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A deep learning approach to infer galaxy cluster masses from Planck Compton-y parameter maps

Nature Astronomy volume 6, pages 1325–1331 (2022)Cite this article

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Abstract

Galaxy clusters are useful laboratories to investigate the evolution of the Universe, and accurate measurement of their total masses allows us to constrain important cosmological parameters. However, estimating mass from observations that use different methods and spectral bands introduces various systematic errors. Here we evaluate the use of a convolutional neural network (CNN) to reliably and accurately infer the masses of galaxy clusters from the Compton-y parameter maps provided by the Planck satellite. The CNN is trained with mock images generated from hydrodynamic simulations of galaxy clusters, with Planck’s observational limitations taken into account. We observe that the CNN approach is not subject to the usual observational assumptions, and therefore is not affected by the same biases. By applying the trained CNNs to the real Planck maps, we find cluster masses compatible with Planck measurements within a 15% bias. Finally, we show that this mass bias can be explained by the well-known hydrostatic equilibrium assumption in Planck masses, and the different parameters in the integrated Compton-y signal and the mass scaling laws. This work highlights that CNNs, supported by hydrodynamic simulations, are a promising and independent tool for estimating cluster masses with high accuracy, which can be extended to other surveys as well as to observations in other bands.

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Fig. 1: Verifying CNN with mock maps.
Fig. 2: Comparing CNN predicted cluster mass with the mass estimated by Planck.
Fig. 3: Verifying the bias causes with Y–M relation.

Data availability

The catalogue of CNN-estimated masses for Planck clusters can be downloaded from https://github.com/The300th/DeepPlanck. The mock y maps used to train the different CNN models can be accessed upon request to the corresponding authors.

Code availability

CNN trained weights and data products are available at https://github.com/The300th/DeepPlanck.

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Acknowledgements

We acknowledge helpful discussions with A. Ferragamo, F. De Luca and F. Sembolini. D.d.A., W.C. and G.Y. thank financial support from Ministerio de Ciencia e Innovación (Spain) under project grant PID2021-122603NB-C21. W.C. is supported by the STFC AGP Grant ST/V000594/1 and by the Atracción de Talento Contract no. 2020-T1/TIC-19882 granted by the Comunidad de Madrid in Spain. W.C. further acknowledges the science research grants from the China Manned Space Project with no. CMS-CSST-2021-A01 and no. CMS-CSST-2021-B01. M.D.P. acknowledges support from Sapienza Università di Roma thanks to Progetti di Ricerca Medi 2019, RM11916B7540DD8D and Progetti di Ricerca Medi 2020, RM120172B32D5BE2.

Author information

Authors and Affiliations

  1. Departamento de Física Teórica and CIAFF, Modulo 8 Universidad Autónoma de Madrid, Madrid, Spain

    Daniel de Andres, Weiguang Cui & Gustavo Yepes

  2. Institute for Astronomy, University of Edinburgh, Edinburgh, UK

    Weiguang Cui

  3. Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Florian Ruppin

  4. Dipartimento di Fisica, Sapienza Universitá di Roma, Rome, Italy

    Marco De Petris & Giulia Gianfagna

  5. INAF - Istituto di Astrofisica e Planetologia Spaziali, Rome, Italy

    Giulia Gianfagna

  6. EURANOVA, Mont-Saint-Guibert, Belgium

    Ichraf Lahouli, Gianmarco Aversano, Romain Dupuis & Mahmoud Jarraya

  7. Instituto de Astrofísica de Canarias (IAC) La Laguna, San Cristóbal de La Laguna, Spain

    Jesús Vega-Ferrero

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Contributions

D.d.A. led the project, wrote and run the machine learning codes and contributed to most of the writing of the paper. W.C. developed, ran The300 simulation, prepared the mock observation images with PYMSZ and contributed to writing most of the paper. F.R. wrote and run the code pipeline to introduce Planck-like limitations into clean mock observations and assisted with the writing of the paper. M.D.P. and G.Y. assisted with interpretation, manuscript preparation and revision. G.G., I.L., G.A., R.D., M.J. and J.V.-F. contributed to this work with the writing of the project and with machine learning technicalities.

Corresponding authors

Correspondence to Daniel de Andres or Weiguang Cui.

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Nature Astronomy and the authors thank Ziang Yan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Figs. 1–11, Discussion and Tables 1–3.

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de Andres, D., Cui, W., Ruppin, F. et al. A deep learning approach to infer galaxy cluster masses from Planck Compton-y parameter maps. Nat Astron 6, 1325–1331 (2022). https://doi.org/10.1038/s41550-022-01784-y

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