论文标题
在Halo模型中包括超级线性光环偏置
Including beyond-linear halo bias in halo models
论文作者
论文摘要
我们得出了一个简单的处方，用于在标准的分析光环模型功率谱计算中包含超出线性光环偏置。这将导致纠正术语添加到通常的两个Halo术语中。我们使用来自$ n $ body模拟的数据来衡量此校正，并证明它可以在两次的术语中提高功率$ \ sim2 $在scales $ k \ sim0.7 \，h mpc^{ - 1} $的情况下，并由两点功能中特定的字段确定的启动确定的确切幅度。这如何转化为全功率谱，取决于单次术语的相对强度，这可以掩盖这种校正在或多或少程度上的重要性，这再次取决于磁场。通常，我们发现我们的校正对于由低质量光环产生的信号更为重要。在将我们的计算与模拟数据进行比较时，我们发现两项和单一术语之间的过渡区域中的功率不足预测，通常会困扰晕圈模型计算时，在包括完整的非线性光环偏置时几乎被完全消除。我们显示了星系，光环和物质的汽车和跨光谱的改进结果。在特定情况下，物质或物质 - 纳洛功率，我们注意到，大部分改进的一部分来自低质量光环和高质量光环之间的非线性偏置。我们设想我们的模型对跨相关信号的分析建模有用。我们的非线性偏见光环模型代码可从https://github.com/alexander-mead/bnl获得
We derive a simple prescription for including beyond-linear halo bias within the standard, analytical halo-model power spectrum calculation. This results in a corrective term that is added to the usual two-halo term. We measure this correction using data from $N$-body simulations and demonstrate that it can boost power in the two-halo term by a factor of $\sim2$ at scales $k\sim0.7\,h Mpc^{-1}$, with the exact magnitude of the boost determined by the specific pair of fields in the two-point function. How this translates to the full power spectrum depends on the relative strength of the one-halo term, which can mask the importance of this correction to a greater or lesser degree, again depending on the fields. Generally we find that our correction is more important for signals that arise from lower-mass haloes. When comparing our calculation to simulated data we find that the under-prediction of power in the transition region between the two- and one-halo terms, which typically plagues halo-model calculations, is almost completely eliminated when including the full non-linear halo bias. We show improved results for the auto and cross spectra of galaxies, haloes and matter. In the specific case of matter-matter or matter-halo power we note that a large fraction of the improvement comes from the non-linear biasing between low- and high-mass haloes. We envisage our model being useful in the analytical modelling of cross correlation signals. Our non-linear bias halo-model code is available at https://github.com/alexander-mead/BNL
