# DPNNet-2.0 Part I: Finding hidden planets from simulated images of protoplanetary disk gaps

arXiv: https://arxiv.org/abs/2107.09086 · HTML: https://mlsanity.com/paper/2107.09086/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 81 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [DeepPose: Human Pose Estimation via Deep Neural Networks](https://mlsanity.com/paper/1312.4659/) (2013)
- [A Comprehensive Analysis of Deep Regression](https://mlsanity.com/paper/1803.08450/) (2018)

## Built on by (most cited first, 7 of 7)

- [PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems](https://mlsanity.com/paper/2305.11111/) (2023)
- [Using Bayesian Deep Learning to infer Planet Mass from Gaps in Protoplanetary Disks](https://mlsanity.com/paper/2202.11730/) (2022)
- [Locating Hidden Exoplanets in ALMA Data Using Machine Learning](https://mlsanity.com/paper/2211.09541/) (2022)
- [GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity](https://mlsanity.com/paper/2308.08010/) (2023)
- [Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems](https://mlsanity.com/paper/2409.17228/) (2024)
- [Kinematic Evidence of an Embedded Protoplanet in HD 142666 Identified by Machine Learning](https://mlsanity.com/paper/2301.05075/) (2023)
- [VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Young Stars](https://mlsanity.com/paper/2509.12324/) (2025)

## Abstract (arXiv)

The observed sub-structures, like annular gaps, in dust emissions from protoplanetary disk, are often interpreted as signatures of embedded planets. Fitting a model of planetary gaps to these observed features using customized simulations or empirical relations can reveal the characteristics of the hidden planets. However, customized fitting is often impractical owing to the increasing sample size and the complexity of disk-planet interaction. In this paper we introduce the architecture of DPNNet-2.0, second in the series after DPNNet citepaud20, designed using a Convolutional Neural Network ( CNN, here specifically ResNet50) for predicting exoplanet masses directly from simulated images of protoplanetary disks hosting a single planet. DPNNet-2.0 additionally consists of a multi-input framework that uses both a CNN and multi-layer perceptron (a class of artificial neural network) for processing image and disk parameters simultaneously. This enables DPNNet-2.0 to be trained using images directly, with the added option of considering disk parameters (disk viscosities, disk temperatures, disk surface density profiles, dust abundances, and particle Stokes numbers) generated from disk-planet hydrodynamic simulations as inputs. This work provides the required framework and is the first step towards the use of computer vision (implementing CNN) to directly extract mass of an exoplanet from planetary gaps observed in dust-surface density maps by telescopes such as the Atacama Large (sub-)Millimeter Array.