Peculiar velocity estimation from kinetic SZ effect using deep neural networks
Peculiar velocity estimation from kinetic SZ effect using deep neural networks
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DOI：
10.1093/mnras/stab1715
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发表时间：
2020-10
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通讯作者：
Yuyu Wang;Nesar Ramachandra;Edgar M. Salazar-Canizales;H. Feldman;R. Watkins;K. Dolag
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文献类型：
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作者：
Yuyu Wang;Nesar Ramachandra;Edgar M. Salazar-Canizales;H. Feldman;R. Watkins;K. Dolag
Sunyaev-Zel'dolvich (SZ) 效应预计将有助于在不久的将来的望远镜巡天中测量遥远星团的速度。我们使用经过数值模拟训练的深度学习框架简化了星系团奇特速度的计算，以避免光学深度的估计。使用最大的宇宙流体动力学模拟之一（Magneticum 模拟）生成扭曲光子背景图像，以进行理想化观测。该模型经过测试，能够在不同噪声条件下从未来的动力学 SZ 观测中获得特定的速度。与分析方法相比，深度学习算法在估计动态 SZ 效应的特殊速度方面表现出鲁棒性，准确度提高了约 17%。
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.