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arXiv 0906.3858astro-ph.CO
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与宇宙e $^{\pm}$过剩有关的暗物质性质的Markov链Monte Carlo研究

A Markov Chain Monte Carlo Study on Dark Matter Property Related to the Cosmic e$^{\pm}$ Excesses

Jie Liu, Qiang Yuan, Xiaojun Bi, Hong Li, Xinmin Zhang

摘要:

在本文中,我们开发了一个马尔可夫链蒙特卡罗程序来研究暗物质的性质,以解释最近的宇宙线电子/正电子过剩的观测。假设暗物质粒子主要与轻子耦合,分别考虑湮灭和衰变为轻子对两种情况。对银河系中心周围弥漫的$γ$射线的中心密度分布的约束也包含在马尔可夫链蒙特卡罗代码中。在数值研究中,引入了7个参数。我们独立拟合了两组数据,发现对于数据组I(PAMELA+ATIC),具有$m_χ\approx0.7$ TeV湮灭(或1.4 TeV衰变)和纯$e^+e^-$通道的暗物质是有利的,而对于数据组II(PAMELA+Fermi-LAT+H.E.S.S.)用于湮灭的$m_χ\approx 2$ TeV(或用于衰变的4 TeV)以及$μ^+μ^-$和$τ^+τ^-$终态的组合可以最好地拟合数据。H.E.S.S.对银河系中心$γ$射线的观测对湮灭暗物质场景中的暗物质晕的中心密度分布有很强的约束。在这种情况下,NFW配置文件被认为是典型的预测从冷暗物质的情况下,被排除具有高的意义($>5σ$)。对于衰变的暗物质,这种约束要弱得多。

英文摘要:

In this paper we develop a Markov Chain Monte Carlo code to study the dark matter properties in interpreting the recent observations of cosmic ray electron/positron excesses. We assume that the dark matter particles couple dominantly to leptons and consider two cases separately with annihilating or decaying into lepton pairs. The constraint on the central density profile from diffuse $γ$-rays around the Galactic center is also included in the Markov Chain Monte Carlo code self-consistently. In the numerical study, 7 parameters are introduced. We fit two data sets independently and find that for the Data set I (PAMELA+ATIC), dark matter with $m_χ\approx0.7$ TeV for annihilation (or 1.4 TeV for decay) and pure $e^+e^-$ channel is favored, while for the Data set II (PAMELA+Fermi-LAT+H.E.S.S.) $m_χ\approx 2$ TeV for annihilation (or 4 TeV for decay) and the combination of $μ^+μ^-$ and $τ^+τ^-$ final states can best fit the data. The H.E.S.S. observation of the Galactic center $γ$-rays puts a strong constraint on the central density profile of the dark matter halo for the annihilation dark matter scenario. In this case the NFW profile which is regarded as the typical predication from the cold dark matter scenario, is excluded with a high significance ($>5σ$). For the decaying dark matter scenario, the constraint is much weaker.

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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