MCLimits Code

• mclimits.C
• mclimits.h
• toy.C

Prerequisites

Project Aims

Prerequisites

Online Resources

Tools

• The ROOT Manual

• ROOT TMVA Users Guide (Toolkit for Multivariate Data Analysis, including ANNs)

• The Karma Rootra

Limitations

• Due to ROOT limitations, it cannot output files larger than 2GB - this has been remedied by cutting the input: an additional constraint was added to genemflat_batch.sh, specifically GeneralParameter string 1 Constraint=(my_failEvent&65536)==0.
• It must also be run on a PBS machine because of the structure of the genemflat_batch.sh file (i.e. PBS commands).
• The script is set up to use an older version of the code (v4_02) - do newer versions offer any improvements?
• The file <nop>teststeerFlatPlotterATLAStthSemileptonic.txt appears to contain an invalid range for the pseudorapidity (max. value = pi)
• Adjusted ntuple_area so that it can be set dynamically (i.e. as a 9th input variable)
• If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&
• It would be desirable to adapt the code to be able to process different signals, e.g. lbb.

TMVA Training Plots

There is a macro in the latest NNFitter version which will calculate the responsiveness of the neural net as a function of the number of training cycles, in order to gauge the optimal number of cycles to use (i.e. avoid the dangers of under- or over-training). The macro must be run in a directory where a neural net run has already been carried out.

• Type source exportscript.sh to export the relevant parameters.
• Enter ROOT and type .x <nop>runTrainTest.C

This will create two .eps output files, one showing the success of signal/background fitting, and the other displaying the sensitivity of the neural net to the number of training cycles used, allowing the speed of convergence to be gauged.

Setting up Subversion

Note - the first part of this section outlines the procedure used to set up a repository with Subversion. Once the repository has been created, these commands do not need to be used again, so if you are working with code from an existing repository you should start at checking out.

Running analysis & making ntuples

This is the procedure used to remake the ATLAS ntuples, using code from CERN Subversion repositories. These ntuples were then used as input for the neural net.

-- CatherineWright - 2010-02-12

Online Resources

The central ATLAS TWiki is an invaluable resource, with extensive information on all aspects of data analysis, including a detailed guide to the relevant software. For an introductory overview see the analysis and computing workbooks here. The computing workbook gives a detailed account of the full chain of data generation and analysis, together with guidance on using Athena and the Grid. This is intended to be consulted before the analysis workbook, since the latter assumes a reasonable working knowledge of the ATLAS computing environment.

Tools

ANN :- This is a kind of algorithm with a structure consisting of "neurons" organised in a sequence of layers. The most common type, which is used here, is the Multi-Layer Perceptron (MLP), which comprises three kinds of layer. The input neurons are activated by a set trigger, and once activated they pass data on to a further set of "hidden" neurons (which can in principle be organised into any number of layers, but most frequently one or two), and finally the processed data is forwarded to the output neurons.

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).

ROOT :- This is a data analysis package developed by CERN specifically for use in nuclear and particle physics. It is extremely flexible and powerful, especially for generating graphical representations (e.g. histograms) of data. Essentially, it may be thought of as an object-oriented language compatible with C++, although ROOT code generally does not resemble pure C++. Some useful ROOT resources are:

• The ROOT Manual

• ROOT TMVA Users Guide (Toolkit for Multivariate Data Analysis, including ANNs)

• The Karma Rootra

Limitations

• Due to ROOT limitations, it cannot output files larger than 2GB - this has been remedied by cutting the input: an additional constraint was added to genemflat_batch.sh, specifically GeneralParameter string 1 Constraint=(my_failEvent&65536)==0.
• It must also be run on a PBS machine because of the structure of the genemflat_batch.sh file (i.e. PBS commands).
• The script is set up to use an older version of the code (v4_02) - do newer versions offer any improvements?
• The file <nop>teststeerFlatPlotterATLAStthSemileptonic.txt appears to contain an invalid range for the pseudorapidity (max. value = pi)
• Adjusted ntuple_area so that it can be set dynamically (i.e. as a 9th input variable)
• If USEHILOSB is set to 1 then && must be appended to cut criteria, e.g. GeneralParameter string 1 Constraint=(my_failEvent&65536)==0&&
• It would be desirable to adapt the code to be able to process different signals, e.g. lbb.

TMVA Training Plots

There is a macro in the latest NNFitter version which will calculate the responsiveness of the neural net as a function of the number of training cycles, in order to gauge the optimal number of cycles to use (i.e. avoid the dangers of under- or over-training). The macro must be run in a directory where a neural net run has already been carried out.

• Type source exportscript.sh to export the relevant parameters.
• Enter ROOT and type .x <nop>runTrainTest.C

This will create two .eps output files, one showing the success of signal/background fitting, and the other displaying the sensitivity of the neural net to the number of training cycles used, allowing the speed of convergence to be gauged.

Setting up Subversion

Note - the first part of this section outlines the procedure used to set up a repository with Subversion. Once the repository has been created, these commands do not need to be used again, so if you are working with code from an existing repository you should start at checking out.

Running analysis & making ntuples

This is the procedure used to remake the ATLAS ntuples, using code from CERN Subversion repositories. These ntuples were then used as input for the neural net.