C. L. Reichardt

N. Gupta, C. L. Reichardt

We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from the images of microwave sky, and to use these reconstructed maps to estimate the masses of galaxy clusters. We use a feed-forward deep learning network, mResUNet, for both steps of the analysis. The first deep learning model, mResUNet-I, is trained to reconstruct foreground and noise suppressed CMB maps from a set of simulated images of the microwave sky that include signals from the cosmic microwave background, astrophysical foregrounds like dusty and radio galaxies, instrumental noise as well as the cluster's own thermal Sunyaev Zel'dovich signal. The second deep learning model, mResUNet-II, is trained to estimate cluster masses from the gravitational lensing signature in the reconstructed foreground and noise suppressed CMB maps. For SPTpol-like noise levels, the trained mResUNet-II model recovers the mass for $10^4$ galaxy cluster samples with a 1-$σ$ uncertainty $ΔM_{\rm 200c}^{\rm est}/M_{\rm 200c}^{\rm est} =$ 0.108 and 0.016 for input cluster mass $M_{\rm 200c}^{\rm true}=10^{14}~\rm M_{\odot}$ and $8\times 10^{14}~\rm M_{\odot}$, respectively. We also test for potential bias on recovered masses, finding that for a set of $10^5$ clusters the estimator recovers $M_{\rm 200c}^{\rm est} = 2.02 \times 10^{14}~\rm M_{\odot}$, consistent with the input at 1% level. The 2 $σ$ upper limit on potential bias is at 3.5% level.

Nikhel Gupta, Christian L. Reichardt

We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. Effectively, this is a novel approach to determining the scaling relation between a cluster's Sunyaev-Zel'dovich (SZ) effect signal and mass. The deep learning algorithm used is mResUNet, which is a modified feed-forward deep learning algorithm that broadly combines residual learning, convolution layers with different dilation rates, image regression activation and a U-Net framework. We train and test the deep learning model using simulated images of the microwave sky that include signals from the cosmic microwave background (CMB), dusty and radio galaxies, instrumental noise as well as the cluster's own SZ signal. The simulated cluster sample covers the mass range 1$\times 10^{14}~\rm M_{\odot}$ $<M_{200\rm c}<$ 8$\times 10^{14}~\rm M_{\odot}$ at $z=0.7$. The trained model estimates the cluster masses with a 1 $σ$ uncertainty $ΔM/M \leq 0.2$, consistent with the input scatter on the SZ signal of 20%. We verify that the model works for realistic SZ profiles even when trained on azimuthally symmetric SZ profiles by using the Magneticum hydrodynamical simulations.