arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.06925astro-ph.CO
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精确到1%的星系团质量:在弱透镜质量推断中包括重子

Towards 1% accurate galaxy cluster masses: Including baryons in weak-lensing mass inference

Dylan Cromer, Nicholas Battaglia, Hironao Miyatake, Melanie Simet

摘要:

星系团是一个很有前途的探测晚时间结构的增长,但限制宇宙学从集群丰度目前受到系统学在其推断的质量。在弱透镜质量推论中,一个绝对的系统效应是忽略重子的存在,并将整个星系团视为一个暗物质晕。在这项工作中,我们提出了一个新的灵活的集群密度模型,捕获重子和暗物质的配置文件,一个新的通用技术计算透镜信号的任意密度配置文件,和堆叠这些透镜信号的方法,适当的模型堆叠弱透镜测量的星系团目录。我们在1400个模拟集群上测试了这个模型。与以前的研究类似,我们发现只有暗物质的模型将平均质量高估了$7.5\%$,但包括我们的重子项后,将其降低为$0.7\%$。此外,为了减轻我们模型的计算复杂性,我们构建了一个仿真器(代理模型),它可以准确地内插我们的模型进行参数推断,同时比原始模型使用起来要快得多。我们还为我们的模型和仿真器提供了一个开源软件框架,称为maszcal,它将作为一个平台,继续努力改进这些质量校准技术。在这项工作中,我们详细介绍了我们的模型,模拟器的构造,以及我们用来验证我们的模型确实减轻了偏见的测试。最后,我们描述了仿真器的准确性测试

英文摘要:

Galaxy clusters are a promising probe of late-time structure growth, but constraints on cosmology from cluster abundances are currently limited by systematics in their inferred masses. One unmitigated systematic effect in weak-lensing mass inference is ignoring the presence of baryons and treating the entire cluster as a dark matter halo. In this work we present a new flexible model for cluster densities that captures both the baryonic and dark matter profiles, a new general technique for calculating the lensing signal of an arbitrary density profile, and a methodology for stacking those lensing signal to appropriately model stacked weak-lensing measurements of galaxy cluster catalogues. We test this model on 1400 simulated clusters. Similarly to previous studies, we find that a dark matter-only model overestimates the average mass by $7.5\%$, but including our baryonic term reduces that to $0.7\%$. Additionally, to mitigate the computational complexity of our model, we construct an emulator (surrogate model) which accurately interpolates our model for parameter inference, while being much faster to use than the raw model. We also provide an open-source software framework for our model and emulator, called maszcal, which will serve as a platform for continued efforts to improve these mass-calibration techniques. In this work, we detail our model, the construction of the emulator, and the tests which we used to validate that our model does mitigate bias. Lastly, we describe tests of the emulator's accuracy

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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