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INTERPRETING THE UNRESOLVED INTENSITY OF COSMOLOGICALLY REDSHIFTED LINE RADIATION

机译:解释宇宙变红线辐射的不可分辨的强度

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Intensity mapping experiments survey the spectrum of diffuse line radiation rather than detect individual objects at high signal-to-noise ratio. Spectral maps of unresolved atomic and molecular line radiation contain three-dimensional information about the density and environments of emitting gas?and efficiently probe cosmological volumes out to high redshift. Intensity mapping survey volumes also contain all other sources of radiation at the frequencies of interest. Continuum foregrounds are typically ~102–103 times brighter than the cosmological signal. The instrumental response to bright foregrounds will produce new spectral degrees of freedom that are not known in advance, nor necessarily spectrally smooth. The intrinsic spectra of foregrounds may also not be well?known in advance. We describe a general class of quadratic estimators to analyze data from single-dish intensity mapping experiments?and determine contaminated spectral modes from the data themselves. The key attribute of foregrounds is not that they are spectrally smooth, but instead that they have fewer bright spectral degrees of freedom than the cosmological signal. Spurious correlations between the signal and foregrounds produce additional bias. Compensation for signal attenuation must estimate and correct this bias. A successful intensity mapping experiment will control instrumental systematics that spread variance into new modes, and it must observe a large enough volume that contaminant modes can be determined independently from the signal on scales of interest.
机译:强度映射实验将调查漫射线辐射的频谱,而不是在高信噪比下检测单个对象。未解析的原子和分子线辐射的光谱图包含有关排放气体的密度和环境的三维信息,并有效地探测宇宙体积到高红移。强度测绘调查量还包含感兴趣频率下的所有其他辐射源。连续谱前景通常比宇宙学信号亮102-103倍。仪器对明亮前景的响应将产生事先未知的新光谱自由度,也不一定在光谱上平滑。前景的固有光谱也可能事先不为人所知。我们描述了一个一般的二次估计器类,以分析来自单皿强度映射实验的数据,并从数据本身确定受污染的光谱模式。前景的关键属性不是它们在光谱上是光滑的,而是它们比宇宙学信号具有更少的明亮光谱自由度。信号和前景之间的虚假相关会产生额外的偏置。信号衰减补偿必须估算并纠正此偏差。一个成功的强度映射实验将控制将方差扩展到新模式的仪器系统,并且必须观察到足够大的体积,以便可以独立于目标信号上的信号确定污染物模式。

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