Exploring Artificial Neural Networks to Discover Higgs at LHC Using Neural Networks for B-tagging By Rohan Adur

Exploring Artificial Neural Networks to Discover Higgs at LHC Outline: What are Neural Networks and how do they work? How can Neural Networks be used in b- jet tagging to discover the Higgs boson? What results have I obtained using Neural Networks to find b-jets?

Neural Networks - Introduction Neural Networks simulate neurons in biological systems They are made up of neurons connected by synapses They are able to solve non-linear problems by learning from experience, rather than being explicitly programmed for a particular problem

The Simple Perceptron The Simple Perceptron is the simplest form of a Neural Network It consists of one layer of input units and one layer of output units, connected by weighted synapses Output layer Input layer Synapses connected by weights

The Simple Perceptron contd. Requires a training set, for which the required output is known Synapse weights start at random values. A learning algorithm then changes the weights until they give the correct output and the weights are frozen The trained network can then be used on data it has never seen before Output layer Input layer Synapses connected by weights

The Multilayer Perceptron The main drawback of the simple perceptron is that it is only able to solve linearly-separable problems Introduce a hidden layer to produce the Multilayer Perceptron The Multilayer Perceptron is able to solve non-linear problems Output layer Input layer Synapses Hidden Layer

Finding Higgs The Higgs boson is expected to decay to b- quarks, which will produce b-jets b-jet detection at LHC is important in detecting Higgs 40 million events happening per second b-taggers must reject light quark jets

b-tagging B mesons are able to travel a short distance before decaying, so b- jets will originate away from the primary vertex Several b-taggers exist IP3D tagger uses the Impact Parameter of the b-jets B ~ 1mm Primary Vertex B-jets IP Secondary Vertex SecVtx tagger reconstructs the secondary vertex and rejects jets which have a low probability of coming from this vertex

IP3D Tagger Good amount of separation between b-jets and light jets

b-tagger performance

Neural Network for b-tagging The current best tagger is a combination of IP3D and SV1 tag weights Using Neural Networks, can this tagger be combined with others to provide better separation?

The Multilayer Perceptron and b-tagging The TMultiLayerPerceptron class is an implementation of a Neural Network built into the ROOT framework It contains several learning methods. The best was found to be the default BFGS method Train with output = 1 for signal and output = 0 for background The b-tagging weights were obtained using the ATHENA release The data was obtained from Rome ttbar AOD files Once extracted, the weights were used to train the Neural Network

Results 5 Inputs used: Transverse momentum, IP3D tag, SV1 tag, SecVtx Tag and Mass 12 Hidden units and 1 Output unit

Results Contd.

EfficiencyIP3D+SV1Neural Network 60% % Rejection rates Mistagging efficiency EfficiencyIP3D+SV1Neural Network 60%1.14%0.73% 50%0.57%0.26% At fixed rejection RejectionIP3D+SV1Neural Network 10057%62%

Discussion of Results Using a Neural Network, b-taggers can be combined to provide up to double the purity at fixed efficiency At fixed rejection rate, the Neural Network provides 5% more signal than the IP3D+SV1 tagger alone Neural Network performance is not always reproducible. Each time training is undertaken a different network is produced

Conclusions Neural Networks are a powerful tool for b- jet classification Neural Networks can be used to significantly increase b-tagging efficiency/rejection ratios and could be useful in the search for Higgs Training a Neural Network on real data will be the next hurdle