Modeling protoplanetary disk SEDs with artificial neural networks:
Revisiting the viscous disk model and updated disk masses
- URL: http://arxiv.org/abs/2009.03323v1
- Date: Mon, 7 Sep 2020 18:00:02 GMT
- Title: Modeling protoplanetary disk SEDs with artificial neural networks:
Revisiting the viscous disk model and updated disk masses
- Authors: \'A. Ribas, C. C. Espaillat, E. Mac\'ias, L. M. Sarro
- Abstract summary: We model the spectral energy distributions (SEDs) of 23 protoplanetary disks in the Taurus-Auriga star-forming region using detailed disk models and a Bayesian approach.
Results yield high viscosities and accretion rates for many sources, which is not consistent with recent measurements of low turbulence levels in disks.
This effect is particularly relevant for disk population studies and alleviates previous observational tensions between the masses of protoplanetary disks and exoplanetary systems.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We model the spectral energy distributions (SEDs) of 23 protoplanetary disks
in the Taurus-Auriga star-forming region using detailed disk models and a
Bayesian approach. This is made possible by combining these models with
artificial neural networks to drastically speed up their performance. Such a
setup allows us to confront $\alpha$-disk models with observations while
accounting for several uncertainties and degeneracies. Our results yield high
viscosities and accretion rates for many sources, which is not consistent with
recent measurements of low turbulence levels in disks. This inconsistency could
imply that viscosity is not the main mechanism for angular momentum transport
in disks, and that alternatives such as disk winds play an important role in
this process. We also find that our SED-derived disk masses are systematically
higher than those obtained solely from (sub)mm fluxes, suggesting that part of
the disk emission could still be optically thick at (sub)mm wavelengths. This
effect is particularly relevant for disk population studies and alleviates
previous observational tensions between the masses of protoplanetary disks and
exoplanetary systems.
Related papers
- Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter
Inference in Disk-Planet Systems [16.738136124873307]
We introduce Disk2Planet, a machine learning-based tool to infer key parameters in disk-planet systems from observed protoplanetary disk structures.
Our tool is fully automated and can retrieve parameters in one system in three minutes on an Nvidia A100 graphics processing unit.
arXiv Detail & Related papers (2024-09-25T18:00:01Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - Analysing the SEDs of protoplanetary disks with machine learning [0.0]
One radiative transfer model requires a couple of minutes to compute.
We created neural networks (NNs) to emulate the SED generation process.
NNs are able to predict SEDs within 1ms with uncertainties of about 5%.
arXiv Detail & Related papers (2023-02-09T13:32:47Z) - Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion
Probabilistic Models [58.357180353368896]
We propose a conditional paradigm that benefits from the denoising diffusion probabilistic model (DDPM) to tackle the problem of realistic and diverse action-conditioned 3D skeleton-based motion generation.
We are a pioneering attempt that uses DDPM to synthesize a variable number of motion sequences conditioned on a categorical action.
arXiv Detail & Related papers (2023-01-10T13:15:42Z) - Slow semiclassical dynamics of a two-dimensional Hubbard model in
disorder-free potentials [77.34726150561087]
We show that introduction of harmonic and spin-dependent linear potentials sufficiently validates fTWA for longer times.
In particular, we focus on a finite two-dimensional system and show that at intermediate linear potential strength, the addition of a harmonic potential and spin dependence of the tilt, results in subdiffusive dynamics.
arXiv Detail & Related papers (2022-10-03T16:51:25Z) - Machine learning-accelerated chemistry modeling of protoplanetary disks [0.0]
We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models.
We trained a K-nearest neighbors regressor to instantly predict the chemistry of other disk models.
arXiv Detail & Related papers (2022-09-27T12:42:13Z) - Momentum Diminishes the Effect of Spectral Bias in Physics-Informed
Neural Networks [72.09574528342732]
Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs)
They often fail to converge to desirable solutions when the target function contains high-frequency features, due to a phenomenon known as spectral bias.
In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under gradient descent with momentum (SGDM)
arXiv Detail & Related papers (2022-06-29T19:03:10Z) - Supernova Light Curves Approximation based on Neural Network Models [53.180678723280145]
Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy.
Recent studies have demonstrated the superior quality of solutions based on various machine learning models.
We study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve.
arXiv Detail & Related papers (2022-06-27T13:46:51Z) - Riemannian Score-Based Generative Modeling [56.20669989459281]
We introduce score-based generative models (SGMs) demonstrating remarkable empirical performance.
Current SGMs make the underlying assumption that the data is supported on a Euclidean manifold with flat geometry.
This prevents the use of these models for applications in robotics, geoscience or protein modeling.
arXiv Detail & Related papers (2022-02-06T11:57:39Z) - DPNNet-2.0 Part I: Finding hidden planets from simulated images of
protoplanetary disk gaps [6.2194417376659015]
We introduce DPNNet-2.0, second in the series after DPNNet citepaud20, for predicting exoplanet masses directly from simulated images of protoplanetary disks.
This work 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.
arXiv Detail & Related papers (2021-07-19T18:00:31Z) - Tunable-spin-model generation with spin-orbit-coupled fermions in
optical lattices [0.5249805590164902]
We study the dynamical behaviour of ultracold fermionic atoms loaded into an optical lattice under the presence of an effective magnetic flux.
At half filling, the system can emulate a variety of iconic spin-1/2 models such as an Ising model, an XY model, a generic XXZ model with arbitrary anisotropy, or a collective one-axis twisting model.
arXiv Detail & Related papers (2020-11-03T16:54:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.