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Peculiar velocity estimation from kinetic SZ effect using deep neural networks

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Dolag,  Klaus
Computational Structure Formation, MPI for Astrophysics, Max Planck Society;

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Citation

Wang, Y., Ramachandra, N., Salazar-Canizales, E. M., Feldman, H. A., Watkins, R., & Dolag, K. (2021). Peculiar velocity estimation from kinetic SZ effect using deep neural networks. Monthly Notices of the Royal Astronomical Society, 506(1), 1427-1437. doi:10.1093/mnras/stab1715.


Cite as: https://hdl.handle.net/21.11116/0000-0009-8E08-F
Abstract
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 independent estimation of the optical depth. Images 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 determine its ability of estimating 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 per cent compared to the analytical approach.