Collaborative Proposal: Unveiling Dark Matter in Dwarf Spheroidal Galaxies

尽管暗物质(DM)弥漫着整个宇宙，但它仍然像人们第一次注意到它的引力特征时一样难以捉摸。幸运的是，矮小星系继续为了解DM的本质提供了一个独特的窗口，因为它们是已知最小的DM结构的宿主，可能代表着大量原始种群的幸存者。不幸的是，矮小星系的研究依赖于观察单个恒星的运动及其模型的解释，这既有缺陷，因为任何一个星系的样本都很少，而且需要高度限制性的假设才能获得答案。现在有可能获得附近矮小球状星系中数千颗恒星的样本，而在那里，以前通常只有几十颗恒星。与此同时，新的、更复杂的非参数统计技术正在开发中，其中不仅包括对一些标准的动态假设的测试，还可能提供放松其中一些假设的方法。这项研究的最终目标是建立一个运动学模型，在尽可能少的假设下，附近所有矮球星系的离散速度达到最大。天文学家和统计学家之间的密切合作将两所大学的研究人员联系在一起，包括能够继续这种合作传统的学生，并通过提供新设备以及广泛获得的数据和统计方法为整个天文学社区服务。研究结果将提供给公众和K-5外展，这已经是每个研究人员活动的常规部分。

AST-0507453MateoThough it permeates the entire Universe, dark matter (DM) remains as elusive today as when its gravitational signature was first noted. Fortunately, dwarf galaxies continue to offer a unique window into the nature of DM, as they are host to the smallest known DM structures and may represent the survivors of a vast primordial population. Unfortunately, dwarf galaxy studies rely on observing the motions of individual stars and their interpretation by models, and this is flawed both by having only small samples for any one galaxy, and by requiring highly restrictive assumptions to obtain an answer. It is now possible to obtain samples of thousands of stars in nearby dwarf spheroidal galaxies, where it was previously usual to have only tens of stars. At the same time, new, far more sophisticated non-parametric statistical techniques are being developed, which not only include tests of some of the standard dynamical assumptions, but also may offer ways to relax some of those assumptions. The ultimate goal of this research is a kinematical model with the fewest possible assumptions being confronted with the maximum number of discrete velocities in all of the nearby dwarf spheroidal galaxies.This close collaboration between astronomers and statisticians links researchers at two universities, including students who can continue this collaborative tradition, and serves the entire astronomical community through the provision of new equipment and the widespread availability of the data and the statistical methods. The results will feed into public and K-5 outreach, which are already a regular part of each researcher's activities.