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arXiv 1902.09562astro-ph.COastro-ph.GAhep-ph
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使用21 cm-边缘数据排除3个keV暖暗物质

Ruling out 3 keV warm dark matter using 21 cm-EDGES data

Atrideb Chatterjee, Pratika Dayal, Tirthankar Roy Choudhury, Anne Hutter

摘要:

弱相互作用的冷暗物质(CDM)粒子在解释各种宇宙学观测方面非常成功,但在小尺度上表现出许多问题。解决这些问题的一种可能方法是调用(所谓的)质量为$m_x \sim$ keV的暖暗物质(WDM)粒子。由于在这种WDM模型中结构的形成是延迟的,因此很自然地期望它们可以使用与第一颗恒星有关的观测来约束,例如,来自宇宙黎明的21厘米信号在这项工作中,我们使用一个详细的星系形成模型,德尔菲,计算21厘米的信号在高红移,并比较这与最近的EDGES观测。我们发现,虽然CDM和5千电子伏WDM模型可以获得一个21厘米的信号内观察到的红移范围内,再现的观测振幅需要引入一个多余的无线电背景。另一方面,可以排除具有$m_x \leq 3$ keV的WDM模型,因为无论使用什么参数,它们都无法匹配EDGES信号的红移范围或幅度。可比从低红移莱曼阿尔法森林获得的值,我们的研究结果扩展的WDM粒子的限制,以任何其他手段无法访问的时代,额外的即将到来的21厘米的数据从宇宙黎明的时代将是至关重要的,在完善这样的约束。

英文摘要:

Weakly interacting cold dark matter (CDM) particles, which are otherwise extremely successful in explaining various cosmological observations, exhibit a number of problems on small scales. One possible way of solving these problems is to invoke (so-called) warm dark matter (WDM) particles with masses $m_x \sim$ keV. Since the formation of structure is delayed in such WDM models, it is natural to expect that they can be constrained using observations related to the first stars, e.g., the 21 cm signal from cosmic dawn. In this work, we use a detailed galaxy formation model, Delphi, to calculate the 21 cm signal at high-redshifts and compare this to the recent EDGES observations. We find that while CDM and 5 keV WDM models can obtain a 21 cm signal within the observed redshift range, reproducing the amplitude of the observations requires the introduction of an excess radio background. On the other hand, WDM models with $m_x \leq 3$ keV can be ruled out since they are unable to match either the redshift range or the amplitude of the EDGES signal, irrespective of the parameters used. Comparable to values obtained from the low-redshift Lyman Alpha forest, our results extend constraints on the WDM particle to an era inaccessible by any other means; additional forthcoming 21 cm data from the era of cosmic dawn will be crucial in refining such constraints.

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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