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arXiv 1605.06705astro-ph.GA
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附近宇宙早期星系的广泛目录

An extensive catalogue of early-type galaxies in the nearby Universe

J. Dabringhausen, M. Fellhauer

摘要:

我们提出了一个目录的1715早型星系的文献,跨越光度范围从微弱的矮球状星系的巨大椭圆星系。该目录的目的是成为最全面和最公开的早期星系数据集之一。本星表的重点是矮椭圆星系,最近已经发表了一些具有详细数据的样品。对于几乎所有的早期类型星系,这个目录包含了它们的位置,距离,红移,半光半径,它们的恒星星族质量和在不同通带的视星等的数据。这里提供的大多数星系的金属丰度和各种颜色的数据。关于恒星星族的星等、颜色、金属丰度和质量的数据,还补充了一些条目,这些条目是根据简单恒星星族模型的数据和现有的观测数据拟合而成的。此外,还对星表中的星等、颜色和金属丰度数据进行了一些简单的变换,以提高这些数据的均匀性。在这个目录中列出了几百个星系的Sérsic剖面、内部速度色散、最大自转速度、动力学质量和年龄的估计。最后,本目录所列的每一数量都附有关于其来源的信息,以便本目录的用户可以很容易地排除他们认为对其目的不够可靠的数据。

英文摘要:

We present a catalogue of 1715 early-type galaxies from the literature, spanning the luminosity range from faint dwarf spheroidal galaxies to giant elliptical galaxies. The aim of this catalogue is to be one of the most comprehensive and publicly available collections of data on early-type galaxies. The emphasis in this catalogue lies on dwarf elliptical galaxies, for which some samples with detailed data have been published recently. For almost all of the early-type galaxies included in it, this catalogue contains data on their locations, distances, redshifts, half-light radii, the masses of their stellar populations and apparent magnitudes in various passbands. Data on metallicity and various colours are available for a majority of the galaxies presented here. The data on magnitudes, colours, metallicities and masses of the stellar populations is supplemented with entries that are based on fits to data from simple stellar population models and existing data from observations. Also, some simple transformations have been applied to the data on magnitudes, colours and metallicities in this catalog, in order to increase the homogeneity of this data. Estimates on the Sérsic profiles, internal velocity dispersions, maximum rotational velocities, dynamical masses and ages are listed for several hundreds of the galaxies in this catalogue. Finally, each quantity listed in this catalogue is accompanied with information on its source, so that users of this catalogue can easily exclude data that they do not consider as reliable enough for their purposes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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