Predicting (n,3n) nuclear reaction cross-sections using XGBoost and Leave-One-Out Cross-Validation

Accurately predicting nuclear reaction cross-sections is crucial for advancing various fields, including nuclear medicine, energy production, and materials science. This study aims to address the challenges associated with predicting (n ,3n) nuclear reaction cross-sections by developing a robust mac...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Applied radiation and isotopes Jg. 219; S. 111714
Hauptverfasser:	Ali Üncü, Yiğit, Danışman, Taner, Özdoğan, Hasan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 01.05.2025
Schlagworte:	(n,3n) reactions Cross-section Leave-One-Out Cross-Validation Machine learning TALYS 1.95 TENDL XGBoost TENDL XGBoost Cross-section Machine learning TALYS 1.95 Leave-One-Out Cross-Validation (n,3n) reactions
ISSN:	0969-8043, 1872-9800, 1872-9800
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!