# Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effect

arXiv: https://arxiv.org/abs/2003.06135 · HTML: https://mlsanity.com/paper/2003.06135/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Inferring halo masses with Graph Neural Networks](https://mlsanity.com/paper/2111.08683/) (2021)
- [The CAMELS project: public data release](https://mlsanity.com/paper/2201.01300/) (2022)
- [Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter](https://mlsanity.com/paper/2201.01305/) (2022)
- [Machine Learning and Cosmology](https://mlsanity.com/paper/2203.08056/) (2022)
- [Reconstructing Patchy Reionization with Deep Learning](https://mlsanity.com/paper/2101.01214/) (2021)
- [Set-based Implicit Likelihood Inference of Galaxy Cluster Mass](https://mlsanity.com/paper/2507.20378/) (2025)

## Abstract (arXiv)

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× 1014~rm Modot <M200rm c< 8× 1014~rm Modot at z=0.7. The trained model estimates the cluster masses with a 1 σ uncertainty Delta M/M ≤ 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.