Speaker
Description
In the 5–300 PeV energy range, the iron nuclear spectrum—particularly above 10 PeV—suffers from large systematic uncertainties arising from hadronic interaction models. We aim to identify a selection variable that exhibits weak dependence on hadronic models to reduce these systematic errors. Existing studies have shown that the lateral azimuthal fluctuation characteristics of showers are minimally affected by hadronic models, demonstrating the potential to reduce systematic uncertainties. The LCm analysis method, based on this concept, has been validated through both simulations and experimental data at the KASCADE experiment. However, due to significant differences in array geometry, detector spacing, and trigger conditions between KASCADE and KM2A, KASCADE's analysis parameters, selection criteria, and fitting procedures cannot be directly transferred. Currently, an LCm-based multivariate discrimination and spectrum unfolding pipeline adapted to the KM2A array has not yet been established, nor is there a systematic error evaluation framework for this method. This project aims to develop an LCm-PCA heavy-nuclei discrimination and spectrum unfolding method tailored to KM2A, combining shower fluctuation features, multivariate dimensionality reduction, and two-dimensional template fitting techniques to reduce the reliance of traditional analyses on hadronic interaction models.
