deletions | additions       diff --git a/section_Analysis_subsection_GMC_properties__.tex b/section_Analysis_subsection_GMC_properties__.tex  index d977c9e..a5ab488 100644  --- a/section_Analysis_subsection_GMC_properties__.tex  +++ b/section_Analysis_subsection_GMC_properties__.tex 
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We identify molecular clouds in the the CO emission line data using the CPROPS algorithm (Rosolowsky & Leroy, 2006)\footnote{We use the \texttt{cpropstoo} implementation at \url{http://github.com/akleroy/cpropstoo}}. We utilize their recommended algorithm for identifying GMCs in interferometer data described as follows. The algorithm first calculates a spatially and varying estimate of the noise in the map by calculating the rms ($\sigma(\alpha,\delta)$) of signal-free channels. Emission is then identified as those pixels in the (three-dimensional) data cube that are larger than $4\sigma(\alpha,\delta)$ in two adjacent velocity channels. This emission mask is then extended to include all connected pixels which are larger than $2\sigma(\alpha,\delta)$ in three adjacent channels. We test the masking algorithm by applying it to the negative data and we find no false positives are included, so the masking criteria are likely robust.  The masked emission is then divided into individual molecular clouds using a seeded watershed algorithm, with individual clouds being defined by local maxima that are separated by at least 15 pc spatially or 2.6 km~s$^{-1}$ in velocity. Any pair of local maxima in the same contiguous region of the mask are also required to be at least $2\sigma(\alpha,\delta)$ above the saddle point of emission connecting those maxima. This cloud extract identifies 394 clouds across the face of the galaxy.  We determine the macroscopic properties of the GMCs in the system by calculating moments of the emission line data. To account for emission below the edge of the emission mask, the values of the moments are extrapolated as a function of pixel value to the 0 K brightness threshold (see Rosolowsky \& Leroy 2006) for details. This extrapolation is necessary to avoid bias in low signal-to-noise data, but it can introduce substantial uncertainty (up to 100\% for clouds near the signal-to-noise cut). We calculate the CO luminosity of the molecular clouds ($L_{\mathrm{CO}}$) by integrating the emission associated with each cloud, with a quadratic extrapolation. The luminous mass is calculated by scaling by a single CO-to-H$_2$ conversion factor:  \begin{equation}