Emerging Methods in Reconstruction, Analysis and Simulation

Particle detection increasingly relies on the synergy between reconstruction techniques, detailed simulation frameworks, and powerful data analysis methods. Such aspects have always been central to experimental physics, and throughout the history of the field, their progress has been closely tied to advances in computing and artificial intelligence is no different.The vertiginous development of AI in recent years is opening new pathways to rethink how we interpret the immense data streams produced by modern detectors, enabling more efficient reconstruction, enhanced simulation accuracy, and new analysis paradigms driven by data-driven inference. Not only that, the rise of AI-assisted coding is lowering barriers to implementing complex reconstruction and analysis workflows and allowing implementing new ideas into working software more quickly.These ongoing advances are reshaping what experimentalists can achieve with modern detectors. This issue aims to capture that evolving landscape, offering a platform for both solid methodological advances and forward-looking ideas at the intersection of physics and computation.Papers are welcome in all topics that explore these new boundaries and possibilities, to name a few — although not limited to:– ML-based event reconstruction and particle identification.– Data-driven and generative models for fast and high fidelity event generation and simulation.– AI-based data acquisition and real time data reduction.– Optimization of detector design and performance.– Continuous monitoring, data validation, and anomaly detection.– Approaches to training and inference based on experimental data.– Development of tools and frameworks for efficient, AI-assisted workflows. Topic editors: Ander Simón Estévez, Santiago Folgueras, Bernardo Sotto-Maior Peralva, Gonzalo Martínez Lema, Saúl Alonso-Monsalve, Krzysztof Graczyk Source: https://www.frontiersin.org/research-topics/79858/emerging-methods-in-reconstruction-analysis-and-simulation