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Neural Networks for HEP

The NNHEP Project


NNHEP is a neural network package adopted for problems in HEP. The main idea of the project is to prepare a software framework, where people can easily add new components (modules, plugins) with new learning algorithms, network architectures, etc. The software design should allow all the elements are properly interfaced with each other in order to create a chain of the desired methods for analysis. In this way, it will be possible to combine different algorithms in the analysis chain. For example, in the pre-processing of input data one can use unsupervised learning methods, like Self Organizing Map (SOM), and then pass the SOM output to the feed-forward neural network (NN), use Genetic Algorithms (GA) to optimise the parameters of NN, add a boosting algorithm in order to improve the efficiency and plot necessary monitoring/final distributions with the modern level of graphics (e.g. in ROOT package). In the same way user should have a possibility to change the intermediate components (e.g. use Bayes NN (BNN) instead of NN) and implement new training methods, etc. It is written in C++.
More details on the project can be found in NNHEP Wiki.


Any questions or comments should be directed to nnhep@projects.hepforge.org.