Speaker
Description
The Cherenkov Telescope Array Observatory (CTAO) represents the next generation of ground-based gamma-ray telescopes, designed to probe the very-high-energy (VHE) sky above 20 GeV with unprecedented sensitivity. With the first Large-Sized Telescope (LST-1) prototype already taking data on La Palma, robust software is required to accurately reconstruct the properties of primary particles (type, energy, and arrival direction) from the stereoscopic records of extensive air showers. In this contribution, we present a status update on CTLearn, a deep-learning-driven framework for event reconstruction in imaging atmospheric Cherenkov telescopes that is compatible with ctapipe, the standard low-level data processing library for CTAO. We highlight a substantial architectural expansion of the framework: while originally built exclusively for TensorFlow, CTLearn has been updated to support both TensorFlow and PyTorch backends while maintaining a unified interface for users. CTLearn utilizes convolutional neural networks to infer event properties directly from pixel-wise camera data, exploiting both integrated charge and temporal waveform information to capture the full evolution of the shower. We report on recent activities validating CTLearn against standard Random Forest methods using LST-1 observations of the Crab Nebula. We discuss training strategies to handle varying observational conditions, specifically comparing the performance of single generalized models versus altitude-dependent ensembles. We also present novel efforts to optimize model performance using transfer learning to adapt networks across telescope configurations, and model compression techniques such as pruning. These optimizations aim to significantly reduce computational resource consumption and inference time while maintaining the high sensitivity required for CTAO science goals.