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  A Deep Learning Approach to Galaxy Cluster X-Ray Masses

Ntampaka, M., ZuHone, J., Eisenstein, D., Nagai, D., Vikhlinin, A., Hernquist, L., et al. (2019). A Deep Learning Approach to Galaxy Cluster X-Ray Masses. The Astrophysical Journal, 876.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0005-D036-3 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-D037-2
Genre: Journal Article

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https://ui.adsabs.harvard.edu/abs/2019ApJ...876...82N (Any fulltext)
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 Creators:
Ntampaka, M.1, Author
ZuHone, J.1, Author
Eisenstein, D.1, Author
Nagai, D.1, Author
Vikhlinin, A.1, Author
Hernquist, L.1, Author
Marinacci, F.1, Author
Nelson, D.1, Author
Pakmor, R.1, Author
Pillepich, A.1, Author
Torrey, P.1, Author
Vogelsberger, M.1, Author
Affiliations:
1Max Planck Institute for Astronomy, Max Planck Society and Cooperation Partners, ou_2421692              

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Free keywords: galaxies: clusters: general methods: statistical X-rays: galaxies: clusters Astrophysics - Cosmology and Nongalactic Astrophysics
 Abstract: We present a machine-learning (ML) approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep ML tool commonly used in image recognition tasks. The CNN is trained and tested on our sample of 7896 Chandra X-ray mock observations, which are based on 329 massive clusters from the {\text{}}{IllustrisTNG} simulation. Our CNN learns from a low resolution spatial distribution of photon counts and does not use spectral information. Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (−0.02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all. In contrast, a more standard core-excised luminosity method achieves 15%─18% scatter. We interpret the results with an approach inspired by Google DeepDream and find that the CNN ignores the central regions of clusters, which are known to have high scatter with mass.

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 Dates: 2019
 Publication Status: Issued
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Title: The Astrophysical Journal
Source Genre: Journal
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Pages: - Volume / Issue: 876 Sequence Number: - Start / End Page: - Identifier: -