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\textbf{Thesis Progress Report}  \textbf{Introduction} \\  The goal of this thesis is to compare the stellar mass growth rate vscurrent  mass at various redshift  of local group dwarf galaxies with distant galaxies. This work was started in the fall, but was registered with the astronomy department. As this is the first term registered in the Physics department, this progress report will cover work done since the fall. \textbf{Data} \\  The early part of the project was dominated by trying to understand spent familiarizing myself with  the data that would be used. used, and the techniques used to gather that data.  The data used for distant galaxies was presented in \cite{Tomczak_2014}. This paper contains a table of mass vs number density (number of galaxies per unit volume) for $0.2 < z < 3$ which we use to construct the stellar mass function (SMF). (SMF) which shows the frequency of galaxies at different masses and redshifts.  However, while we plotted this data and various subsets of it (only star forming or quiescent galaxies) we do not use the raw data. Instead, we use a fitting function - a  paramaterised double Schechter function -  from \cite{Leja_2015} which smooths the data and ensures the number density at each mass is monotonically increasing as $z \rightarrow 0$. With this done, graphs showing the mass over $0.2 < z < 3$ for various start masses were constructed. The local group dwarf galaxy data was taken from \cite{Weisz_2014}. This paper determines the mass of a subset of the known dwarf galaxies between $0 < z < 2.6$ 2.6$. It does this  by calculating determining  the star formation history (sfh). (SFH) by constructing a color magnitude diagram and determining using know properties of stars when star formation occurred.  As with the distant galaxies, graphs showing the percentage of mass over time for various groups of the galaxies (grouped by galaxy shape or location) were plotted to better understand the data. A second distant galaxy data set was introduced later in the project. This data, taken from \cite{Whitaker_2014} was used to confirm that the comparison between the two main data sets were reasonable and conformed to other data. Again, a paramaterisation parameterization  rather than raw data was used. \textbf{Analysis} \\  A number of corrections must be applied to these data sets before they can be compared.  The first correction is for mergers. The local group data is based on galaxies that have not undergone mergers \textbf{is this true?}, mergers,  while some of the distant galaxies will have. This has the effect of reducing the total number of galaxies in the sample over time, reducing the overall number density.  We make the merger correction to the SMF using the method shown in \cite{Gomez_2015}. Supporting material such as plots of expected merger rates at various mass ratios and redshifts were also constructed to ensure that we were applying this correction correctly. We also apply a correction for mass loss to both the local group and \cite{Whitaker_2014} data. As these both determine mass by integrating the star formation rate over time, the data shows the total stellar mass formed by a certain time, rather than the total stellar mass present at that time. Much of this mass loss is caused by the death of high mass, short lifespan ($ < 100Myr$) stars and so can be approximated as instantaneous using a multiplicative factor. The \cite{Tomczak_2014} determines mass data is  from observed luminosity instantaneous mass  and is  not star formation rates, calculated from the SFH  and so this correction  is not applied there. applied.  Finally, a environmental correction was applied to the local group data. If we split the known local group We expect that  galaxies into groups based on their location - satellites in different places in the Local Group (satellites  of the Milky Way, satellites of M31 (Andromeda) and those galaxies  attached to neither of these two - we find that the main galaxies) would have different growth rates. However,  the \cite{Weisz_2014} data, which data  contains only a subset of approximately half of all galaxies, known local group galaxies and  does not sample evenly from these three groups. environments (for observational reasons).  To correct for this, we weight galaxies to ensure that at each mass and redshift the different environments are correctly weighted. A significant analysis was also performed on the errors on the local group data reported by \cite{Weisz_2014}. These errors were calculated using methods defined in \cite{Dolphin_2012} (systematic) and \cite{Dolphin_2013} (random) but are considered extremely conservative. A method to determine a more reasonable set of uncertainties was not found and so we adopt the literature  convention used in a later paper established  by Weisz \cite{Weisz__2014} and simply apply a 50\% fractional uncertainty to all masses. This method is also considered conservative but significantly improves on the original. \textbf{Comparison} \\  Having made the corrections discussed above, we compare the data sets. We find that the \cite{Tomczak_2014} data tends to be within broadly agrees with  the (large) error bars of \cite{Whitaker_2014} data, but that both show higher growth rates than  the local group data. Compared to If  this data set, the local group data underestimates continues to hold as  the rate of growth at all masses final corrections are applied  andreshifts by ~0.3 dex. However, we find that  the \cite{Whitaker_2014} data work  is significatly different to both other data sets. checked, this is an interesting result as it would should galaxy strangulation; a process by which stellar mass growth rates are slowed in the presence of large gravitational fields.  \textbf{Conclusion} \\  While we have preliminarily  done most of the corrections needed to compare these data sets, there is still work to do. In particular, work will the merger correction is not perfectly understood and may  be needed to explain the source of some of  the discrepancy between the\cite{Whitaker_2014} data and the other  two distant galaxy  data sets. A significant amount of work will Tests to show that the code used for analysis is performing correctly  also need to  be needed written. Finally, this work also needs  to write be written  up and present this work. presented.