Difference: AtlasDataAnalysis (87 vs. 88)

Revision 882011-03-01 - AlistairGemmell

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Computentp, Neural Nets and MCLIMITS

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 The key feature of a neural network is its ability to be "trained" to recognise patterns in data, allowing high efficiency algorithms to be developed with relative ease. This training is typically done with sample data which has been generated artificially, resulting in an algorithm that is very effective at recognising certain patterns in data sets. The only shortcoming is the danger of "over-training" an ANN, meaning that it becomes overly discriminating and searches across a narrower range of patterns than is desired (one countermeasure is to add extra noise to training data).

Computentp :- Simply running the code as above will result in less than optimal Neural Net training. The training procedure requires equal numbers of events from signal and from background (in this case it results in half of the signal events being used in training, half for testing). However, the above code will take events from the background signal samples in proportion to the file sizes - these result in proportions not quite in accordance with physical ratios. As the Neural Net weights results according to information about the cross-section of the process and so on stored in the tree, the final result is that while the outputs are weighted in a physical fashion, the Net is not trained to the same ratios, and so is not optimally trained. To solve this problem, Computentp is used to mix together all background and signal samples., and assign TrainWeights to them, so that the events are weighted correctly for the Net's training.


Preparing samples for the Neural Net

Samples are produced for the Neural Net from AODs - results have previously been obtained for MC samples derived from v12 and v15 of athena. Current efforts are directed toward debugging the v15 results, and then upgrading to v16 input. The inputs are created from AODs using the TtHHbbDPDBasedAnalysis package (currently 00-04-18 and its branches are for v15, 00-04-19 is for v16).

Issues still to be resolved

1. In share/TtHHbbSetups.py:

include( "AtlasGeoModel/SetGeometryVersion.py" )
include( "AtlasGeoModel/GeoModelInit.py" )

Athena warns that both of these files are obsolete - this does not lead to an ERROR or WARNING, but nonetheless needs to be looked at.

2. In src/PreselectLeptons.cxx:

    const EMShower* shower;
    if ((*elecItr)->author() != egammaParameters::AuthorUnknown){
      trackIsolationEnergy20 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.2);
      trackIsolationEnergy30 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.3);
      trackIsolationEnergy40 = m_trackIsolationTool->trackIsolationEnergy((*elecItr)->trackParticle(),0.4);
      shower                 = (*elecItr)->detail<EMShower>(m_showerContainerName);
      //CCT: shower - seems to have stopped working in for data made with r838 (v15.3.1.6)                                                                               
      //was giving a seg fault as it tried to get etcone20 even if "shower" had not been successfully obtained!                                                                   
      if (!shower) {
        mLog << MSG::WARNING << "Invalid EMShower pointer!" << endreq;
        etcone20               = shower->etcone20()/pT;
        mLog << MSG::INFO << "shower->etcone20() = " << shower->etcone20() << ", pT = " << pT << endreq;

The warning message appears an awful lot - also, don't think this influences the Net inputs, but still should be looked at - do we need to get anything from the shower?

3. In the jobOptions file we currently have:

PreselectLeptons.McEventInfoName = "MyEvent"

However, in the athena output we still have:

StoreGateSvc      WARNING retrieve(const): No valid proxy for object McEventInfo  of type EventInfo(CLID 2101)

Need to work out why this jobOption does not over-ride the default. It might itself be overridden by python/ttH_defaults.py - if this is the case, then a number of other settings are also over-ridden.


Running the Neural Net

Overview of the process

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