论文标题
使用深神经网络从动力学SZ效应中奇特的速度估计
Peculiar Velocity Estimation from Kinetic SZ Effect using Deep Neural Networks
论文作者
论文摘要
预计Sunyaev-Zel'Dolvich（SZ）效应将有助于测量不久的将来的望远镜调查中远处簇的速度。我们使用经过数值模拟训练的深度学习框架来简化星系簇的特殊速度，以避免对光学深度的估计。使用最大的宇宙流体动力学模拟之一，磁模拟生成了扭曲的光子背景图像，用于理想化的观测值。该模型被测试为在不同噪声条件下的未来动力学SZ观测值中具有特殊的速度。与分析方法相比，深度学习算法通过动力学SZ效应的特殊速度估算特殊速度时表现出鲁棒性。
The Sunyaev-Zel'dolvich (SZ) effect is expected to be instrumental in measuring velocities of distant clusters in near future telescope surveys. We simplify the calculation of peculiar velocities of galaxy clusters using deep learning frameworks trained on numerical simulations to avoid the estimation of the optical depth. The image of distorted photon backgrounds are generated for idealized observations using one of the largest cosmological hydrodynamical simulations, the Magneticum simulations. The model is tested to be capable peculiar velocities from future kinetic SZ observations under different noise conditions. The deep learning algorithm displays robustness in estimating peculiar velocities from kinetic SZ effect by an improvement in accuracy of about 17% compared to the analytical approach.
