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Non-linear bias measurements require a great level of control of potentialsystematic effects in galaxy redshift surveys. Our goal is to demonstrate theviability of using Counts-in-Cells (CiC), a statistical measure of the galaxydistribution, as a competitive method to determine linear and higher-ordergalaxy bias and assess clustering systematics. We measure the galaxy bias bycomparing the first four moments of the galaxy density distribution with thoseof the dark matter distribution. We use data from the MICE simulation toevaluate the performance of this method, and subsequently perform measurementson the public Science Verification (SV) data from the Dark Energy Survey (DES).We find that the linear bias obtained with CiC is consistent with measurementsof the bias performed using galaxy-galaxy clustering, galaxy-galaxy lensing,CMB lensing, and shear+clustering measurements. Furthermore, we compute theprojected (2D) non-linear bias using the expansion $\delta_{g} = \sum_{k=0}^{3}(b_{k}/k!) \delta^{k}$, finding a non-zero value for $b_2$ at the $3\sigma$level. We also check a non-local bias model and show that the linear biasmeasurements are robust to the addition of new parameters. We compare our 2Dresults to the 3D prediction and find compatibility in the large scale regime($>30$ Mpc $h^{-1}$)

Measuring Linear and Non-linear Galaxy Bias Using Counts-in-Cells in the Dark Energy Survey Science Verification Data