Finalizing the Bootstrap Monte Carlo Simulation
By Elijah Bernstein-Cooper, September 1, 2015, 0 comments.

Table of Contents

Intercept Error

We calibrate the Planck data by scaling the slope, and offsetting the Planck by an intercept determined from the Kainulainen et al. (2009) 2MASS data. This 2MASS (K+09) data has been background subtracted. We fit a first-degree polynomial to the Planck vs. K+09 relationship. We subtract the fitted intercept from the Planck data in further analysis.

However we should incorporate the error of the intercept in our monte carlo simulation. One way to estimate this error is to use the 2MASS data from Lee et al. (2012) for Perseus (Lee+12). The Lee+12 data is background subtracted independently from the K+09 data. Thus an estimate of the error of the intercept for the Planck data is to use the fitted intercept between the K+09 and Lee+12 relationship.

Planck vs. K+09

Planck vs. Lee+12

Lee+12 vs. K+09

Figure 1

Contour plots of Lee+12 2MASS, K+09 2MASS, and Planck relationships. Scaling the Planck data with the K+09 vs. Lee+12 data would lead to different intercepts. We therefore take the fitted intercept between the Lee+12 and K+09 relationship as the uncertainty in the intercept, 0.2 mag.

Extending HI width of California

We should perhaps include more HI components in California, seeing as there are two HI components.

Figure 2

Cloud spectra. California now includes two components associated with the cloud HI. The width of the HI range has not changed, maybe a couple km/s, but the range is know slightly more negative.

Examining vs N(HI) Relationship in California

Figure 3

vs N(HI) relationships for each cloud. The contour levels are now the same in each cloud: 0.99, 0.98, 0.95, 0.86, 0.59% of the data. The bootstrap fit is dominated by the lower data with relatively smaller error.

The scatter plot with error bars shows the high data points do not contribute significantly to the fit because of their larger error.

Figure 4

vs N(HI) relationships for each cloud, showing only every 1 out of every 100 data points.