# A NEAT Solution: Feature Selection through Neuroevolution in Deep Learning-Based $$H \rightarrow \tau \tau$$ Classification

Author/​Artist:
Rheingold, Grant [Browse]
Format:
Senior thesis
Language:
English
The presence of background noise from other Standard Model processes makes identifying $$H \rightarrow \tau \tau$$ particularly difficult. Contemporary methods involve building deep neural networks to classify the Higgs signal from the background and initial success has been seen. Difficulty arises however in determining, both from a physics and statistical perspective, the optimal selection of features for these networks. We present an implementation of the Neuroevolution of Augmenting Topologies (NEAT) algorithm in feature selecting for a bag of multilayer dropout neural networks. Our method called Deep NEAT'' achieves an Approximate Median Significance (AMS) of $$3.595 \pm 0.027$$, outperforming benchmarks set by TMVA (AMS: 3.120) and Multiboost (AMS: 3.405). Deep NEAT demonstrates a method for automatic feature selection and confirms the benefit of machine learning techniques in high energy particle physics.