### Event Reconstruction  
The images collected by IACTs are the indirect signature (mostly in the ultraviolet wavelength band) of the interaction of a VHE photon (or charged particle) with the atmosphere. The data need to be processed to reconstruct the energy and the arrival direction of the primary event and to discriminate between photon- and hadron-initiated events. To this aim, several reconstruction steps are required, after the Cherenkov signals recorded by the telescopes' cameras have been fully calibrated [14]. * **Image Cleaning. ** The main sources of noise in the images collected by the telescope's camera are the night sky diffused background light, electronic noise, and other signals not related to Cherenkov radiation (e.g., stars). These must be removed in order to isolate the [PERSON] shower image. * **Image Parameterization. ** Each atmospheric shower produces a unique [PERSON] light signature due to the complex cascade of particle interactions in the atmosphere. To analyze these distinctive patterns, we employ a comprehensive set of morphological parameters that capture essential features such as the shower's geometric properties and the spatial distribution of Cherenkov light across the camera plane. * **Stereo Reconstruction. ** When the same shower is observed by more than one telescope, the information from multiple detectors can be combined in order to reconstruct the full 3D trajectory of the incident primary. For gamma/hadron discrimination, we employ the Random Forest algorithm, a powerful ensemble learning technique particularly well-suited for classification problems in astroparticle physics. This method is implemented in the ASTRI data reduction and scientific analysis software package (A-SciSoft v0.7.4 ) [14], establishing our benchmark for performance evaluation. Random Forest [15] operates by constructing multiple decision trees during training, with each tree built from a bootstrap sample (known as \"bagging\") of the training data. The algorithm introduces randomness by considering only a subset of features at each split point--a technique known as the random subspace method. This approach ensures diversity among trees and reduces correlation between them, thereby enhancing the generalization ability of the ensemble. The combined analysis of morphological parameters and stereoscopic data yields a detailed characterization of each event. Through this machine learning approach, we derive both a classification parameter (gammaness) indicating the likelihood of the event being gamma-ray-induced and an estimate of the primary particle's energy. For classification tasks such as distinguishing gamma-ray events from hadronic backgrounds, the final prediction is determined by majority voting across all trees, while for regression problems (such as energy estimation), it uses the average of the predictions. The strength of Random Forest lies in combining numerous \"weak learners\" (individual decision trees) into a robust \"strong learner\" (the forest). Each tree captures different patterns in the data, and their collective wisdom provides superior predictive performance compared to any individual tree. For our gamma/hadron separation task, this ensemble approach effectively handles the complex, high-dimensional feature space created by both morphological and temporal shower parameters. Random Forests provide several key advantages over traditional decision trees:  
* **Reduction in Overfitting**: By averaging the predictions of multiple trees, the Random Forest mitigates the risk of overfitting that is often associated with individual decision trees. * **Robustness**: The aggregation of multiple trees' predictions leads to more stable and reliable outputs, making the model robust to noise and variations in the data. * **Feature Importance**: Random Forests inherently provide a measure of feature importance, allowing for insights into which features are most influential in the prediction process.