Table of Contents
In the lastest post I discussed how to develop the core sample. I suggested that the cores be chosen randomly. However we would like to choose random cold clumps that are not directly adjacent to one another. I thus chose a random sample, excluding any randomly chosen cores within 25$\ft$ from the rest of sample, or 5 pixels.
Below is figure showing the randomly chosen cores and the respective regions I chose by hand. By choosing random cores we’re likely sampling cores at various stages of maturity, i.e., N(H$_2$) contents.
$A_V$ map overplotted with 10 random core regions in each cloud.
Unfortunately this selection method was not very fruitful. Below show HI vs H surface density distributions and model fits for each cloud. The names correspond to the galactic latitude.
We see very few thresholds in HI, and little H$_2$ in most of the cores.
HI vs. H for different cores with fitted models as solid lines, and the shaded regions as the 68% confidence regions for the model fits. The majority of the cores do not show a turnover to molecular hydrogen.
High N(H$_2$) Selection
If our goal is to steady the relationship between H$_2$ and HI then we should choose more developed cores. The next best sample I can think of is to use cold clumps with the highest N(H$_2$) values determined from extinction measurements. See Figure 3 for the distribution of cold clumps with the highest molecular hydrogen contents of each cloud.
$A_V$ map overplotted with locations of cores with the highest N(H$_2$) contents. This core selection is more similar to what I chose at first, corresponding to the highest $A_V$ core regions in each cloud. These cores may be better suited to testing the HI-to-H$_2$ transition given that H$_2$ has been identified in the cores.