Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. These models learn to classify supernova types using their light curves as inputs. Preprocessing these curves is a crucial step that significantly affects the final quality. In this talk, we study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve. We use these approximations as inputs for supernovae classification models and demonstrate that the proposed methods outperform the state-of-the-art based on Gaussian processes applying to the Zwicky Transient Facility Bright Transient Survey light curves. MLP demonstrates similar quality as Gaussian processes and speed increase. Normalizing Flows exceeds Gaussian processes in terms of approximation quality as well.

翻译：光度数据驱动的超新星分类由于在天文学中出现对大数据进行实时处理而成为一项挑战。最近的研究表明,基于各种机器学习模型的解决方案质量较高。这些模型学会使用光曲线进行超新星类型的分类。这些模型使用光曲线作为输入。这些曲线是影响最终质量的关键步骤。在这次演讲中,我们研究了多层光谱(MLP)、海湾神经网络(BNN)和正常流(NF)的应用,以近似单一光曲线的观测。我们将这些近似作为超新星分类模型的输入,并证明所拟议的方法超过了基于高斯进程、适用于Zwicky-Traight-Tradient调查光曲线的最新工艺。 MLP显示了与高斯进程和速度提高相似的质量。在近光质量方面,正常流也超过了高斯进程。