用卷积神经网络约束星系群内介质密度的新方法
A new way to constrain the densities of intra-group medium in groups of galaxies with convolutional neural networks
摘要：
冲压压力（RP）可以影响冷气体含量的演化和星系的恒星形成率。RP强度的关键参数之一是组内介质（$ρ_{\rm igm}$）的密度，如果来自它的X射线发射太弱而无法观察到，则很难估计。我们提出了一种新的方式来约束$ρ_{\rm igm}$通过应用卷积神经网络（CNN）模拟气体密度和运动学地图下的强RP星系。我们在各种RP条件下使用星系的$9\times{}10^4$ 2D图像训练CNN，然后使用$10^4$新的测试图像验证性能。这种新的方法可以应用于正在进行的WALLABY和SKA调查的实际观测数据，以快速获得$ρ_{\rm igm}$的估计。模拟的星系图像具有$1.0$ kpc分辨率，这与未来WALLABY调查的预期一致。经训练的CNN模型预测归一化IGM密度$\hatρ_{\rm igm}$，其中$0.0 \le \hatρ_{\rm igm, n} < 10.0$分别准确地具有用于密度、运动学和联合2D图的$0.72$、$0.83$和$0.74$的均方根误差值（$\rm RMSE$）。经过训练的模型无法精确地预测星系相对于IGM（$v_{\rm rel}$）的相对速度，并且难以概括不同的RP条件。我们将我们的CNN应用于所观察到的NGC 1566在Dorado群中的HI柱密度图，以估计其IGM密度。
英文摘要：
Ram pressure (RP) can influence the evolution of cold gas content and star formation rates of galaxies. One of the key parameters for the strength of RP is the density of intra-group medium ($ρ_{\rm igm}$), which is difficult to estimate if the X-ray emission from it is too weak to be observed. We propose a new way to constrain $ρ_{\rm igm}$ through an application of convolutional neural networks (CNNs) to simulated gas density and kinematic maps galaxies under strong RP. We train CNNs using $9\times{}10^4$ 2D images of galaxies under various RP conditions, then validate performance with $10^4$ new test images. This new method can be applied to real observational data from ongoing WALLABY and SKA surveys to quickly obtain estimates of $ρ_{\rm igm}$. Simulated galaxy images have $1.0$ kpc resolution, which is consistent with that expected from the future WALLABY survey. The trained CNN models predict the normalised IGM density, $\hatρ_{\rm igm}$ where $0.0 \le \hatρ_{\rm igm, n} < 10.0$, accurately with root mean squared error values ($\rm RMSE$) of $0.72$, $0.83$ and $0.74$ for the density, kinematic and joined 2D maps, respectively. Trained models are unable to predict the relative velocity of galaxies with respect to the IGM ($v_{\rm rel}$) precisely, and struggle to generalise for different RP conditions. We apply our CNNs to the observed HI column density map of NGC 1566 in the Dorado group to estimate its IGM density.
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