Galaxy velocity bias in cosmological simulations: towards per cent-level calibration
Galaxy velocity bias in cosmological simulations: towards per cent-level calibration
复制标题
宇宙学模拟中的星系速度偏差：朝着百分比水平校准
DOI：
10.1093/mnras/stab3587
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发表时间：
2022
影响因子：
4.8
通讯作者：
Anbajagane D
中科院分区：
文献类型：
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作者：
Anbajagane D
星系团的质量，丰富的宇宙学信息，可以估计从内部暗物质（DM）的速度色散，这反过来又可以从卫星星系的速度观测推断。然而，星系是有偏差的DM示踪剂，并且偏差可以随着宿主晕和星系属性以及时间而变化。我们精确地校准速度偏差，bv-定义为星系和DM速度色散的比率-作为红移，宿主晕质量和星系恒星质量阈值（）的函数，对于来自五个宇宙学模拟的大质量晕（）：IllustrisTNG，Magneticum，Bahamas + Macsis，The Three Hundred Project和MultiDark Planck-2。我们首先比较星系和DM速度色散模拟的标度关系，前者估计使用一种新的合奏速度似然方法，是无偏的低星系计数每个晕，而后者使用局部线性回归。模拟结果表明bv随M_（200）c的增加而增加，随红移和红移的增加而减小。总体估计的理论不确定度为2- 3%，但仅考虑三个最高分辨率的模拟时，不确定度为10%。我们更新了SDSS redMapPer集群样本的质量丰富度归一化，并发现我们改进的dbvestimates将归一化不确定性从22%降低到8%，表明动态质量估计与弱透镜质量估计具有竞争力。我们讨论了进一步提高这种精度的必要步骤。我们的估计是公开的。
Galaxy cluster masses, rich with cosmological information, can be estimated from internal dark matter (DM) velocity dispersions, which in turn can be observationally inferred from satellite galaxy velocities. However, galaxies are biased tracers of the DM, and the bias can vary over host halo and galaxy properties as well as time. We precisely calibrate the velocity bias,bv– defined as the ratio of galaxy and DM velocity dispersions – as a function of redshift, host halo mass, and galaxy stellar mass threshold (), for massive haloes () from five cosmological simulations: IllustrisTNG, Magneticum, Bahamas + Macsis, The Three Hundred Project, and MultiDark Planck-2. We first compare scaling relations for galaxy and DM velocity dispersion across simulations; the former is estimated using a new ensemble velocity likelihood method that is unbiased for low galaxy counts per halo, while the latter uses a local linear regression. The simulations show consistent trends ofbvincreasing withM200cand decreasing with redshift and. The ensemble-estimated theoretical uncertainty inbvis 2–3 per cent, but becomespercent-levelwhen considering only the three highest resolution simulations. We update the mass–richness normalization for an SDSS redMaPPer cluster sample, and find our improvedbvestimates reduce the normalization uncertainty from 22 to 8 per cent, demonstrating that dynamical mass estimation is competitive with weak lensing mass estimation. We discuss necessary steps for further improving this precision. Our estimates forare made publicly available.