Simulation and efficiency calibration of a high purity germanium detector by using monte carlo method

The aim of this thesis is to simulate a high purity germanium detector through Monte Carlo N Particles code for any given radiation source. In order to simulate the detector we entered the geometry of the source and detector as data into MCNP and the code gave spectrum of the source as output. We used Cs-137, Co-60, Eu-152 and Am-241 as sources of gama resources. After getting the output of MCNP we compared the simulation data with experimental data of detector. Comparison showed that simulation of the detector was successful. In simulating HpGe detector we created the geometry very close to the one in which experimental data was observed. The geometry includes detector, radioactive source inside lead (Pb) shielding. Inside the shielding there is air as first medium for radiation to encounter and then the parts of which detector is made. Geometry of lead shielding, air and the parts of the detector are entered into Monte Carlo code as input data. Monte Carlo code has data library of the interactions of radiation with matter. With this data library Monte Carlo code simulates the reactions happening like Compton scattering, electron-positron annihilations, bremsstrahlung effect, etc. After entering geometry data into Monte Carlo code we evaluated output data according to our need to simulate gama detector. Code listed energy-count data as output in two columns just like a multi-channel gama detector. Second objective of the thesis is to calculate the efficiency calibration of the given detector without using radioactive source. To do it we assumed that intensity values of gama resources were 1 (normalized) hence corresponding probability values calculated by MCNP equaled to efficiency values. First we took Cs-137 as a reference source and calculated its efficiency value with MCNP for a given distance of radiation source from detector. Its difference from the experimental efficiency value was calculated as a coefficient which was 12. After finding this coefficient we were able to calculate efficiency calibration for other sources by multiplying this coefficient with efficiency value calculated by MCNP. The efficiency values found with above mentioned method are inline with experimental efficiency values of the radiation sources as seen in the graphics in the thesis. This compatibility suggest that the energy calibration of any given detector can be calculated by MCNP without using a radition source for any given distance of radiation source from detector. Achieving this two objective provides excellent advantage in measuring gama spectroscopy. This advantage is based on the MCNP code that can measure gama spectroscopy without actual radioactive source. MCNP code is written by Fortran 77 language. To give the brief explanation of how MCNP code runs we must consider it in three parts. 1-Input Data, 2-Code body and 3-Output Data. Every kind of radiation source related geometry can be turned into input data for MCNP code. Source information like energy of source, the distance between source and simulated detector etc. are input datas to MCNP. Code body gets these informations and process through its libraries and outputs in what data it is asked to give. Output data can be anything that from particle flux to gaussian distribution of energy of radiation to probability of counted photons. In this way MCNP code can simulate any kind of radiation count case. Throughout thesis we examined and graphed MCNP output comparing with the experimental results of HpGe detector. In these graphics we showed both MCNP output and experimental data side by side. It was shown that simulation data was in great accord with experimental data. These accord suggests that when radioactive source is none existant we would be able to calculate the spectroscopy of radiation source. Only thing that needs to be known is detector characteristics which are specific to each detector. If detector characteristics are known these datas can be turned into geometry of the detector in question. So this geometry can be input to MCNP and energy count relationship can be calculated by MCNP as we did in this thesis. Above mentioned MCNP code is able to calculate any value about the radiation source within error limits. These limits are also specified in MCNP output. The checking if the results are within these limits is also performed. So the results are reliable datas after the checking is done. The important thing when making calculations is to establish geometry of the given radioactive source case correctly. To do this, it is recommended that the geometry would be as simple as possible at the beginning of simulation. After getting the correct output values more complex geometries can be input to MCNP code. In our case geometry was simple enough so we did not try to get it simpler. In simulating the detectors, geometry is simple enough, one source and one detector and the environment create the geometry. Sometimes the case could be as complex as it is hard to experiment. For case like this MCNP is important tool as it can simulate complex cases as well as simple cases. MCNP simulations is used many areas of nuclear reactions. Especially when there are big number of particles involving. Big particle numbers can be input to MCNP code as the only limit is computing power. So complex cases need more computing power in order to calculate needed values. If computing power is not enough number of particles should be decreased to limit the time it takes MCNP to calculate. Random number generator of MCNP is used to simulate events. MCNP is fairly unique and successful tool in simulating the nuclear reactions by making use of random numbers. It follows every particle throughout their life and holds particle histories for every reaction particle is involved. Each reaction is sampled to simulate them by MCNP. In these samplings Monte Carlo technique is used. Monte Carlo technique is simply a model that sample statistical processes with random numbers. Hence Monte Carlo name comes from the city with its famous casino. As It may seem simple to use, with this method, much complex events involving many particles problem can be solved without getting lost in complex numerical integrals. Therefore, with some simple equations MCNP can help find the solutions. MCNP has developed error checking methods as well as finding solution to the complex problems. Variance reduction is one of them and MCNP does its best to provide physicist with variance reduction tools. It was shown that MCNP code could be used in cases where experimental data was not available or hard to collect for example a reactor and its core is the source of radiation. So another studies can be done using MCNP code simulation wherever experimental difficulties exist.