We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately +/-2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.
MIRECLE: Science Yield for a Mid-IR Explorer-Class Mission to Study Non-Transiting Rocky Planets Orbiting the Nearest M-Stars Using Planetary Infrared Excess
Results from the Wide-field Infrared Survey Explorer (WISE) Future Uses Session at the WISE at 5 Meeting
在“WISE at 5：2015年2月10日至12日在加利福尼亚州帕萨迪纳举行的“遗产与前景”会议上，与会者应邀参加了一个互动会议，探讨广域红外探测器（WISE）数据的未来用途。65名参与者（其中许多人是数据的广泛用户）集思广益，提出了任务仍需回答的首要问题，以及补充当前和未来的数据集和WISE/NEOWISE数据的额外处理，这将有助于解决这些最重要的科学问题。结果主要分为与河外研究（如活动星系核、类星体）和星下质量物体有关的两个专题。总之，与会者发现，用互相关的多波长勘测（例如SDSS、Pan-STARRS、LSST、Gaia、Euclid等）补充WISE/NEOWISE数据，对未来所有的任务目标都非常有利。此外，经常提到开发或实施机器学习工具，以梳理和理解交叉相关的数据，供未来使用。最后，与会者一致认为，对数据进行额外处理，如共同添加WISE和NEOWISE，提取多历元光度数据库和视差和自行目录，将大大改善所确定的最重要项目的科学成果。在这方面，像MaxWISE这样的项目将执行最重要的额外处理和提取，并使数据和目录易于通过公共门户网站访问，这被认为是极其重要的。
Survey Footprint Explorer: A Browser-Based Interactive Tool for Visualizing and Cross-Matching Astronomical Survey Footprints
Waterloo Centre for Astrophysics, University of Waterloo（滑铁卢天体物理中心，滑铁卢大学）；Department of Physics and Astronomy, University of Waterloo（滑铁卢大学物理与天文系）；Center for Astronomy, Space Science and Astrophysics, Independent University, Bangladesh（孟加拉独立大学天文学、空间科学与天体物理中心）；Kapteyn Astronomical Institute, University of Groningen（格罗宁根大学卡普坦天体物理研究所）；Definity Insurance Company（Definity保险公司）
Optimal scheduling and science delivery of spectra for millions of targets in thousands of fields: the operational concept of the Maunakea spectroscopic explorer (MSE)
The Photo-z Infrared Telescope (PIRT) -- a space instrument for rapid follow up of high-redshift gamma-ray bursts and electromagnetic counterparts to gravitational wave events
这是Maunakea Spectroscopic Explorer 2018图书。它旨在为国际天文学和工程界以及相关机构提供MSE科学和技术设计的所有方面的简明参考指南。当前版本是MSE科学目标及其实际实施的状态报告，继2018年1月举行的系统概念设计审查之后。MSE是一个计划中的10米级、宽视场、光学和近红外设施，旨在实现变革性科学，同时填补新兴的国际大型天文设施网络中的一个关键空白。MSE完全致力于数千至数百万天体样本的多目标光谱学。由于其独特的设计能力，它将在这一领域引领世界：它将拥有大口径（11.25米）和宽（1.52平方米）。度）视野;它将有能力以从R2500到R40,000的宽范围的光谱分辨率进行观测，具有大规模的多路复用（每次曝光4332个光谱，所有光谱分辨率在任何时候都可用），并且对目标观测效率超过80%。MSE将揭示微弱宇宙的组成和动力学，旨在精确研究微弱的天体物理现象。它还将为多波长成像调查提供关键的后续行动，例如大型综合巡天望远镜，盖亚，欧几里得，宽视场红外巡天望远镜，平方公里阵列和下一代甚大阵列。
