基于混合密度网络的星系三维形状恢复
Galaxy 3D Shape Recovery using Mixture Density Network
AI总结：
本研究针对现有星系三维形状恢复方法的假设局限，提出基于混合密度网络的监督学习方法，利用EAGLE模拟数据训练，在长椭球、三轴系统的形状及不确定性恢复上更具优势，为相关巡天提供参考。
摘要：
自本世纪初以来，天文学家一直在利用恒星运动学地图与成像结合所提供的丰富信息，试图恢复星系的固有三维（3D）形状。常见的固有形状恢复方法依赖于运动学轴与形态轴的固有不对准度和三轴性参数之间的预期单调关系。然而，近期研究对形状与固有运动学不对准度相关的基础假设提出了质疑。在本研究中，我们旨在利用星系的投影恒星运动学和流量分布，通过混合密度网络（MDN）的监督机器学习方法恢复单个星系的三维形状。我们利用EAGLE流体动力学宇宙学模拟的模拟数据集，针对一组精心挑选的常见运动学和光度学参数训练MDN模型。与现有方法相比，我们证明了MDN模型在恢复星系三维形状及不确定性方面具有潜在提升，尤其是对于长椭球和三轴系统。我们针对与当前及未来积分场光谱星系巡天相关的星系固有形状恢复提出了具体建议。
英文摘要：
Since the turn of the century, astronomers have been exploiting the rich information afforded by combining stellar kinematic maps and imaging in an attempt to recover the intrinsic, three-dimensional (3D) shape of a galaxy. A common intrinsic shape recovery method relies on an expected monotonic relationship between the intrinsic misalignment of the kinematic and morphological axes and the triaxiality parameter. Recent studies have, however, cast doubt about underlying assumptions relating shape and intrinsic kinematic misalignment. In this work, we aim to recover the 3D shape of individual galaxies using their projected stellar kinematic and flux distributions using a supervised machine learning approach with mixture density network (MDN). Using a mock dataset of the EAGLE hydrodynamical cosmological simulation, we train the MDN model for a carefully selected set of common kinematic and photometric parameters. Compared to previous methods, we demonstrate potential improvements achieved with the MDN model to retrieve the 3D galaxy shape along with the uncertainties, especially for prolate and triaxial systems. We make specific recommendations for recovering galaxy intrinsic shapes relevant for current and future integral field spectroscopic galaxy surveys.
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