We present speculator —a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating spectra, we use a principal component analysis to construct a set of basis functions and neural networks to learn the basis coefficients as a function of the SPS model parameters. For photometry, we parameterize the magnitudes (for the filters of interest) as a function of SPS parameters by a neural network. The resulting emulators are able to predict spectra and photometry under both simple and complicated SPS model parameterizations to percent-level accuracy, giving a factor of 10 3 –10 4 speedup over direct SPS computation. They have readily computable derivatives, making them amenable to gradient-based inference and optimization methods. The emulators are also straightforward to call from a GPU, giving an additional order of magnitude speedup. Rapid SPS co...

中文翻译：

---

SPECULATOR：模拟恒星族的合成，以实现快速准确的银河光谱和光度法

我们介绍了投机者-一种快速，准确，灵活的框架，用于模拟恒星种群合成（SPS）模型，以预测星系光谱和光度法。为了模拟光谱，我们使用主成分分析来构建一组基函数，并使用神经网络学习作为SPS模型参数的函数的基系数。对于光度学，我们通过神经网络将幅度（对于感兴趣的滤光片）参数化为SPS参数的函数。由此产生的仿真器能够在简单和复杂的SPS模型参数化下预测光谱和光度，达到百分比级的精度，比直接进行SPS计算的速度提高了10 3 –10 4。它们具有易于计算的导数，使其适合基于梯度的推理和优化方法。仿真器也很容易从GPU调用，从而提供了额外的数量级加速。快速SPS编码器