论文标题
绘制暗物质和查找细丝：透镜分析技术的校准模拟数据
Mapping dark matter and finding filaments: calibration of lensing analysis techniques on simulated data
论文作者
论文摘要
我们在模拟成像和星系簇的重力镜头数据上量化了质量映射技术的性能。最佳方法取决于科学目标。我们评估簇径向密度曲线的测量，偏离球形性以及它们对宇宙网络的丝状依恋。我们发现，直接（KS93）对剪切测量结果产生的质量图是公正的，并且可以通过用MRLEN进行过滤来抑制它们的噪声。前拟合技术（例如Lenstool）会进一步抑制噪声，但在群集核心中以偏椭圆形为代价，并且在大半径上的质量过高估计。有趣的是，当前对细丝的搜索是由弱透明星系的固有形状而不是通过视线结构的投影来限制的。因此，解决高密度的镜头星系的空间或基于气球的成像调查很快就会在大多数簇周围检测到一两个细丝。
We quantify the performance of mass mapping techniques on mock imaging and gravitational lensing data of galaxy clusters. The optimum method depends upon the scientific goal. We assess measurements of clusters' radial density profiles, departures from sphericity, and their filamentary attachment to the cosmic web. We find that mass maps produced by direct (KS93) inversion of shear measurements are unbiased, and that their noise can be suppressed via filtering with MRLens. Forward-fitting techniques, such as Lenstool, suppress noise further, but at a cost of biased ellipticity in the cluster core and over-estimation of mass at large radii. Interestingly, current searches for filaments are noise-limited by the intrinsic shapes of weakly lensed galaxies, rather than by the projection of line-of-sight structures. Therefore, space-based or balloon-based imaging surveys that resolve a high density of lensed galaxies, could soon detect one or two filaments around most clusters.
