Imaging Electrons in ATLAS

Efficient and accurate electron reconstruction, identification, and calibration are critical for signal selection and uncertainty reduction in a broad range of ATLAS analyses. Traditionally, electron algorithms are built using physics-motivated, derived variables. This talk explores an alternate method for representing electrons by building images using calorimeter cells. These images can then be processed using machine learning algorithms like convolutional neural networks, yielding improved identification and calibration.