论文标题
测试一环星系偏置:功率谱
Testing one-loop galaxy bias: Power spectrum
论文作者
论文摘要
我们测试了一环星系偏见的有效性方案,以实现各种偏见的示踪剂。我们最严格的测试要求偏置模型同时使用来自模拟的测量的非线性物质频谱,以单独从偏置扩展中测试一环效应。此外,我们通过高衍生和规模依赖性的噪声项研究了短期非局部性和光晕排除的相关性,以及使用共同进化关系以减少自由拟合参数的数量的影响。通过比较这些假设的有效性和优点,我们发现具有固定的二次潮汐偏见的四参数模型(线性,二次,非定位偏见和恒定的射击噪声)为我们的量子较小的样本中的较小量子和$ k k ________________________________________________的固定二次潮汐偏差提供了强大的建模选择。 0.35 \,h/\ mathrm {mpc} $,用于$ 6 \,(\ mathrm {gpc}/h)^3 $)的样本卷。对于更偏见的示踪剂,包括依赖比例的噪声是最有益的。在考虑自动和交叉功率谱的组合时,这也是首选的选择,这可能与星系聚类和弱透镜的联合研究有关。我们还测试了通过GRPT,EFT和混合方法Respresso来解释扰动理论的使用。尽管所有这些具有相似的性能,但我们发现后者在有效性和恢复的平均后验值方面是最好的,它部分基于模拟。
We test the regime of validity of one-loop galaxy bias for a wide variety of biased tracers. Our most stringent test asks the bias model to simultaneously match the galaxy-galaxy and galaxy-mass spectrum, using the measured nonlinear matter spectrum from the simulations to test one-loop effects from the bias expansion alone. In addition, we investigate the relevance of short-range nonlocality and halo exclusion through higher-derivative and scale-dependent noise terms, as well as the impact of using co-evolution relations to reduce the number of free fitting parameters. From comparing validity and merit of these assumptions we find that a four-parameter model (linear, quadratic, cubic nonlocal bias, and constant shot noise) with fixed quadratic tidal bias provides a robust modeling choice for the auto power spectrum of the less massive halos in our set of samples and their galaxy populations (up to $k_{\mathrm{max}} = 0.35\,h/\mathrm{Mpc}$ for a sample volume of $6\,(\mathrm{Gpc}/h)^3$). For the more biased tracers it is most beneficial to include scale-dependent noise. This is also the preferred option when considering combinations of the auto and cross power spectrum, which might be relevant in joint studies of galaxy clustering and weak lensing. We also test the use of perturbation theory to account for matter loops through gRPT, EFT and the hybrid approach RESPRESSO. While all these have similar performance, we find the latter to be the best in terms of validity and recovered mean posterior values, in accordance with it being based partially on simulations.