通过物理启发的概率模型绘制天文光变曲线的地形图
Topographic Mapping of astronomical light curves via a physically inspired Probabilistic model
摘要：
我们提出了一个概率生成的方法来构建地形图的光变曲线从食双星。该模型定义了一个低维流形的局部噪声模型诱导的平滑非线性映射从低维潜在空间到空间的概率模型的观察到的光变曲线。局部噪声模型是描述如何生成这种光变曲线的物理模型。由于该模型的原则概率性质，成本函数自然出现，模型参数通过MAP估计使用期望最大化算法拟合。一旦模型已经被训练，每个光曲线可以被投影到潜在空间作为局部噪声模型上的平均后验概率。我们展示了我们的方法在人工生成的光变曲线的数据集和由来自真实观测的光变曲线组成的数据集上。
英文摘要：
We present a probabilistic generative approach for constructing topographic maps of light curves from eclipsing binary stars. The model defines a low-dimensional manifold of local noise models induced by a smooth non-linear mapping from a low-dimensional latent space into the space of probabilistic models of the observed light curves. The local noise models are physical models that describe how such light curves are generated. Due to the principled probabilistic nature of the model, a cost function arises naturally and the model parameters are fitted via MAP estimation using the Expectation-Maximisation algorithm. Once the model has been trained, each light curve may be projected to the latent space as the the mean posterior probability over the local noise models. We demonstrate our approach on a dataset of artificially generated light curves and on a dataset comprised of light curves from real observations.
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