MCNP output data analysis with ROOT (MODAR)

Abstract

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. New version program summary Program title: MODAR Catalogue identifier: AEGA_v1_1 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEGA_v1_1.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 150 927 No. of bytes in distributed program, including test data, etc.: 4 981 633 Distribution format: tar.gz Programming language: C++ Computer: Most Unix workstations and PCs Operating 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 × 740 bins histograms, allocates about 60 MB. These data are running under ROOT and include consumption by ROOT itself. Classification: 17.6 Catalogue identifier of previous version: AEGA_v1_0 Journal reference of previous version: Comput. Phys. Comm. 181 (2010) 1161 External routines: ROOT version 5.24.00 ( http://root.cern.ch/drupal/) Does the new version supersede the previous version?: Yes Nature of problem: The output of a 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. Reasons for new version: For applications involving the Associate Particle Technique, a large number of gamma rays are produced by the fast neutrons interactions. To study the energy spectra, it is useful to identify the gamma-ray energy peaks in a straightforward way. Therefore, the possibility to show gamma rays corresponding to specific reactions has been added in MODAR. Summary of revisions: It is possible to use a gamma ray database to better identify in the energy spectra gamma ray peaks with their first and second escapes. Histograms can be scaled by the number of source particle to evaluate the number of counts that is expected without statistical uncertainties. 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 × 740 two dimensional histograms takes 3 minutes with a DELL computer equipped with INTEL Core 2.