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arXiv 2103.02078astro-ph.COastro-ph.IM
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重叠源对宇宙切变估计的影响:统计灵敏度和象元噪声偏差

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

Javier Sanchez, Ismael Mendoza, David P. Kirkby, Patricia R. Burchat

摘要:

在第四阶段的成像巡天中,有相当数量的宇宙学有用信息来自于那些图像与天空中其他来源的图像重叠的来源。宇宙剪切信号主要编码在观测星系的估计形状中,因此直接受到重叠的影响。我们引入了一个框架的基础上的Fisher形式主义重叠源(混合)的宇宙剪切估计的影响进行分析。对于鲁宾天文台遗产调查的空间和时间(LSST),我们提出了预期的损失,统计灵敏度为十年的调查,由于混合。我们发现,对于大约62%的星系,有可能在全深度LSST图像中被检测到,至少有1%的通量在其像素是来自重叠的来源。我们还发现,重叠星系的措施之间的统计相关性,并在较小程度上较高的散粒噪声水平,由于它们的存在,减少星系的有效数密度,$N_{eff}$,由$\sim$ 18%。我们计算了$N_{eff}$的上限为39.4个星系/弧分$^2$在$r$带。我们研究了不同的恒星密度对$N_{eff}$的影响,并说明了将调查扩展到银河系低纬度地区的收益递减。我们扩展了Fisher形式主义来预测由于混合最大似然(ML)形状估计的像素噪声偏差的增加。我们发现,噪声偏置是敏感的特定形状估计和测量的整体平均形状,使用,和属性的星系,包括红移依赖的数量,如大小和光度。

英文摘要:

In Stage-IV imaging surveys, a significant amount of the cosmologically useful information is due to sources whose images overlap with those of other sources on the sky. The cosmic shear signal is primarily encoded in the estimated shapes of observed galaxies and thus directly impacted by overlaps. We introduce a framework based on the Fisher formalism to analyze effects of overlapping sources (blending) on the estimation of cosmic shear. For the Rubin Observatory Legacy Survey of Space and Time (LSST), we present the expected loss in statistical sensitivity for the ten-year survey due to blending. We find that for approximately 62% of galaxies that are likely to be detected in full-depth LSST images, at least 1% of the flux in their pixels is from overlapping sources. We also find that the statistical correlations between measures of overlapping galaxies and, to a much lesser extent the higher shot noise level due to their presence, decrease the effective number density of galaxies, $N_{eff}$, by $\sim$18%. We calculate an upper limit on $N_{eff}$ of 39.4 galaxies per arcmin$^2$ in $r$ band. We study the impact of varying stellar density on $N_{eff}$ and illustrate the diminishing returns of extending the survey into lower Galactic latitudes. We extend the Fisher formalism to predict the increase in pixel-noise bias due to blending for maximum-likelihood (ML) shape estimators. We find that noise bias is sensitive to the particular shape estimator and measure of ensemble-average shape that is used, and properties of the galaxy that include redshift-dependent quantities such as size and luminosity.

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