Machine-learning techniques for model-independent searches in dijet final states

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.

• •Focuses on using machine learning in particle physics.
• •Aims to find new particles or interactions.
• •Employs a model-independent approach.