MCNP Output Data Analysis with ROOT (MODAR)

MCNP Output Data Analysis with ROOT (MODAR) is a tool based on CERN's ROOT software. MODAR has been designed to handle time–energy data issued by MCNP simulations of neutron inspection devices using the associated particle technique. MODAR exploits ROOT's Graphical User Interface and functionalities to visualize and process MCNP simulation results in a fast and user-friendly way. MODAR allows to take into account the detection system time resolution (which is not possible with MCNP) as well as detectors energy response function and counting statistics in a straightforward way.Program summaryProgram title: MODARCatalogue identifier: AEGA_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGA_v1_0.htmlProgram obtainable from: CPC Program Library, Queen's University, Belfast, N. IrelandLicensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 155 373No. of bytes in distributed program, including test data, etc.: 14 815 461Distribution format: tar.gzProgramming language: C++Computer: Most Unix workstations and PCOperating system: Most Unix systems, Linux and windows, provided the ROOT package has been installed. Examples where tested under Suse Linux and Windows XP.RAM:   Depends on the size of the MCNP output file. The example presented in the article, which involves three two-dimensional 139×740139×740 bins histograms, allocates about 60 MB. These data are running under ROOT and include consumption by ROOT itself.Classification: 17.6External routines: ROOT version 5.24.00 (http://root.cern.ch/drupal/)Nature of problem: The output of an MCNP simulation is an ASCII file. The data processing is usually performed by copying and pasting the relevant parts of the ASCII file into Microsoft Excel. Such an approach is satisfactory when the quantity of data is small but is not efficient when the size of the simulated data is large, for example when time–energy correlations are studied in detail such as in problems involving the associated particle technique. In addition, since the finite time resolution of the simulated detector cannot be modeled with MCNP, systems in which time–energy correlation is crucial cannot be described in a satisfactory way. Finally, realistic particle energy deposit in detectors is calculated with MCNP in a two-step process involving type-5 then type-8 tallies. In the first step, the photon flux energy spectrum associated to a time region is selected and serves as a source energy distribution for the second step. Thus, several files must be manipulated before getting the result, which can be time consuming if one needs to study several time regions or different detectors performances. In the same way, modeling counting statistics obtained in a limited acquisition time requires several steps and can also be time consuming.Solution method: In order to overcome the previous limitations, the MODAR C++ code has been written to make use of CERN's ROOT data analysis software. MCNP output data are read from the MCNP output file with dedicated routines. Two-dimensional histograms are filled and can be handled efficiently within the ROOT framework. To keep a user friendly analysis tool, all processing and data display can be done by means of ROOT Graphical User Interface. Specific routines have been written to include detectors finite time resolution and energy response function as well as counting statistics in a straightforward way.Additional comments: The possibility of adding tallies has also been incorporated in MODAR in order to describe systems in which the signal from several detectors can be summed. Moreover, MODAR can be adapted to handle other problems involving two-dimensional data.Running time:   The CPU time needed to smear a two-dimensional histogram depends on the size of the histogram. In the presented example, the time–energy smearing of one of the 139×740139×740 two-dimensional histograms takes 3 minutes with a DELL computer equipped with INTEL Core 2.