论文标题
机器学习加速化的化学模型
Machine learning-accelerated chemistry modeling of protoplanetary disks
论文作者
论文摘要
目标。借助（子）毫米观测值和詹姆斯·韦伯（James Webb）空间望远镜红外光谱的大量分子发射数据，访问原磁盘的化学成分的快进模型至关重要。 方法。我们使用了热化学建模代码来生成各种多种原星盘模型。我们训练了一个最近的邻居（KNN）回归器，以立即预测其他磁盘模型的化学反应。 结果。我们表明，由于所采用的原月经磁盘模型中局部物理条件之间的相关性，可以仅使用一小部分物理条件来准确地重现化学反应。我们讨论此方法的不确定性和局限性。 结论。所提出的方法可用于对线排放数据的贝叶斯拟合，以从观测值中检索磁盘属性。我们提出了在其他磁盘化学模型集上复制相同方法的管道。
Aims. With the large amount of molecular emission data from (sub)millimeter observatories and incoming James Webb Space Telescope infrared spectroscopy, access to fast forward models of the chemical composition of protoplanetary disks is of paramount importance. Methods. We used a thermo-chemical modeling code to generate a diverse population of protoplanetary disk models. We trained a K-nearest neighbors (KNN) regressor to instantly predict the chemistry of other disk models. Results. We show that it is possible to accurately reproduce chemistry using just a small subset of physical conditions, thanks to correlations between the local physical conditions in adopted protoplanetary disk models. We discuss the uncertainties and limitations of this method. Conclusions. The proposed method can be used for Bayesian fitting of the line emission data to retrieve disk properties from observations. We present a pipeline for reproducing the same approach on other disk chemical model sets.
