Multivariate methods for hadronic final states in electron-positron collisions at center of mass energy = 500 GeV

We approach the hadronic final state events in a future linear collider at s = 500 GeV from the knowledge discovery (data mining) point of view. We present FastCal, a fast configurable calorimeter Monte Carlo simulator for linear collider detector simulations that produces data at a rate that is 3000 times that of full simulation. Neural networks based on earlystopping are designed for the jet-combinatorial problem. CJNN, a neural network package is presented for use in the linear collider analysis environment. Neural network performances are optimized by implementing an ensemble of neural networks. A binary tree is used to obtain novel automatic cuts on physics variables. Data visualization is introduced as a crucial component of data analysis, and principal component analysis is used to understand data distributions and structures in multiple dimensions. Finally, cluster analyses with fuzzy c-means and demographic clustering are used to partition data automatically in an unsupervised regime, and we show that for fruitful use of these algorithms, understanding the data structures is crucial.