Emission-line diagnostics of HII regions using conditional Invertible Neural Networks

TL;DR

This paper introduces a novel conditional invertible neural network method coupled with an emission predictor to accurately infer physical properties of star-forming regions from spectral data, addressing degeneracies in observational astrophysics.

Contribution

The paper presents a new cINN-based approach that estimates multiple physical parameters of HII regions from optical emission lines, improving inference accuracy and efficiency.

Findings

The cINN accurately predicts seven physical parameters from spectral data.

The method is validated with synthetic models, showing consistent posterior estimates.

Observational uncertainties impact the network's performance, which is evaluated.

Abstract

Young massive stars play an important role in the evolution of the interstellar medium (ISM) and the self-regulation of star formation in giant molecular clouds (GMCs) by injecting energy, momentum, and radiation (stellar feedback) into surrounding environments, disrupting the parental clouds, and regulating further star formation. Information of the stellar feedback inheres in the emission we observe, however inferring the physical properties from photometric and spectroscopic measurements is difficult, because stellar feedback is a highly complex and non-linear process, so that the observational data are highly degenerate. On this account, we introduce a novel method that couples a conditional invertible neural network (cINN) with the WARPFIELD-emission predictor (WARPFIELD-EMP) to estimate the physical properties of star-forming regions from spectral observations. We present a cINN that predicts the posterior distribution of seven physical parameters (cloud mass, star formation efficiency, cloud density, cloud age which means age of the first generation stars, age of the youngest cluster, the number of clusters, and the evolutionary phase of the cloud) from the luminosity of 12 optical emission lines, and test our network with synthetic models that are not used during training. Our network is a powerful and time-efficient tool that can accurately predict each parameter, although degeneracy sometimes remains in the posterior estimates of the number of clusters. We validate the posteriors estimated by the network and confirm that they are consistent with the input observations. We also evaluate the influence of observational uncertainties on the network performance.