Machine-learning techniques for model-independent searches in dijet final states
Published:Dec 23, 2025 14:33
•1 min read
•ArXiv
Analysis
This article likely discusses the application of machine learning to analyze data from particle physics experiments, specifically focusing on identifying new particles or interactions in dijet events without relying on pre-defined models. The use of 'model-independent' suggests a focus on discovering unexpected phenomena.
Key Takeaways
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