暗物质背后的数据:探索星系旋转

A. Villano, K. Harris, Judit Bergfalk, Raphael Hatami, Francis Vititoe, Julia Johnston
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摘要

据估计,暗物质约占所有正常/重子物质的84%,但无法直接成像。尽管暗物质还不能直接观测到,但它对螺旋星系中恒星和气体运动的影响已经被探测到。显示星系运动的一种方法是旋转曲线,它是星系中恒星和气体围绕质心运动的速度测量图。根据牛顿万有引力定律,旋转速度是星系中可见和不可见质量的一个指标。既然可见物质可以用光度法测量,那么暗物质的质量就可以被估计出来,从而对星系的大小分布提供了一个深入的了解。为了更好地理解研究科学家关于暗物质的发现,他们的方法应该很容易被任何好奇的人复制。我们的互动研讨会是一个极好的教育工具,通过提供制作星系旋转曲线的指南来研究暗物质如何影响可见物质的旋转。使用在线数据库(SPARC),基于python的笔记本将引导您完成生成旋转曲线的整个过程,并让您了解银河系的每个组成部分。旋转曲线的建立过程分为三个步骤:绘制速度测量数据、构造各分量的旋转曲线、将总速度拟合到测量值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Data Behind Dark Matter: Exploring Galactic Rotation
Dark matter is estimated to make up ~84% of all normal/baryonic matter, but cannot be directly imaged. Despite the fact that dark matter cannot be directly observed yet, its influence on the motion of stars and gas in spiral galaxies have been detected. One way to show motion in galaxies are rotation curves that are plots of velocity measurements of how fast stars and gas move in a galaxy around the center of mass. According to Newton's Law of Gravitation, the rotational velocity is an indication of the amount of visible and non-visible mass in the galaxy. Given that the visible matter is measurable using photometry, dark matter mass can therefore be estimated, offering an insight into the size distribution in galaxies. In order to gain a greater appreciation of the research scientists' findings about dark matter, their method should be easily reproduced by any curious individual. Our interactive workshop is an excellent educational tool to investigate how dark matter impacts the rotation of visible matter by providing a guide to produce galactic rotation curves. The Python-based notebooks are set up to walk you through the whole process of producing rotation curves using an online database (SPARC) and to allow you to learn about each component of the galaxy. The three steps of the rotation curve building process is plotting the measured velocity data, constructing the rotation curves for each component, and fitting the total velocity to the measured values.
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