A Deep Learning Approach to Galaxy Cluster X-Ray Masses

Citation:

Ntampaka M, ZuHone J, Eisenstein D, Nagai D, Vikhlinin A, Hernquist L, Marinacci F, Nelson D, Pakmor R, Pillepich A, et al. A Deep Learning Approach to Galaxy Cluster X-Ray Masses. The Astrophysical Journal [Internet]. 2019;876.

Date Published:

May 01, 2019

Abstract:

We present a machine-learning (ML) approach for estimating galaxycluster masses from Chandra mock images. We utilize a ConvolutionalNeural Network (CNN), a deep ML tool commonly used in image recognitiontasks. The CNN is trained and tested on our sample of 7896 Chandra X-raymock observations, which are based on 329 massive clusters from the{\text{}}{IllustrisTNG} simulation. Our CNN learns from a low resolutionspatial distribution of photon counts and does not use spectralinformation. Despite our simplifying assumption to neglect spectralinformation, the resulting mass values estimated by the CNN exhibitsmall bias in comparison to the true masses of the simulated clusters(-0.02 dex) and reproduce the cluster masses with low intrinsic scatter,

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