Gamma ray full spectral analysis method optimization of an ill-conditioned problem

In gamma ray full spectral analysis, the risk that the covariance matrix is ill-conditioned is high, especially in prompt gamma ray neutron activation analysis technology. The ill-conditioned matrix increases the sensitivity of the system to errors. To address this issue, this study offered an optimization approach that involved adding minor disturbances to the covariance matrix and determining the value of the disturbances using the particle swarm optimization algorithm. After optimization, the datasets from two verification experiments became more stable.