论文标题
病毒模式$ k_v $用温暖的暗物质结构形成的方法
The virial mode $k_v$ approach to Structure Formation with Warm Dark Matter
论文作者
论文摘要
小规模的结构打开一个窗口，以限制暗物质的动态特性。在这里，我们在半分析方法中研究了温暖的暗物质（WDM）的聚类，并将WDM的线性功率谱与冷暗物质（CDM）进行了新的转移功能$ t_v（a，k）$的病毒波数$ k_v =2π/λ_v$对应于a ravius $ r _v = $ r_v = 2 $ r r_v = r_v = r_v = r_v = r_v = r_v = r_v = r_v = r_v = r_v = r_v = r_v = c = r_v = r_v = r_v = v = r_v = v = r_v = r_v = r_v = r_v = v = r_v = 2 $ r_v = r_ {fs}/2 =λ_{fs}/4 $。该结构中包含的病毒质量$ m_v $对应于在比例因子$ a_ {nr} $上形成的WDM粒子形成的最轻的结构，并与相应的$λ_{fs} $相对应。病毒传输函数$ t_v（a，k）= [1+ \ left（k/k_v \ right）^{β_v}]^{γ_V} $，以病毒模式$ k_v $和两个常数参数$β_V$和$β_V$和$γ_V$给出。我们将$ T_V（A，K）$与WDM的Boltzmann代码类比较质量范围1-10 KEV，我们获得了约束$β_Vγ_V= -18 $，$ν= 1.020 \ pm 0.025 $。在标准方法中，传输函数由$ t（a，k）= [1+ \ left（α\，k \ right）^β]^γ$ \ cite {viel：2005qj}，其中$α$编码WDM的动力学特性，必须通过Boltzmann code进行数字调整。相比之下，在我们的病毒方法中，物理量$ k_v $仅根据自由流量表$λ_{fs} $给出，并且可以通过分析确定。我们的病毒建议与班级有良好的一致性，并从标准转移函数中略微改善了结果。总而言之，我们提出了一种新的出于物理动机的传输函数$ t_v（a，k）$，其中WDM的属性在病毒波数$ k_v $中编码，可以简单地确定和改善WDM群集属性的预测。
The small scale structure opens a window to constrain the dynamical properties of Dark Matter. Here we study the clustering of warm dark matter (WDM) in a semi-analytical approach and compared the linear power spectrum of WDM with cold dark matter (CDM) employing a new transfer function $T_v(a,k)$ in terms of the viral wave number $k_v=2π/λ_v$ corresponding to a structure with a viral radius $r_v=λ_v/2$, half the size of the free streaming scale radius $r_v=r_{fs}/2=λ_{fs}/4$. The virial mass $M_v$ contained in this structure corresponds to the lightest structure formed for a WDM particle becoming non-relativistic at the scale factor $a_{nr}$ with the corresponding $λ_{fs}$. The viral transfer function $T_v(a,k)=[1+ \left(k/k_v \right)^{β_v }]^{γ_v}$ is given in terms of the viral mode $k_v$ and two constant parameters $β_v$ and $γ_v$. We compare $T_v(a,k)$ with the Boltzmann code CLASS for WDM in the mass range 1-10 keV and we obtain the constraint $β_vγ_v=-18$ with $ν=1.020 \pm 0.025$. In the standard approach the transfer function is given by $T(a,k)=[1+\left(α\, k \right)^β]^γ$ \cite{Viel:2005qj} where $α$ encodes the dynamical properties of WDM and must be numerically adjusted by means of a Boltzmann code. In contrast, in our viral approach the physical quantity $k_v$ is simply given in terms of the free streaming scale $λ_{fs}$ and can be analytically determined. Our viral proposal has a good agreement with CLASS and improves slightly the results from the standard transfer function. To conclude, we have proposed a new physically motivated transfer function $T_v(a,k)$ where the properties of WDM are encoded in the viral wave number $k_v$, is straightforward to determine and improves the prediction of WDM clustering properties.
