SI2-SSE: Statistical software for astronomical surveys

由于来自各种望远镜的公开在线数据集的激增，天文学研究正在经历一场变革，一项大规模的国际努力已经在进行中，以联合这些不同的数据集，供天文学家随时使用。一类特别重要的数据来自于多历元宽视场巡天，它本质上是天空的“电影”。这些时域天文学的进展对于系外行星的发现、超新星和其他瞬态、变星和吸积现象等多种重要的研究课题至关重要。然而，大多数天文学家只使用狭窄范围的经典统计方法来解释这些大数据集。这个问题现在可以通过R统计计算环境及其快速增长的CRAN附加包得到缓解。该项目将把R软件的功能引入天文研究界，并将专门的天文统计方法引入R。特别是，该研究包括两个互补的项目。首先，将开发用于分析观测时间间隔不规则的时域数据的CRAN包。这是一个在其他领域很少遇到的困难，但在多历元天文学研究中很常见，这是由于日周期、卫星轨道、测量节奏模式和望远镜分配的限制。天文学家已经为这类问题开发了各种各样的治疗方法，但大多数都没有进行统计评估或纳入广泛使用的软件包，因此这项研究的一部分将是对竞争方法的统计评估。其次，原型VOStat Web服务将发展成为一个主要工具，并集成到不断发展的虚拟天文台（VAO）软件环境中。VOStat将在许多应用统计领域提供数十种功能：数据操作和可视化、非参数统计和密度估计、概率密度函数、回归和推理、多元分析、聚类和分类、审查和截断、时间序列分析、空间点处理和图像处理。这些成就将提高VAO的统计复杂性，并为成千上万的其他天文研究提供帮助。这些软件的发展将改善每年大量天文研究的统计分析。在R中编码同时具有继承方法和图形的大型基础设施的优势，这本身对整个天文社区具有巨大的价值。虽然CRAN包的生产直接允许代码的广泛传播，但通过VOStat将代码集成到VAO软件环境中将使所有天文学家都可以方便地访问它。强大的教学成分将进一步鼓励经验不足的天文学家学习和使用更先进的统计方法。此外，关于不规则时间序列的天体统计方法的CRAN包可能对也可能遇到这类数据集的统计学家、物理学家和经济学家有价值。

Astronomical research is undergoing a transformation due to the proliferation of publicly available online datasets from all types of telescopes, and a large international effort is already underway to federate these diverse datasets for ready use by astronomers. A particularly important class of data arises from multi-epoch wide-field surveys, which are essentially 'movies' of the sky. These advances in time domain astronomy are crucial for such diverse and important research topics as exoplanet discovery, supernovae and other transients, variable stars, and accretion phenomena. However, most astronomers use only a narrow range of classical statistical methods for interpreting these large datasets. This problem can now be alleviated with the R statistical computing environment and its rapidly growing CRAN add-on packages. This project will bring the R software capabilities into the astronomical research community and introduce specialized astrostatistical methodology into R.In particular, the research includes two complementary projects. First, CRAN packages will be developed for the analysis of time domain data with irregularly spaced observation times. This is a difficulty rarely encountered in other fields but common in multi-epoch astronomical studies, due to diurnal cycles, satellite orbits, survey cadence patterns, and telescope allocation limitations. Astronomers have developed a wide range of treatments for such problems, but most have not been evaluated statistically or incorporated into widely-used software packages, so a part of this study will be a statistical evaluation of competing methods. Second, the prototype VOStat Web service will be developed into a major tool and integrated into the growing Virtual Astronomical Observatory (VAO) software environment. VOStat will provide dozens of functionalities in many areas of applied statistics: data manipulation and visualization, nonparametric statistics and density estimation, probability density functions, regression and inference, multivariate analysis, clustering and classification, censoring and truncation, time series analysis, spatial point processes and image processing. These achievements will improve the statistical sophistication within the VAO and for thousands of other astronomical studies.These software developments will improve the statistical analysis of a large number of astronomical research studies every year. Coding within R has the simultaneous advantage of inheriting the large infrastructure of methodology and graphics, itself of enormous value to the entire astronomical community. While the production of CRAN packages directly allows wide dissemination of the code, integrating the code into the VAO software environment through VOStat will make it conveniently accessible to all astronomers. A strong pedagogical component will further encourage less experienced astronomers to learn and use more advanced statistical methods. In addition, the CRAN packages on astrostatistical methods for irregular time series may have value to statisticians, physicists and economists who also might encounter datasets of this type.