That is non-convex.
However, unless you just chose not to show the constraints (other than t and gamma1 being nonnegative), a globally optimal solution is to choose all variables to be zero. But your code doesn’t even use all the declared CVX variables, and you included a bunch of unused input data, so I have no idea what the actual optimization problem you want to solve is.
Depending on what that actual problem is, perhaps gamma can be handled parametrically, i.e., call CVX within a for loop, using a different value of gamma on each CVX invocation. This is a standard kind of thing in cross-validation or hyperparameter tuning. Then choose the value of gamma which provides the best (lowest) optimal objective value; and use the corresponding optimal variable values (argmin) for that value of gamma1.