We proposed a new penalized method in this paper to solve sparse Poisson Regression problems. Being different from l1 penalized log- likelihood estimation, our new method can be viewed as penalized weighted score function method, which produces a tuning-free feature. We show that under mild conditions, our estimator is l1 consistent and the tuning parameter can be pre-specified, which shares the same good property of the square-root Lasso.