论文标题

一种新方法来限制具有卷积神经网络的星系组中组内培养基的密度

A new way to constrain the densities of intra-group medium in groups of galaxies with convolutional neural networks

论文作者

Shen, Austin X., Bekki, Kenji

论文摘要

RAM压力(RP)可以影响星系冷气含量和星形形成速率的演变。 RP强度的关键参数之一是组内培养基的密度($ρ_ {\ rm Igm} $),如果从中的X射线发射太弱而无法观察到,这很难估算。我们提出了一种新的方法,通过将卷积神经网络(CNN)应用于模拟的气体密度和运动型地图星系,来限制$ρ_ {\ rm Igm} $。我们使用$ 9 \ times {} 10^4 $ 2D图像在各种RP条件下训练CNN,然后用$ 10^4 $新的测试图像验证性能。这种新方法可以应用于正在进行的袋鼠和SKA调查中的真实观察数据中,以快速获得$ρ_{\ rm Igm} $的估计。模拟的Galaxy图像具有$ 1.0 $ KPC的分辨率,这与未来Wallaby调查的预期相一致。受过训练的CNN模型预测了归一化的IgM密度,$ \hatρ_{\ rm Igm} $其中$ 0.0 \ le \hatρ_{\ rm igm,n} <10.0 $,准确地具有均方根误差值($ \ \ \ rmse $ 0.72 $,$ 0.72 $,$ 0.72 $和$ 0.83 $,and $ an分别为2D地图。受过训练的模型无法准确预测星系的相对速度($ v _ {\ rm rel} $),并难以推广到不同的RP条件。我们将CNN应用于Dorado组NGC 1566的观察到的HI柱密度图,以估计其IgM密度。

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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