The picasso gas model: Painting intracluster gas on gravity-only simulations

We introduce picasso , a model designed to predict thermodynamic properties of the intracluster medium based on the properties of halos in gravity-only simulations. The predictions result from 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. Once trained, the model can be applied to make predictions for arbitrary potential distributions, allowing its use with flexible inputs such as N−body particle distributions or radial profiles. We present the model, and train it using pairs of gravity-only and hydrodynamic simulations. We show that when trained to learn the mapping from gravity-only to non-radiative hydrodynamic simulations, picasso can make remarkably accurate and precise predictions of intracluster gas thermodynamics, with percent-level bias and ~20% scatter for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:mi>r</mml:mi> <mml:mi>/</mml:mi> <mml:msub> <mml:mi>R</mml:mi> <mml:mrow> <mml:mn>500</mml:mn> <mml:mi>c</mml:mi> </mml:mrow> </mml:msub> <mml:mo>∈</mml:mo> <mml:mrow> <mml:mo stretchy="true" form="prefix">[</mml:mo> <mml:mn>0.1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="true" form="postfix">]</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . Training the model on hydrodynamic simulations including sub-resolution physics modeling yields robust predictions as well, albeit with the introduction of a radius-dependent bias and an increase in scatter. We further show that the model can be trained to make accurate predictions from very minimal halo information, down to mass and concentration, at the cost of modestly reduced precision. picasso is made publicly available on Github (https://github.com/fkeruzore/picasso) as a Python package, which includes trained models that can be used to make predictions easily and efficiently, in a fully auto-differentiable and hardware-accelerated framework.