Chemical post-processing of magneto-hydrodynamical simulations of star-forming regions: robustness and pitfalls

TL;DR

This study validates the use of post-processing chemical modeling in magneto-hydrodynamical simulations of star-forming regions, demonstrating high accuracy and highlighting key parameters affecting reliability.

Contribution

It provides the first comprehensive validation of post-processing chemistry techniques against self-consistent simulations, emphasizing the importance of integration time-step and sampling choices.

Findings

Post-processing achieves percent-level accuracy in chemical abundances.

Number of particles used does not significantly affect average properties.

Longer integration time-steps can lead to large errors and equilibrium bias.

Abstract

A common approach to model complex chemistry in numerical simulations is via post-processing of existing magneto-hydrodynamic simulations, relying on computing the evolution of chemistry over the dynamic history of a subset of particles from within the raw simulation. Here, we validate such a technique, assessing its ability to recover the abundances of chemical species, using the chemistry package KROME. We also assess, for the first time, the importance of the main free input parameters, by means of a direct comparison with a self-consistent state-of-the-art simulation in which chemistry was directly coupled to hydrodynamics. We have found that the post-processing is highly reliable, with an accuracy at the percent level, even when the most relaxed input parameters are employed. In particular, our results show that the number of particles used does not affect significantly the average properties, although it suppresses the appearance of possibly important spatial features. On the other hand, the choice of the integration time-step plays a crucial role. Longer integration time-steps can produce large errors, as the post-processing solution will be forced towards chemical equilibrium, a condition that does not always necessarily apply. When the interpolation-based reconstruction of chemical properties is performed, the errors further increase up to a factor of $\sim2$. Concluding, our results suggest that this technique is extremely useful when exploring the relative quantitative effect of different chemical parameters and/or networks, without the need of re-running simulations multiple times, but some care should be taken in the choice of particles sub-sample and integration time-step.