Line Profiles of Forbidden Emission Lines and What They Can Tell Us About Protoplanetary Disk Winds

Abstract

Emission in forbidden lines of oxygen, neon, and other species are commonly used to trace winds from protoplanetary disks. Using Cloudy, we calculate such emission for parametrized wind models of the magnetothermal type, following Bai et al. These models share characteristics with both photoevaporative and magnetocentrifugal winds, which can be regarded as end members, and are favored by recent theoretical research. Both broad and narrow low-velocity components of the lines can be produced with plausible wind parameters, something that traditional wind models have difficulty with. Line luminosities, blueshifts, and widths, as well as trends of these with accretion luminosity and disk inclination, are in general accordance with observations.

Modeling JWST MIRI-MRS Observations of T Cha: Mid-IR Noble Gas Emission Tracing a Dense Disk Wind

“…However, they can also be fit with a photoevaporative wind (Rab et al 2023), albeit with the emitting region of the [O I] line lying mostly in the bound, static atmosphere just inside of the wind (thus explaining its lack of a significant blueshift without the need for a dust cavity, the explanation proposed by Pascucci et al 2011). [O I] emission was also described as predominantly originating in an almost hydrostatic atmosphere in the photoevaporative models by Nemer & Goodman (2024). This restricted emitting region is due to the higher excitation temperature of the [O I] line, which can only be produced in the hottest, innermost regions where the EUV penetrates (Ercolano & Owen 2016).…”

“…As simulations of MHD winds continue to be developed and improved-particularly with regard to their thermochemistrythey should be tested against these sorts of observations to see if they indeed provide a remedy. Nemer & Goodman (2024) provided a first step toward this using the simple analytic magnetothermal model of Bai et al (2016) and indeed see that such models produce a much more extended blue wing of the [Ne II] line than the same models in the purely photoevaporative limit. Nevertheless, these models underpredict the luminosity of the line by an order of magnitude compared to typically observed values.…”

Constraining Protoplanetary Disk Winds from Forbidden Line Profiles with Simulation-based Inference

“…We use the analytic wind models of Nemer & Goodman (2024) to generate simulated spectra, then compress them with a neural spectrum encoder, and train a neural density estimator (NDE) to carry out SBI as the mapping from model parameters to (compressed) spectra. Afterward, we test the trained SBI network with a sample of simulated spectra (with known parameters) that it has not seen during training.…”

“…We utilize the radiative transfer software CLOUDY (Ferland et al 2017) to analyze the density and velocity distributions within our models of stellar winds. This analysis, coupled with appropriate energetic photon luminosities, allows us to compute the emission of the [O I] 6300 line, following the methodology outlined in Nemer & Goodman (2024). Our focus lies predominantly on the inner-to-intermediate regions of the disk, spanning distances from (0.04-4 au), as these regions contribute the majority of the emission observed in the line.…”

“…These lines exhibit distinguishable low-velocity components (LVCs), emission blueshifted by less than 30 km s −1 , and high-velocity components (HVCs; Hartigan et al 1995). Recent studies have successfully modeled LVCs with magnetothermal wind models (Nemer et al 2020;Nemer & Goodman 2024), although recent observations have shown that LVCs can be further deconstructed into broader components (BCs), with a full width at half-maximum (FWHM) of at least 40 km s −1 , and narrower components (NCs; Rigliaco et al 2013;Simon et al 2016;Fang et al 2023). This decomposition into different components is usually done with a multi-Gaussian fitting, where the choice of the number and shapes of the Gaussians is chosen by minimizing the residual after fitting (Fang et al 2018).…”