We study the minimax risk for detecting a sparse elevated-mean Gaussian submatrix inside a larger noisy matrix. When the planted submatrix has size $n\times n$ and the ambient matrix has size $N\times N$ with $N = n^{1+α}$, the classical work of \cite{butuceasubmatrix2013} identifies the sharp detection boundary around which the minimax risk converges to $0$ or $1$. This paper extends that zero-one theory by determining the precise asymptotic rate of the minimax risk throughout a two-variable phase diagram. Above the detection boundary, we determine the precise exponent for the stretched or super-exponential decay of the risk. Below the boundary, where the risk tends to 1, we identify the exact polynomial order of the rate of convergence up to absolute multiplicative constants. In both of these regimes, the form of the sharp asymptotics changes around the line $α+ δ= 1/2$ where $δ$ indicates the signed distance from the boundary. Finally, on the detection boundary, we show that the minimax risk converges to the non-degenerate constant $\frac12$ in the very sparse case where $n$ remains fixed and $N \to \infty$. Each of these rates corresponds to the risk of a suitably calibrated scan or sum test, whence follow the upper bounds. To show the sharpness of these bounds, we rely on refined second-moment methods applied to random variables chosen carefully according to the particular regime. Our results also extend to the tensor setting.
