Bradley C. Whitmore, Sahar S. Allam, Tamas Budavari, Stefano Casertano, Ronald A. Downes, Thomas Donaldson, S. Michael Fall, Stephen H. Lubow, Lee Quick, Louis-… 展开作者
Bradley C. Whitmore, Sahar S. Allam, Tamas Budavari, Stefano Casertano, Ronald A. Downes, Thomas Donaldson, S. Michael Fall, Stephen H. Lubow, Lee Quick, Louis-Gregory Strolger, Geoff Wallace, Richard L. White
备注：
25 pages, 30 figures, 2 tables, 4 appendices; AJ accepted
The Hubble Source Catalog is designed to help optimize science from the Hubble Space Telescope by combining the tens of thousands of visit-based source lists in the Hubble Legacy Archive into a single master catalog. Version 1 of the Hubble Source Catalog includes WFPC2, ACS/WFC, WFC3/UVIS, and WFC3/IR photometric data generated using SExtractor software to produce the individual source lists. The catalog includes roughly 80 million detections of 30 million objects involving 112 different detector/filter combinations, and about 160 thousand HST exposures. Source lists from Data Release 8 of the Hubble Legacy Archive are matched using an algorithm developed by Budavari & Lubow (2012). The mean photometric accuracy for the catalog as a whole is better than 0.10 mag, with relative accuracy as good as 0.02 mag in certain circumstances (e.g., bright isolated stars). The relative astrometric residuals are typically within 10 mas, with a value for the mode (i.e., most common value) of 2.3 mas. The absolute astrometric accuracy is better than $\sim$0.1 arcsec for most sources, but can be much larger for a fraction of fields that could not be matched to the PanSTARRS, SDSS, or 2MASS reference systems. In this paper we describe the database design with emphasis on those aspects that enable the users to fully exploit the catalog while avoiding common misunderstandings and potential pitfalls. We provide usage examples to illustrate some of the science capabilities and data quality characteristics, and briefly discuss plans for future improvements to the Hubble Source Catalog.
The Hubble Missing Globular Cluster Survey. III. Astro-photometric catalogs, artificial-star tests, and improved absolute proper motions
INAF - Osservatorio Astronomico di Padova（意大利国家天体物理研究所帕多瓦天文台）；Space Telescope Science Institute（太空望远镜科学研究所）；INAF - Astrophysics and Space Science Observatory of Bologna（意大利国家天体物理研究所博洛尼亚天体物理学与空间科学观测站）；atlanTTic, Universidade de Vigo, Escola de Enxeñaría de Telecomunicación（维戈大学电信工程学院 AtlanTTic 中心）；Universidad de La Laguna（拉古纳大学）；INFN - Sezione di Pisa, Universitá di Pisa（比萨大学意大利国家核物理研究所比萨分部）；Department of Physics and Astronomy, University of Bologna（博洛尼亚大学物理与天文学系）；Institute for Computational Cosmology & Centre for Extragalactic Astronomy, Department of Physics, Durham University（杜伦大学物理学院计算宇宙学研究所与河外天文中心）
Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point Source Detection and Deblending
概率编目（PCAT）在拥挤的领域中优于传统的单波段光学数据编目方法（Portillo et al. 2017）。我们将我们的工作扩展到多个波段，与以前的单波段结果相比，实现了更高的灵敏度（$\sim$ 0.4 mag）和更高的速度（500倍）。我们证明了多波段PCAT模拟数据的有效性，无论是在恢复准确的后验在目录空间，并在直接去混源。以Sloan数字巡天（SDSS）观测的M2为例，以Hubble空间望远镜数据为真值，对$r$和$i$波段数据的联合拟合比单波段概率星表法更深入$\sim0.4$，对F606 W $\leq 20$的假发现率小于20%.与DAOPHOT相比，使用相同的数据，双波段SDSS星表拟合深度接近1.5个星等，并保持了较低的错误发现率，直到F606 W $\sim 20.5$。考虑到最近在计算速度方面的改进，多波段PCAT显示出应用于大规模调查的前景，并且是多仪器观测数据联合分析的合理框架。
Hubble Space Telescope Proper Motion (HSTPROMO) Catalogs of Galactic Globular Clusters. V. The rapid rotation of 47 Tuc traced and modeled in three dimensions
