论文标题
光曲线的天文分类与封闭式复发单元合奏
Astronomical Classification of Light Curves with an Ensemble of Gated Recurrent Units
论文作者
论文摘要
随着收集数量不断增加的天文数据，手动分类已经过时了。机器学习是前进的唯一途径。牢记这一点，大型的天气调查望远镜（LSST）团队在2018年举办了光度LSST LSST天文学时间序列分类挑战（Plactrc）。该挑战的目的是开发将天文来源准确地分类为不同类别的模型，从有限的训练集扩展到大型测试集。在本文中，我们报告了基于双向门控复发单元（GRU）的深度学习模型进行实验的结果，以处理Properionc数据集的时间序列数据。我们证明GRU确实适合处理时间序列数据。随着最小的预处理且没有增强，我们堆叠的GRU和致密网络的合奏的准确度为76.243％。来自LSST等天文学调查的数据将帮助研究人员回答与暗物质，暗能量和宇宙起源有关的问题；天文来源的准确分类是实现这一目标的第一步。 我们的代码是开源的，并已在GitHub上提供：https：//github.com/aknightwing/astronolical-classification-plasticc
With an ever-increasing amount of astronomical data being collected, manual classification has become obsolete; and machine learning is the only way forward. Keeping this in mind, the Large Synoptic Survey Telescope (LSST) Team hosted the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) in 2018. The aim of this challenge was to develop models that accurately classify astronomical sources into different classes, scaling from a limited training set to a large test set. In this text, we report our results of experimenting with Bidirectional Gated Recurrent Unit (GRU) based deep learning models to deal with time series data of the PLAsTiCC dataset. We demonstrate that GRUs are indeed suitable to handle time series data. With minimum preprocessing and without augmentation, our stacked ensemble of GRU and Dense networks achieves an accuracy of 76.243%. Data from astronomical surveys such as LSST will help researchers answer questions pertaining to dark matter, dark energy and the origins of the universe; accurate classification of astronomical sources is the first step towards achieving this. Our code is open-source and has been made available on GitHub here: https://github.com/AKnightWing/Astronomical-Classification-PLASTICC
