论文标题
使用深度学习从21cm断层扫描中推断天体物理学和暗物质特性
Inferring Astrophysics and Dark Matter Properties from 21cm Tomography using Deep Learning
论文作者
论文摘要
21cm断层扫描为我们宇宙历史上早期时代的天体物理学和基本物理学打开了一个窗口，在我们宇宙的历史上，回报时期（EOR）和宇宙黎明（CD）。诸如功率光谱之类的摘要统计数据省略了由于其高度非高斯性质而在此信号中编码的信息。在这里，我们采用基于网络的方法直接推断CD和EOR天体物理学与21cm层析成像的基本物理学共同推断。我们展示了一个温暖的暗物质（WDM）宇宙，其中暗物质密度参数$ω__\ MATHRM {M} $和WDM MASS $ M_ \ MATHRM {WDM} $强烈影响CD和EOR。反映了21厘米灯孔的三维性质，我们提出了一个新的，尽管是简单的3D卷积神经网络，可在中等训练成本下进行有效的参数恢复。在模拟上，我们观察到CD和EOR天体物理学的高保真参数恢复（$ r^2> 0.78-0.99 $），以及DM密度$ω__\ MATHRM {M} $（$ r^2> 0.97 $）和WDM Mass（$ r^2> 0.61 $，都更好$ m_ \ mathrm {wdm} <3-4 \，$ kev）。对于现实的模拟观察到的轻孔，包括平方公里阵列的噪音和前景水平，我们注意到，在乐观的前景场景参数恢复中不受影响，而对于中度，较不乐观的前景水平（占用所谓的楔形物）（占用所谓的楔形），WDM质量的恢复恢复，而在$ r^2> 0.9 $ r^2> 0.9 $ r^2> 0. 9 $ r^2> 0.9。我们通过在裸露的模拟和模拟观察之间转移学习来进一步测试基于网络的推断，以防止建模不确定性和系统学的鲁棒性；我们发现特定的X射线光度和电离效率的稳健恢复，而DM密度和WDM质量则具有增加的偏置和散射。
21cm tomography opens a window to directly study astrophysics and fundamental physics of early epochs in our Universe's history, the Epoch of Reionisation (EoR) and Cosmic Dawn (CD). Summary statistics such as the power spectrum omit information encoded in this signal due to its highly non-Gaussian nature. Here we adopt a network-based approach for direct inference of CD and EoR astrophysics jointly with fundamental physics from 21cm tomography. We showcase a warm dark matter (WDM) universe, where dark matter density parameter $Ω_\mathrm{m}$ and WDM mass $m_\mathrm{WDM}$ strongly influence both CD and EoR. Reflecting the three-dimensional nature of 21cm light-cones, we present a new, albeit simple, 3D convolutional neural network for efficient parameter recovery at moderate training cost. On simulations we observe high-fidelity parameter recovery for CD and EoR astrophysics ($R^2>0.78-0.99$), together with DM density $Ω_\mathrm{m}$ ($R^2>0.97$) and WDM mass ($R^2>0.61$, significantly better for $m_\mathrm{WDM}<3-4\,$keV). For realistic mock observed light-cones that include noise and foreground levels expected for the Square Kilometre Array, we note that in an optimistic foreground scenario parameter recovery is unaffected, while for moderate, less optimistic foreground levels (occupying the so-called wedge) the recovery of the WDM mass deteriorates, while other parameters remain robust against increased foreground levels at $R^2>0.9$. We further test the robustness of our network-based inference against modelling uncertainties and systematics by transfer learning between bare simulations and mock observations; we find robust recovery of specific X-ray luminosity and ionising efficiency, while DM density and WDM mass come with increased bias and scatter.
