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arXiv 2301.02001astro-ph.GA
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利用机器学习将星系周介质观测结果映射到理论

Mapping Circumgalactic Medium Observations to Theory Using Machine Learning

Sarah Appleby, Romeel Davé, Daniele Sorini, Christopher Lovell, Kevin Lo

AI总结:

本研究提出基于Simba模拟样本训练的随机森林框架,从类星体吸收线观测预测CGM物理条件,绕过传统简化假设,结合归一化流提升覆盖度,模型性能良好且已开源。

摘要:

我们提出了一种随机森林框架,用于从类星体吸收线可观测值预测星系周介质(CGM)的物理条件,该框架基于Simba宇宙学模拟中的Voigt轮廓拟合合成吸收体样本训练而成。传统上,从CGM吸收体观测中提取物理条件涉及均匀单相云等简化假设,但通过使用宇宙学模拟,我们绕过了这类假设,以更好地捕捉CGM可观测值与底层气体条件之间的复杂关系。我们针对一系列恒星形成率、恒星质量和碰撞参数下星系周围的HI和选定金属线的合成光谱训练随机森林模型,以预测吸收体的过密度、温度和金属丰度。这些模型能很好地复现Simba中的真实值:在所有离子中,由金属线(而非HI)预测的过密度的归一化横向标准差为0.50-0.54 dex,温度为0.32-0.54 dex,金属丰度为0.49-0.53 dex。通过特征重要性分析,随机森林表明过密度主要由吸收体柱密度决定,温度由线宽主导,金属丰度对比恒星形成率最敏感。而通过每次移除一个可观测值来检验特征重要性时,过密度和金属丰度似乎更多由碰撞参数驱动。我们引入了一种归一化流方法,以确保网络能准确覆盖真实物理条件中的离散程度。训练好的模型可在线获取。

英文摘要:

We present a random forest framework for predicting circumgalactic medium (CGM) physical conditions from quasar absorption line observables, trained on a sample of Voigt profile-fit synthetic absorbers from the Simba cosmological simulation. Traditionally, extracting physical conditions from CGM absorber observations involves simplifying assumptions such as uniform single-phase clouds, but by using a cosmological simulation we bypass such assumptions to better capture the complex relationship between CGM observables and underlying gas conditions. We train random forest models on synthetic spectra for HI and selected metal lines around galaxies across a range of star formation rates, stellar masses, and impact parameters, to predict absorber overdensities, temperatures, and metallicities. The models reproduce the true values from Simba well, with normalised transverse standard deviations of $0.50-0.54$ dex in overdensity, $0.32-0.54$ dex in temperature, and $0.49-0.53$ dex in metallicity predicted from metal lines (not HI), across all ions. Examining the feature importance, the random forest indicates that the overdensity is most informed by the absorber column density, the temperature is driven by the line width, and the metallicity is most sensitive to the specific star formation rate. Alternatively examining feature importance by removing one observable at a time, the overdensity and metallicity appear to be more driven by the impact parameter. We introduce a normalising flow approach in order to ensure the scatter in the true physical conditions is accurately spanned by the network. The trained models are available online.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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