区分矮星系尖点和核心的概率深度学习模型
A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies
摘要：
在冷暗物质（DM）宇宙学中的数值模拟形成了密度分布具有陡峭的内部斜率（“尖点”）的晕，然而对星系的观测通常指向平坦的中心“核心”。我们开发了一个卷积混合密度神经网络模型，以获得DM晕的内部密度斜率的概率密度函数（PDF）。我们训练网络模拟矮星系从NIHAO和AURIGA项目，其中包括DM尖点和核心：视线速度和二维空间分布的恒星被用作输入，以获得一个PDF表示的概率预测一个特定的内斜率。该模型准确地恢复了预期的DM配置文件：$\sim$ 82 $\%$的星系有一个派生的内部斜率$\pm$ 0.1内的真实值，而$\sim$ 98 $\%$内$\pm$ 0.3。我们将我们的模型应用于四个本星系群矮球状星系，并发现与基于Jeans模型的代码GravSphere获得的结果一致：天炉座dSph具有强烈的迹象，拥有一个中央DM核心，船底座和六分仪有尖点（尽管后者具有很大的不确定性），而雕塑家显示双峰PDF表示尖点是首选，但不能排除核心。我们的研究结果表明，基于模拟的推理与神经网络提供了一个创新的和互补的方法来确定内部的物质密度分布在星系中，这反过来又可以帮助限制难以捉摸的DM的属性。
英文摘要：
Numerical simulations within a cold dark matter (DM) cosmology form halos whose density profiles have a steep inner slope (`cusp'), yet observations of galaxies often point towards a flat central `core'. We develop a convolutional mixture density neural network model to derive a probability density function (PDF) of the inner density slopes of DM halos. We train the network on simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both DM cusps and cores: line-of-sight velocities and 2D spatial distributions of their stars are used as inputs to obtain a PDF representing the probability of predicting a specific inner slope. The model recovers accurately the expected DM profiles: $\sim$82$\%$ of the galaxies have a derived inner slope within $\pm$0.1 of their true value, while $\sim$98$\%$ within $\pm$0.3. We apply our model to four Local Group dwarf spheroidal galaxies and find results consistent with those obtained with the Jeans modelling based code GravSphere: the Fornax dSph has a strong indication of possessing a central DM core, Carina and Sextans have cusps (although the latter with large uncertainties), while Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a core can not be ruled out. Our results show that simulation-based inference with neural networks provide a innovative and complementary method for the determination of the inner matter density profiles in galaxies, which in turn can help constrain the properties of the elusive DM.
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