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arXiv 2108.04826hep-phastro-ph.HEastro-ph.IM
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Horizon-10 T和Axion夸克块观测到的多模聚团事例

Multi-Modal Clustering Events observed by Horizon-10T and Axion Quark Nuggets

Ariel Zhitnitsky

摘要:

Horizon-10 T合作项目\cite{Beisembaev:2016cyg,2017EPJWC.14514001B,Beznosko:2019cI,2019EPJWC.20806002B,Beisembaev:2019nzd}报告了多模态事件(MME)的观测结果,其中包含多个峰值,表明其聚类起源。这些事件已被证明很难用传统的宇宙射线(CR)来解释。我们认为这些MMEs可能是轴子夸克块金(Axion Quark Nugget,AQN)暗物质模型中暗物质湮灭事件的结果。AQN暗物质模型最初是为了解释宇宙中暗物质和可见物质之间的相似性而提出的,即没有任何拟合参数的$Ω_{\rm DM}\sim Ω_{\rm visible}$。我们支持这一建议,通过证明观测\cite{Beisembaev:2016cyg,2017EPJWC.14514001B,Beznosko:2019cI,2019EPJWC.20806002B,Beisembaev:2019nzd},包括出现的频率,强度,空间分布,持续时间,集群功能,和许多其他属性很好地匹配的大气中AQN湮灭事件的发射特性。我们列出了一些AQN事件的特征,这些特征与传统的CR空气簇射非常不同。这些特征的观察(不观察)可以证实(反驳)我们的建议。

英文摘要:

The Horizon-10T collaboration \cite{Beisembaev:2016cyg,2017EPJWC.14514001B,Beznosko:2019cI,2019EPJWC.20806002B,Beisembaev:2019nzd} have reported observation of Multi-Modal Events (MME) containing multiple peaks suggesting their clustering origin. These events are proven to be hard to explain in terms of conventional cosmic rays (CR). We propose that these MMEs might be result of the dark matter annihilation events within the so-called axion quark nugget (AQN) dark matter model, which was originally invented for completely different purpose to explain the observed similarity between the dark and the visible components in the Universe, i.e. $Ω_{\rm DM}\sim Ω_{\rm visible}$ without any fitting parameters. We support this proposal by demonstrating that the observations \cite{Beisembaev:2016cyg,2017EPJWC.14514001B,Beznosko:2019cI,2019EPJWC.20806002B,Beisembaev:2019nzd}, including the frequency of appearance, intensity, the spatial distribution, the time duration, the clustering features, and many other properties nicely match the emission characteristics of the AQN annihilation events in atmosphere. We list a number of features of the AQN events which are very distinct from conventional CR air showers. The observation (non-observation) of these features may substantiate (refute) our proposal.

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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