标题： 噪声网络：确定反映观测不确定性的HII区域的物理属性 显示英文标题

标题： Noise-Net: Determining physical properties of HII regions reflecting observational uncertainties

摘要： 恒星反馈是指年轻恒星与其诞生地之间的能量相互作用，在宇宙的恒星形成历史和星际介质（ISM）的演化中起着重要作用。正确解释恒星形成区域的观测结果对于理解恒星反馈至关重要，但由于反馈过程的复杂性和观测中的退化性，这是一项非显而易见的任务。在我们最近的论文中，我们引入了一种条件可逆神经网络（cINN），该网络从12条光学发射线的光度预测恒星形成区域的七个物理属性，作为一种分析退化观测的新方法。我们证明了我们的网络，在由WARPFIELD-Emission预测器（WARPFIELD-EMP）生成的合成恒星形成区域模型上进行训练，可以准确且精确地预测物理属性。在本文中，我们提出了一种cINN的新更新版本，在网络训练过程中考虑了观测不确定性。我们新提出的网络名为Noise-Net，通过使用发射线光度和相应的不确定性作为网络的必要输入信息，反映了不确定性对参数预测的影响。我们研究了Noise-Net在不同不确定性下的性能，并将其与之前版本的cINN进行了比较，后者在训练过程中不学习不确定性。我们确认Noise-Net在典型的观测不确定性范围内优于之前的网络，并且即使在面对较大的不确定性时也能保持高准确性。 显示英文摘要

摘要： Stellar feedback, the energetic interaction between young stars and their birthplace, plays an important role in the star formation history of the universe and the evolution of the interstellar medium (ISM). Correctly interpreting the observations of star-forming regions is essential to understand stellar feedback, but it is a non-trivial task due to the complexity of the feedback processes and degeneracy in observations. In our recent paper, we introduced a conditional invertible neural network (cINN) that predicts seven physical properties of star-forming regions from the luminosity of 12 optical emission lines as a novel method to analyze degenerate observations. We demonstrated that our network, trained on synthetic star-forming region models produced by the WARPFIELD-Emission predictor (WARPFIELD-EMP), could predict physical properties accurately and precisely. In this paper, we present a new updated version of the cINN that takes into account the observational uncertainties during network training. Our new network named Noise-Net reflects the influence of the uncertainty on the parameter prediction by using both emission-line luminosity and corresponding uncertainties as the necessary input information of the network. We examine the performance of the Noise-Net as a function of the uncertainty and compare it with the previous version of the cINN, which does not learn uncertainties during the training. We confirm that the Noise-Net outperforms the previous network for the typical observational uncertainty range and maintains high accuracy even when subject to large uncertainties.