Towards a method to anticipate dark matter signals with deep learning at the LHC

Understand

We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks.

• We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays.
• This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals.
• We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample.