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arXiv 1402.2290astro-ph.CO
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射电星系群和多示踪剂技术:推动原始非高斯性的极限

Radio Galaxy populations and the multi-tracer technique: pushing the limits on primordial non-Gaussianity

Luis D. Ferramacho, Mario G. Santos, Matt J. Jarvis, Stefano Camera

摘要:

我们探索使用不同的射电星系人口示踪剂的不同质量的晕,因此,不同的偏置属性,约束原始的非高斯的本地类型。我们进行了Fisher矩阵分析的基础上预测的汽车和交叉角功率谱的这些人口,使用模拟的红移分布作为一个功能的检测通量和演变的偏差为不同的星系类型(恒星形成星系,星暴星系,射电宁静类星体,FRI和FRII AGN星系)。我们表明,这样的多示踪剂分析大大提高了非高斯性的信息,大大减少了宇宙方差贡献的整体误差预算。通过使用这种方法应用到未来的调查,我们预测的约束sigma_fnl=3.6的本地非高斯参数的星系检测通量限制为10 \muJy和sigma_fnl=2.2的1 \muJy。我们表明,这显着改善了使用整个未分化的人口(sigma_fnl=48为10 \muJy和sigma_fnl=12为1 \muJy)时获得的限制。我们的结论是,连续无线电调查本身有可能约束原始非高斯的精度至少比目前的约束与普朗克数据的CMB双谱的两倍,打开一个窗口,以获得西格玛fnl~1平方公里阵列。

英文摘要:

We explore the use of different radio galaxy populations as tracers of different mass halos and therefore, with different bias properties, to constrain primordial non-Gaussianity of the local type. We perform a Fisher matrix analysis based on the predicted auto and cross angular power spectra of these populations, using simulated redshift distributions as a function of detection flux and the evolution of the bias for the different galaxy types (Star forming galaxies, Starburst galaxies, Radio-Quiet Quasars, FRI and FRII AGN galaxies). We show that such a multi-tracer analysis greatly improves the information on non-Gaussianity by drastically reducing the cosmic variance contribution to the overall error budget. By using this method applied to future surveys, we predict a constraint of sigma_fnl=3.6 on the local non-Gaussian parameter for a galaxy detection flux limit of 10 \muJy and sigma_fnl=2.2 for 1 \muJy. We show that this significantly improves on the constraints obtained when using the whole undifferentiated populations (sigma_fnl=48 for 10 \muJy and sigma_fnl=12 for 1 \muJy). We conclude that continuum radio surveys alone have the potential to constrain primordial non-Gaussianity to an accuracy at least a factor of two better than the present constraints obtained with Planck data on the CMB bispectrum, opening a window to obtain sigma_fnl~1 with the Square Kilometer Array.

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