%0 Journal Article %1 shaaban:2001:GPEM %A Shaaban, N. %A Hasegawa, S. %A Suzuki, A. %A Takahashi, H. %D 2001 %J Genetic Programming and Evolvable Machines %K EGS4 algorithms, charge detectors, energy genetic loss, semiconductor software spectrum, %N 3 %P 289--299 %R doi:10.1023/A:1011905527157 %T The Use of Genetic Algorithms for the Improvement of Energy Characteristics of CdZnTe Semiconductor Detectors %V 2 %X A new charge loss correction method using genetic algorithms (GA) has been proposed to improve gamma ray energy spectrum characteristics of CdZnTe detectors. The correction method is based on the analysis of signal waveform shapes taking into account the contribution of multiple interaction processes to pulse shape generation. A GA recognizes the charge deposition places for each signal and provides the related corrective factors of the pulse heights; the corrected pulse height spectrum was obtained by summing up the corrected pulse heights for each signal. An enhanced energy spectrum characteristic was obtained after the correction process for 662 keV photons. This method is simple and useful for pulse shape analysis; the results demonstrate promise for the successful application of GAs for digital signal processing data analysis.

@article{shaaban:2001:GPEM, abstract = {A new charge loss correction method using genetic algorithms (GA) has been proposed to improve gamma ray energy spectrum characteristics of CdZnTe detectors. The correction method is based on the analysis of signal waveform shapes taking into account the contribution of multiple interaction processes to pulse shape generation. A GA recognizes the charge deposition places for each signal and provides the related corrective factors of the pulse heights; the corrected pulse height spectrum was obtained by summing up the corrected pulse heights for each signal. An enhanced energy spectrum characteristic was obtained after the correction process for 662 keV photons. This method is simple and useful for pulse shape analysis; the results demonstrate promise for the successful application of GAs for digital signal processing data analysis.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Shaaban, N. and Hasegawa, S. and Suzuki, A. and Takahashi, H.}, biburl = {https://www.bibsonomy.org/bibtex/2021b98d7d1c7fcb57531b157d73bfa6d/brazovayeye}, doi = {doi:10.1023/A:1011905527157}, email = {noha@qs.t.u-tokyo.ac.jp}, interhash = {e23c1a42183db4313b72d86843806362}, intrahash = {021b98d7d1c7fcb57531b157d73bfa6d}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {EGS4 algorithms, charge detectors, energy genetic loss, semiconductor software spectrum,}, month = {September}, notes = {Article ID: 357596}, number = 3, pages = {289--299}, timestamp = {2008-06-19T17:51:30.000+0200}, title = {The Use of Genetic Algorithms for the Improvement of Energy Characteristics of {CdZnTe} Semiconductor Detectors}, volume = 2, year = 2001 }