Multi-epoch machine learning 1: Unravelling nature versus nurture for galaxy formation
Multi-epoch machine learning 1: Unravelling nature versus nurture for galaxy formation
复制标题
多时代机器学习 1：揭示星系形成的自然与培育
DOI：
--
复制
发表时间：
2021
影响因子：
4.8
通讯作者：
S. Khochfar
中科院分区：
文献类型：
--
作者：
R. McGibbon;S. Khochfar
我们提出了一种新的机器学习方法来预测暗物质的重子属性只有从N体模拟的subhalos。我们的模型是建立使用极端随机树（ERT）算法，并采取subhalo性质在很大范围内的红移作为其输入功能。我们使用IllustrisTNG模拟来训练我们的模型，以预测黑洞质量，气体质量，星等，星星形成率，恒星质量和金属丰度。我们比较我们的方法与基线模型从以前的作品的结果，并对模型，只考虑了大量的历史的subhalo。我们发现，我们的新模型显着优于其他两个模型。然后，我们通过查看ERT算法的特征重要性得分来研究每个输入的预测能力。我们生产的每个重子属性的特征重要性图，并发现它们有显着差异。我们确定低的红移是最重要的预测星星的形成率和气体质量，与高红移是最重要的预测恒星质量和金属丰度，并考虑这意味着什么性质与培育。我们发现，本研究中研究的星系的物理特性都是由后天驱动的，而不是自然驱动的。唯一显示出大自然影响更大的属性是现今星系的星星形成率。最后，我们验证了特征重要性图正在发现物理模式，并且显示的趋势不是ERT算法的假象。
We present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos from N-body simulations. Our model is built using the extremely randomized tree (ERT) algorithm and takes subhalo properties over a wide range of redshifts as its input features. We train our model using the IllustrisTNG simulations to predict blackhole mass, gas mass, magnitudes, star formation rate, stellar mass, and metallicity. We compare the results of our method with a baseline model from previous works, and against a model that only considers the mass history of the subhalo. We find that our new model significantly outperforms both of the other models. We then investigate the predictive power of each input by looking at feature importance scores from the ERT algorithm. We produce feature importance plots for each baryonic property, and find that they differ significantly. We identify low redshifts as being most important for predicting star formation rate and gas mass, with high redshifts being most important for predicting stellar mass and metallicity, and consider what this implies for nature versus nurture. We find that the physical properties of galaxies investigated in this study are all driven by nurture and not nature. The only property showing a somewhat stronger impact of nature is the present-day star formation rate of galaxies. Finally we verify that the feature importance plots are discovering physical patterns, and that the trends shown are not an artefact of the ERT algorithm.