Recognizing the fingerprints of the Galactic bar: a quantitative approach to comparing model (l,v) distributions to observation

TL;DR

This paper introduces a new feature-matching method to compare hydrodynamical models with observed (l,v) gas distributions in the Milky Way, improving model identification of the Galactic bar region.

Contribution

It presents a novel automatic feature extraction and matching technique that outperforms traditional methods like ^2 or envelope distances in model fitting.

Findings

The method accurately identifies the correct Galactic model.

Feature matching outperforms traditional ^2 and envelope distance methods.

Application to the Galactic Bar region demonstrates effectiveness.

Abstract

We present a new method for fitting simple hydrodynamical models to the (l,v) distribution of atomic and molecular gas observed in the Milky Way. The method works by matching features found in models and observations. It is based on the assumption that the large-scale features seen in (l,v) plots, such as ridgelines and the terminal velocity curve, are influenced primarily by the underlying large-scale Galactic potential and are only weakly dependent on local ISM heating and cooling processes. In our scheme one first identifies by hand the features in the observations: this only has to be done once. We describe a procedure for automatically extracting similar features from simple hydrodynamical models and quantifying the "distance" between each model's features and the observations. Application to models of the Galactic Bar region (|l|<30deg) shows that our feature-fitting method performs better than \chi^2 or envelope distances at identifying the correct underlying galaxy model.