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arXiv 2103.14563astro-ph.CO
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利用机器学习技术从21 cm数据中提取电离层光学厚度$τ$

Extracting the Optical Depth to Reionization $τ$ from 21 cm Data Using Machine Learning Techniques

Tashalee S. Billings, Paul La Plante, James E. Aguirre

摘要:

即将到来的测量高红移21厘米信号的时代的再电离(EoR)是一个有前途的探测天体物理学的第一个星系和宇宙学参数。特别是,光学深度$τ$的最后散射表面的宇宙微波背景(CMB)应严格约束的直接测量的中性氢状态在高红移。一个强大的测量$τ$从21厘米的数据将有助于消除它作为一个讨厌的参数从宇宙学参数的CMB估计。以前的建议提取$τ$从未来的21厘米数据集通常使用的21厘米功率谱产生的半数值模型,以重建再电离的历史。我们在这里提出了一种不同的方法,该方法使用卷积神经网络(CNN)在21 cm EoR信号的模拟图像上训练,以提取$τ$。我们构建了一个改进了之前提出的架构的CNN,并执行自动超参数优化。我们表明,经过良好训练的CNN能够准确地预测$τ$,即使在去除傅立叶模式时,这些模式预计会被21 cm信号的明亮前景污染所破坏。优化网络的典型随机误差小于$3.06\%$,偏差因子更小。虽然是初步的,但这种方法可以产生对$τ$的约束,从而改善CMB的低$\ell$ EE观测的样本方差有限测量,使这种方法成为对推断$τ$的更传统方法的有价值的补充。

英文摘要:

Upcoming measurements of the high-redshift 21 cm signal from the Epoch of Reionization (EoR) are a promising probe of the astrophysics of the first galaxies and of cosmological parameters. In particular, the optical depth $τ$ to the last scattering surface of the cosmic microwave background (CMB) should be tightly constrained by direct measurements of the neutral hydrogen state at high redshift. A robust measurement of $τ$ from 21 cm data would help eliminate it as a nuisance parameter from CMB estimates of cosmological parameters. Previous proposals for extracting $τ$ from future 21 cm datasets have typically used the 21 cm power spectra generated by semi-numerical models to reconstruct the reionization history. We present here a different approach which uses convolution neural networks (CNNs) trained on mock images of the 21 cm EoR signal to extract $τ$. We construct a CNN that improves upon on previously proposed architectures, and perform an automated hyperparameter optimization. We show that well-trained CNNs are able to accurately predict $τ$, even when removing Fourier modes that are expected to be corrupted by bright foreground contamination of the 21 cm signal. Typical random errors for an optimized network are less than $3.06\%$, with biases factors of several smaller. While preliminary, this approach could yield constraints on $τ$ that improve upon sample-variance limited measurements of the low-$\ell$ EE observations of the CMB, making this approach a valuable complement to more traditional methods of inferring $τ$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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