Exploring Artificial Neural Networks to Discover Higgs at LHC

1. Exploring Artificial Neural Networks to Discover Higgs at LHC Using Neural Networks for B-tagging By Rohan Adur www.hep.ucl.ac.uk/~radur

2. 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?

3. 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

4. The Simple Perceptron Output layer • 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 Synapses connected by weights Input layer

5. 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 Synapses connected by weights Input layer

6. The Multilayer Perceptron Output layer • 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 Synapses HiddenLayer Synapses Input layer

7. 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

8. 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 ~ 1mm Primary Vertex B B-jets Secondary Vertex IP • SecVtx tagger reconstructs the secondary vertex and rejects jets which have a low probability of coming from this vertex

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

10. b-tagger performance

11. 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?

12. 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 10.0.1 release • The data was obtained from Rome ttbar AOD files • Once extracted, the weights were used to train the Neural Network

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

14. Results Contd.

15. Results Contd. Rejection rates Mistagging efficiency At fixed rejection

16. 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

17. 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