A new one dimensional search technique for unimodal functions is proposed which is on the average better than the Golden Section Method and is easy to program for machine search. An analysis based on the further assumption that the objective function is symmetric demonstrates the assertions made reg
A new multivariate technique for top quark search
✍ Scribed by Lasse Holmström; Stephan R. Sain; Hannu E. Miettinen
- Publisher
- Elsevier Science
- Year
- 1995
- Tongue
- English
- Weight
- 818 KB
- Volume
- 88
- Category
- Article
- ISSN
- 0010-4655
No coin nor oath required. For personal study only.
✦ Synopsis
We present a new multivariate event classifier, the PDE classifier, which can be used to identify rare signals in the presence of large backgrounds. The classifier is based on a discriminant function which uses kernel density estimates of the signal and background event densities. The PDE method offers the flexibility of a neural network but is conceptually simpler, theoretically better understood, and easier to optimize in practice. The technique is tested on Monte Carlo data using processes t~ --* e + Et + jets as signal and W + jets --~ e + Et + jets as background. The performance of the PDE classifier is similar to that of a neural network. Both methods outperform conventional analysis.
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