shaping the gas: understanding gas shapes in dark matter haloes with interpretable machine learning

@article{MachadoPolettiValle2021shapingTG,
  title={shaping the gas: understanding gas shapes in dark matter haloes with interpretable machine learning},
  author={Luis Fernando Machado Poletti Valle and Camille Avestruz and David J Barnes and Arya Farahi and Erwin T Lau and Daisuke Nagai},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2021}
}
The non-spherical shapes of dark matter and gas distributions introduce systematic uncertainties that affect observable–mass relations and selection functions of galaxy groups and clusters. However, the triaxial gas distributions depend on the non-linear physical processes of halo formation histories and baryonic physics, which are challenging to model accurately. In this study, we explore a machine learning approach for modelling the dependence of gas shapes on dark matter and baryonic… 
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