A Guide to Comparisons of Star Formation Simulations with Observations

TL;DR

This paper reviews a method called "Taste-Testing" for comparing star formation simulations with observations by creating synthetic observations and applying statistical tests to understand the physics of molecular clouds and star formation.

Contribution

It introduces the "Taste-Testing" approach for more effective observation-theory comparisons in star formation research, emphasizing statistical analysis in observational space.

Findings

Synthetic observations facilitate meaningful statistical comparisons.

The lognormal nature of molecular clouds and the IMF may arise from random processes.

Direct relationships between CMF and IMF are not necessarily implied by their lognormal shapes.

Abstract

We review an approach to observation-theory comparisons we call "Taste-Testing." In this approach, synthetic observations are made of numerical simulations, and then both real and synthetic observations are "tasted" (compared) using a variety of statistical tests. We first lay out arguments for bringing theory to observational space rather than observations to theory space. Next, we explain that generating synthetic observations is only a step along the way to the quantitative, statistical, taste tests that offer the most insight. We offer a set of examples focused on polarimetry, scattering and emission by dust, and spectral-line mapping in starforming regions. We conclude with a discussion of the connection between statistical tests used to date and the physics we seek to understand. In particular, we suggest that the "lognormal" nature of molecular clouds can be created by the interaction of many random processes, as can the lognormal nature of the IMF, so that the fact that both the "Clump Mass Function" (CMF) and IMF appear lognormal does not necessarily imply a direct relationship between them.