Speaker
Description
The LHAASO experiment combines multiple detector arrays with complementary sensitivities to extensive air showers. Among them, KM2A has strong gamma–hadron discrimination capability, while LACT provides high-resolution imaging and precise directional reconstruction. Exploiting the synergy between these two detectors is crucial for improving the overall performance of the experiment.
In this study, we investigate a hybrid reconstruction approach in which KM2A information is used to assist LACT particle identification. A common dataset is reconstructed independently by both detectors, and the KM2A-derived parameters are incorporated into the machine learning classifier of LACT.
By augmenting LACT input features with KM2A observables, the gamma–hadron separation is significantly improved. This method demonstrates the potential of multi-detector data fusion to enhance reconstruction performance in ground-based gamma-ray experiments.
