  Protoplanetary disks are the sites of vigorous hydrodynamic processes, such
as accretion and outflows, and ultimately establish the conditions for the
formation of planets. The properties of disk outflows are often inferred
through analysis of forbidden emission lines. These lines contain multiple
overlapping components, tracing different emission regions with different
processes that excite them: a high-velocity component (tracing a jet), a broad
low-velocity component (tracing inner disk wind), and a narrow low-velocity
component (tracing outer disk wind). They are also heavily contaminated by
background spectral features. All of these challenges call into question the
traditional approach of fitting Gaussian components to the line profiles, and
cloud the physical interpretation of those components. We introduce a novel
statistical technique to analyze emission lines in protoplanetary disks.
Simulation-Based Inference is a computationally efficient machine learning
technique that produces posterior distributions of the parameters (e.g.
magnetic field, radiation sources, geometry) of a representative wind model
when given a spectrum, without any prior assumption about line shapes
(e.g.symmetry). In this pathfinder study, we demonstrate that this technique
indeed accurately recovers the parameters from simulated spectra without noise
and background. A following work will deal with the analysis of observed
spectra.
