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Ubh Elementinin E4, M4, E5 ve M5 Geçişleri İçin Dönüşüm Katsayılarının Yapay Sinir Ağları Modeli ile Tahmin Edilmesi

Predictions of Conversion Coefficients for E4, M4, E5 and M5 transitions in Ubh element by Using Artificial Neural Network Model

Journal Name:

  • Cumhuriyet Üniversitesi Fen Fakültesi Fen Bilimleri Dergisi

Publication Year:

  • 2014

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
The primary decay modes for superheavy nuclei are alpha emission and spontaneous fission. Another decay mode is conversion electron emission which competes with the gamma-ray emission. Internal conversion coefficients, which are defined by the ratio of the number of emitted electron to the number of gamma-rays, are important for experimental and theoretical nuclear structure studies. These coefficients can be calculated by many experimental and theoretical ways. Furthermore, there are many computer code for calculations of these coefficients. In this work, the conversion coefficients belonging to the super heavy Unbihexium (Ubh) whose atomic number is 126 were calculated for E4 and E5 (electric), M4 and M5 (magnetic) multipole orders in the transition in low energy range. For the calculations, a mathematical model artificial neural network method was used.
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Abstract (Original Language): 
Süperağır çekirdekler için öncelikli bozunum modları, alfa yayınlanması ve kendiliğinden fisyondur. Diğer bir önemli mod ise, gama yayınlanması ile yarışan, dönüşüm elektron yayınlanmasıdır. Dönüşüm elektronlarının gama ışınlarına oranı olarak bilinen iç dönüşüm katsayıları, teorik ve deneysel nükleer yapı çalışmalarında önemlidir. Bu katsayılar deneysel ve teorik yollarla hesaplanabilmektedir. Ayrıca, bu katsayıların hesaplanması için geliştirilmiş pek çok bilgisayar programı da mevcuttur. Bu çalışmada, Z=126 atom numaralı Unbihexium (Ubh) süperağır çekirdeğine ait dönüşüm katsayıları, düşük geçiş enerji aralığında, E4 ve E5 (elektrik), M4 ve M5 (manyetik) geçişleri için, bir matematiksel model olan yapay sinir ağları metodu kullanılarak elde edilmiştir.
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