Speaker
Description
Post-processing techniques emulating baryonic physics in gravity-only simulations are a cornerstone of modern cosmology. In particular, predicting the thermodynamic properties of the intracluster gas is necessary to exploit these simulations for galaxy cluster cosmology in the millimeter-wave and X-ray domains. In this talk, I will introduce picasso, a model to predict thermodynamic properties of the intracluster medium from halos in gravity-only simulations. Predictions are based on the combination of an analytical gas model, mapping gas properties to the gravitational potential, and of a machine learning model to predict the model parameters for individual halos based on their scalar properties, such as mass and concentration. The model is trained using pairs of gravity-only and hydrodynamic simulations with identical initial conditions. I will show that picasso can make remarkably accurate and precise predictions of intracluster gas thermodynamics. I will discuss the numerical implementation of the picasso model, which is publicly available as a Python package that includes trained models and can be used to make predictions easily and efficiently in a fully auto-differentiable and hardware-accelerated framework. I will finish by presenting the first synthetic data products created by using picasso to emulate intracluster gas thermodynamics in state-of-the-art gravity-only cosmological simulations.
| Would you be interested in presenting a poster if the conference is oversubcribed? | No |
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