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

• URL: http://arxiv.org/abs/2003.06135v2
• Date: Wed, 15 Jul 2020 14:32:54 GMT
• Title: Mass Estimation of Galaxy Clusters with Deep Learning I: Sunyaev-Zel'dovich Effect
• Authors: Nikhel Gupta and Christian L. Reichardt
• Abstract summary: We present a new application of deep learning to infer the masses of galaxy clusters directly from images of the microwave sky. 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. We verify that the model works for realistic SZ profiles even when trained on azimuthally symmetric SZ profiles by using the Magneticum hydrodynamical simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\sigma$ uncertainty $\Delta M/M \leq 0.2$, 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.

Related papers

• Simulation-free Schr\"odinger bridges via score and flow matching [89.4231207928885]
  We present simulation-free score and flow matching ([SF]$2$M) Our method generalizes both the score-matching loss used in the training of diffusion models and the recently proposed flow matching loss used in the training of continuous flows. Notably, [SF]$2$M is the first method to accurately model cell dynamics in high dimensions and can recover known gene regulatory networks simulated data.
  arXiv  Detail & Related papers  (2023-07-07T15:42:35Z)
• Neural Inference of Gaussian Processes for Time Series Data of Quasars [72.79083473275742]
  We introduce a new model that enables it to describe quasar spectra completely. We also introduce a new method of inference of Gaussian process parameters, which we call $textitNeural Inference$. The combination of both the CDRW model and Neural Inference significantly outperforms the baseline DRW and MLE.
  arXiv  Detail & Related papers  (2022-11-17T13:01:26Z)
• The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback [2.436653298863297]
  AGN and supernovae feedback can affect measurements of integrated SZ flux of halos from CMB surveys. We search for analogues of the $Y-M$ relation which are more robust to feedback processes for low masses. Our results can be useful for using upcoming SZ surveys to constrain the nature of baryonic feedback.
  arXiv  Detail & Related papers  (2022-09-05T18:00:00Z)
• Offline Reinforcement Learning at Multiple Frequencies [62.08749079914275]
  We study how well offline reinforcement learning algorithms can accommodate data with a mixture of frequencies during training. We present a simple yet effective solution that enforces consistency in the rate of $Q$-value updates to stabilize learning.
  arXiv  Detail & Related papers  (2022-07-26T17:54:49Z)
• Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem [78.20667552233989]
  We formulate a physics-informed compressed sensing (PICS) method for the reconstruction of velocity fields from noisy and sparse magnetic resonance signals. We find that the method is capable of reconstructing and segmenting the velocity fields from sparsely-sampled signals.
  arXiv  Detail & Related papers  (2022-07-04T14:51:59Z)
• Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter [2.0223261087090303]
  We study the Sunyaev-Zeldovich flux$-$cluster mass relation ($Y_mathrmSZ-M$) We find a new proxy for cluster mass which combines $Y_mathrmSZ$ and concentration of ionized gas. We show that the dependence on $c_mathrmgas$ is linked to cores of clusters exhibiting larger scatter than their outskirts.
  arXiv  Detail & Related papers  (2022-01-04T19:00:01Z)
• Satellite galaxy abundance dependency on cosmology in Magneticum simulations [101.18253437732933]
  We build an emulator of satellite abundance based on cosmological parameters. We find that $A$ and $beta$ depend on cosmological parameters, even if weakly. We also show that satellite abundance cosmology dependency differs between full-physics (FP) simulations, dark-matter only (DMO) and non-radiative simulations.
  arXiv  Detail & Related papers  (2021-10-11T18:00:02Z)
• STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning [58.6792963686231]
  Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. It is not clear how to choose the WNs' minimum update directions, the first minibatch sizes, and the local update frequency. We show that there is a trade-off curve between local update frequencies and local mini sizes, on which the above complexities can be maintained.
  arXiv  Detail & Related papers  (2021-06-19T06:13:45Z)
• deep21: a Deep Learning Method for 21cm Foreground Removal [1.5914835340090137]
  We train a deep convolutional neural network (CNN) with a UNet architecture and three-dimensional convolutions on simulated observations. Cleaned maps recover cosmological clustering statistics within 10% at all relevant angular scales and frequencies. Our approach demonstrates the feasibility of analyzing 21cm intensity maps, as opposed to derived summary statistics, for upcoming radio experiments.
  arXiv  Detail & Related papers  (2020-10-29T18:00:02Z)
• Mass Estimation of Galaxy Clusters with Deep Learning II: CMB Cluster Lensing [0.0]
  We present a new application of deep learning to reconstruct the cosmic microwave background (CMB) temperature maps from the images of microwave sky. We use a feed-forward deep learning network, mResUNet, for both steps of the analysis.
  arXiv  Detail & Related papers  (2020-05-28T13:39:49Z)
• Constraining the recent star formation history of galaxies : an Approximate Bayesian Computation approach [0.0]
  We present a method to identify galaxies undergoing a strong variation of star formation activity in the last tens to hundreds Myr. We analyze a sample of COSMOS galaxies using high signal-to-noise ratio broad band photometry.
  arXiv  Detail & Related papers  (2020-02-18T19:00:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.