Electromagnetic Showers Beyond Shower Shapes

Abstract

Correctly identifying the nature and properties of outgoing particles from high energy collisions at the Large Hadron Collider is a crucial task for all aspects of data analysis. Classical calorimeter-based classification techniques rely on shower shapes -- observables that summarize the structure of the particle cascade that forms as the original particle propagates through the layers of material. This work compares shower shape-based methods with computer vision techniques that take advantage of lower level detector information. In a simplified calorimeter geometry, our DenseNet-based architecture matches or outperforms other methods on $e^+$-$\gamma$ and $e^+$-$\pi^+$ classification tasks. In addition, we demonstrate that key kinematic properties can be inferred directly from the shower representation in image format.