论文标题
21厘米功率谱时,无星系现象学模型
A galaxy-free phenomenological model for the 21-cm power spectrum during reionization
论文作者
论文摘要
来自当前一代的干涉仪的上限靶向21 cm信号的高红移信号最近开始排除物理现实,尽管仍然是极端的,但仍是电离时代(EOR)的模型。虽然推断第一个星系的详细特性是测量高$ z $ 21厘米信号的最重要动机之一,但它们还可以对播层间介质(IGM)的性质提供有用的约束。在此激励的基础上,我们为21 cm功率谱构建了一个简单的现象学模型,该模型直接以IGM性能为角度,该属性绕过了通常在推理管道中采用的计算价格昂贵的3-D半数字建模,并避免了有关星系特性的明确假设。关键的简化假设是(i)电离场是二进制的,由球形气泡组成,并通过参数气泡尺寸分布很好地描述了丰度,并且(ii)(ii)``buld'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''尽管模型的简单性,但从\ textSc {21cmfast}生成的模拟21-CM功率谱中恢复的IgM的平均离子化部分和自旋温度通常与真实输入值吻合。这表明可以使用具有非常不同的假设,参数和先验的模型在IGM上获得可比的约束。因此,随着未来几年的上限不断改善,我们的方法将与半数模型互补。
Upper limits from the current generation of interferometers targeting the 21-cm signal from high redshifts have recently begun to rule out physically realistic, though still extreme, models of the Epoch of Reionization (EoR). While inferring the detailed properties of the first galaxies is one of the most important motivations for measuring the high-$z$ 21-cm signal, they can also provide useful constraints on the properties of the intergalactic medium (IGM). Motivated by this, we build a simple, phenomenological model for the 21-cm power spectrum that works directly in terms of IGM properties, which bypasses the computationally expensive 3-D semi-numerical modeling generally employed in inference pipelines and avoids explicit assumptions about galaxy properties. The key simplifying assumptions are that (i) the ionization field is binary, and composed of spherical bubbles with an abundance described well by a parametric bubble size distribution, and (ii) that the spin temperature of the ``bulk'' IGM outside bubbles is uniform. Despite the simplicity of the model, the mean ionized fraction and spin temperature of the IGM recovered from mock 21-cm power spectra generated with \textsc{21cmfast} are generally in good agreement with the true input values. This suggests that it is possible to obtain comparable constraints on the IGM using models with very different assumptions, parameters, and priors. Our approach will thus be complementary to semi-numerical models as upper limits continue to improve in the coming years.