Measurement of 100Mo (n, 2n) 99Mo reaction cross section and covariance analysis using extended unscented transformation technique at the incident neutron energy of 13.9 MeV

In this paper, the measurement and covariance analysis of the cross section of 100Mo (n, 2n) 99Mo reaction, with the 197Au (n, 2n)196 Au reaction being used as the monitor, at the incident neutron energy of 13.9 MeV is reported. The 3H (d, n) 4He nuclear reaction is used as the neutron source. The experiment was performed at the Purnima neutron facility, BARC. The method of activation with off-line -ray spectrometry is used. The covariance analysis of the 100Mo (n, 2n) 99Mo reaction is also performed, for the first time, using the extended unscented transformation (EUT) technique1, which is an extension of unscented transformation (UT) technique2, for the determination of partial uncertainties arising due to attributes in combination with the micro-correlation technique of Geraldo and Smith3. The present results obtained for 100Mo (n, 2n) 99Mo reaction cross section are found to be in good agreement with EXFOR data and the theoretically calculated value using the TALYS 1. 8 code. Comparisons with the data in the available basic evaluated nuclear data libraries, such as ENDF/B-VIII.0, JEFF-3.3, JENDL-4.0, ROSFOND-2010, CENDL-3.1 and TENDL 2017 are also presented and discussed

Measurement of 100Mo (n, 2n) 99Mo reaction cross section and covariance analysis using extended unscented transformation technique at the incident neutron energy of 13.9 MeV

351-357In this paper, the measurement and covariance analysis of the cross section of 100Mo (n, 2n) 99Mo reaction, with the 197Au (n, 2n)196 Au reaction being used as the monitor, at the incident neutron energy of 13.9 MeV is reported. The 3H (d, n) 4He nuclear reaction is used as the neutron source. The experiment was performed at the Purnima neutron facility, BARC. The method of activation with off-line -ray spectrometry is used. The covariance analysis of the 100Mo (n, 2n) 99Mo reaction is also performed, for the first time, using the extended unscented transformation (EUT) technique1, which is an extension of unscented transformation (UT) technique2, for the determination of partial uncertainties arising due to attributes in combination with the micro-correlation technique of Geraldo and Smith3. The present results obtained for 100Mo (n, 2n) 99Mo reaction cross section are found to be in good agreement with EXFOR data and the theoretically calculated value using the TALYS 1. 8 code. Comparisons with the data in the available basic evaluated nuclear data libraries, such as ENDF/B-VIII.0, JEFF-3.3, JENDL-4.0, ROSFOND-2010, CENDL-3.1 and TENDL 2017 are also presented and discussed

Regression analysis of experimental reaction cross-section data of

Pre-processing of neutron reaction cross-section is essential in the nuclear data evaluation. This work aims to pre-process experimental cross-section data of 241 Am (n, 2n) 240 Am neutron reaction. Pre-processing of the experimental data includes re-normalization, removal of the outliers, integrating multiple cross-section values at single energy to single cross-section value, and regression on the cleaned experimental data. To remove outliers from the data, standardized residual and studentized residual have been used. For integration of multiple cross-section values to single cross-section value, the weighted average method has been used. Regression on the cleaned experimental data has been accomplished using the Gaussian Process Regression (GPR) and Polynomial Regression (PR), and the performance of both regression methods has been studied using statistical indices such as the determination of coefficient (R2) and the sum of the square of residual (SSres)

Application of Machine Learning algorithms for experimental data processing and estimation of

In this paper, Machine learning techniques have been employed for preparation and estimation of 96 Mo (n, p) 96Nb reaction data. The experimental data of 96 Mo (n, p) 96Nb reaction available in the EXFOR database was retrieved, analyzed and processed using renormalization and data cleaning techniques. Estimation of the renormalized experimental data with outlier and without outlier, over the entire neutron energy range, was then performed using machine learning regression algorithms of Ordinary Least square, Ridge, Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Regressor. The results obtained were then compared and it was observed that the data preparation plays a significant role in data quality

Measurement of Neutron Induced Reaction Cross Sections of Palladium Isotopes at the Neutron Energy of 14.54 ± 0.24 MeV with Covariance Analysis

The 110Pd(n,2n)109Pd, 102Pd(n,2n)101Pd, 105Pd(n,p)105Rh and 106Pd(n,p)106mRh reaction cross sections have been measured with respect to the 197Au(n,2n)196Au monitor reaction at the neutron energy of 14.54 ± 0.24 MeV by using the method of activation and off-line γ-ray spectrometry. The mono-energetic neutron beam was generated from the D–T reaction. The uncertainties in the various basic nuclear parameters in the measured reactions and their correlations were estimated by using covariance analysis. The present data were compared with the EXFOR based literature data, evaluated data of various libraries available in national nuclear data center and with the calculated values from TALYS-1.9 code