Learning the relationship between galaxies spectra and their star formation histories using convolutional neural networks and cosmological simulations
Learning the relationship between galaxies spectra and their star formation histories using convolutional neural networks and cosmological simulations
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DOI：
10.1093/mnras/stz2851
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发表时间：
2019-03
影响因子：
4.8
通讯作者：
C. Lovell;V. Acquaviva;P. Thomas;K. Iyer;E. Gawiser;S. Wilkins
中科院分区：
文献类型：
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作者：
C. Lovell;V. Acquaviva;P. Thomas;K. Iyer;E. Gawiser;S. Wilkins
我们提出了一种新的方法来推断银河系恒星形成历史(SFH)，使用机器学习方法结合两个宇宙流体动力学模拟。我们训练卷积神经网络，从Eagle和Illustris模型中学习合成星系光谱和高分辨率SFH之间的关系。为了评估我们的SFH重建，我们使用对称平均绝对百分比误差(SMAPE)，它在低误差区域内充当真实百分比误差。在尘埃衰减光谱上，我们达到了很高的测试精度(SMAPE中值=10.5%)。包括模拟观测噪声的影响会增加误差(12.5%)，但通过包括噪声的多个实现来缓解误差，这增加了训练集的规模，减少了过度拟合(10.9%)。我们还对观测误差和模型误差进行了估计。为了进一步评估泛化特性，我们将在一个模拟上训练的模型应用于另一个模拟的光谱，这导致误差仅略有增加(中位数SMAPE$\sim 15{\，{\rm{per\，cent}$)。我们将每个训练好的模型应用于SDSS DR7光谱，并发现比$\extsf{Vespa}$目录中更平滑的历史。这种新的方法补充了现有光谱能量分布拟合技术的结果，提供了直接受最新宇宙学模拟结果驱动的SFH。
We present a new method for inferring galaxy star formation histories (SFH) using machine learning methods coupled with two cosmological hydrodynamic simulations. We train convolutional neural networks to learn the relationship between synthetic galaxy spectra and high-resolution SFHs from the eagle and Illustris models. To evaluate our SFH reconstruction we use Symmetric Mean Absolute Percentage Error (SMAPE), which acts as a true percentage error in the low error regime. On dust-attenuated spectra we achieve high test accuracy (median SMAPE = 10.5 per cent). Including the effects of simulated observational noise increases the error (12.5 per cent), however this is alleviated by including multiple realizations of the noise, which increases the training set size and reduces overfitting (10.9 per cent). We also make estimates for the observational and modelling errors. To further evaluate the generalization properties we apply models trained on one simulation to spectra from the other, which leads to only a small increase in the error (median SMAPE $\sim 15{\,{\rm {per\, cent}}}$). We apply each trained model to SDSS DR7 spectra, and find smoother histories than in the $\textsf{vespa}$ catalogue. This new approach complements the results of existing spectral energy distribution fitting techniques, providing SFHs directly motivated by the results of the latest cosmological simulations.