arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1707.04748astro-ph.IM
收藏

Gaia空间任务时间序列数据库的贝叶斯无偏性

Bayesian Unbiasing of the Gaia Space Mission Time Series Database

Héctor E. Delgado, Luis M. Sarro

摘要:

21世纪的天体物理学家面临着从极其昂贵和复杂的空间或地面仪器项目中提取最大科学回报的艰巨任务。本文集中在挖掘的时间序列目录产生的欧洲航天局盖亚任务,在2013年12月推出。我们特别处理的问题,推断真正的分布的造父变星在银河系的卫星星系被称为大麦云(LMC)的变化特性。经典造父变星是所谓的距离阶梯的第一步:一系列测量宇宙距离和破译宇宙结构和演化的技术。在这项工作中,我们试图通过模拟扭曲真实周期分布的混叠现象来消除星表的偏差。我们已经表示了问题的2级生成贝叶斯图形模型,并使用马尔可夫链蒙特卡罗(MCMC)算法的推理(分类和回归)。我们对合成数据的结果表明,该系统成功地消除了系统偏差,并能够推断出频率和震级分布的真实超参数。

英文摘要:

21 st century astrophysicists are confronted with the herculean task of distilling the maximum scientific return from extremely expensive and complex space- or ground-based instrumental projects. This paper concentrates in the mining of the time series catalog produced by the European Space Agency Gaia mission, launched in December 2013. We tackle in particular the problem of inferring the true distribution of the variability properties of Cepheid stars in the Milky Way satellite galaxy known as the Large Magellanic Cloud (LMC). Classical Cepheid stars are the first step in the so-called distance ladder: a series of techniques to measure cosmological distances and decipher the structure and evolution of our Universe. In this work we attempt to unbias the catalog by modelling the aliasing phenomenon that distorts the true distribution of periods. We have represented the problem by a 2-level generative Bayesian graphical model and used a Markov chain Monte Carlo (MCMC) algorithm for inference (classification and regression). Our results with synthetic data show that the system successfully removes systematic biases and is able to infer the true hyperparameters of the frequency and magnitude distributions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑