The performance of the likelihood ratio test is considered for a many-point interaction point process featuring a reduced number of isolated points. Limit theorems are proved that establish the Poissonian asymptotic distribution of the log-likelihood function for point processes with the isolated-point-penalization joint probability density function. The asymptotic distribution is used to approximate the detection probability associated with the likelihood ratio test. The approximation is compared to empirical results generated using Markov-chain Monte Carlo simulation. The reported results provide an efficient alternative method to simulation in assessing the performance of hypothesis testing for the point-process model considered.