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

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.

32 pages, 23 figures, Accepted for publication by MNRAS on 21. January

References in corpus (13)

• The Host Galaxies and Classification of Active Galactic Nuclei
• Kernel density estimation via diffusion
• The Effects of Stellar Rotation. II. A Comprehensive Set of Starburst99 Models
• Before the first supernova: combined effects of HII regions and winds on molecular clouds
• Imprints of galaxy evolution on H ii regions Memory of the past uncovered by the CALIFA survey
• Invertible Networks or Partons to Detector and Back Again
• Application of Convolutional Neural Networks for Stellar Spectral Classification
• Deep Transfer Learning for Star Cluster Classification: I. Application to the PHANGS-HST Survey
• APOGEE Net: Improving the derived spectral parameters for young stars through deep learning
• WARPFIELD-EMP: The Self-Consistent Prediction of Emission Lines from Evolving HII Regions in Dense Molecular Clouds
• Forming clusters within clusters: How 30 Doradus recollapsed and gave birth again
• Stellar Parameter Determination from Photometry using Invertible Neural Networks
• Massive star feedback in clusters: variation of the FUV interstellar radiation field in time and space

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