planets in dusty protoplanetary discs

arXiv:2402.12448v1 Announce Type: cross
Abstract: Current methods to characterize embedded planets in protoplanetary disc observations are severely limited either in their ability to fully account for the observed complex physics or in their computational and time costs. To address this shortcoming, we developed DBNets: a deep learning tool, based on convolutional neural networks, that analyses substructures observed in the dust continuum emission of protoplanetary discs to quickly infer the mass of allegedly embedded planets. We focussed on developing a method …

abstract arxiv astro-ph.ep astro-ph.im computational costs cs.lg current deep learning embedded physics tool type young