Application of Kalman filtering technique for evaluation of neutron cross section data of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4041" altimg="si1.svg"><mml:msup><mml:mrow/><mml:mrow><mml:mn>100</mml:mn></mml:mrow></mml:msup></mml:math>Mo (n, 2n) <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4049" altimg="si2.svg"><mml:msup><mml:mrow/><mml:mrow><mml:mn>99</mml:mn></mml:mrow></mml:msup></mml:math>Mo reaction

Ram, Sangeetha Prasanna; Nair, Jayakrishnan; Suryanarayana, S. V.; Ganesan, S.

doi:10.1016/j.nima.2021.165850

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…Initially, hyper parameters σ 2 f and λ had been calculated by maximizing the log marginal function [17,18] which resulted in the problem of overfitting. Therefore, to avoid this, λ has been calculated by considering the chi-square test (χ 2 ) [4]. The chi-square test (χ 2 ) is minimum for λ ranging from 0.27 to 0.3 whereas χ 2 is maximum for λ at 0.19001 (calculated by maximizing log marginal function).…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

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Regression analysis of experimental reaction cross-section data of ²⁴¹Am(n, 2n)240Am

Phatak¹,

Nair²,

Ram³

et al. 2023

EPJ Web of Conf.

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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).

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Section: Gaussian Process Regressionmentioning

confidence: 99%

“…In [3] two regression methods such as logistic regression and random forest regression methods have been used to highlight measurement features that are common among many of the outlier data points. In [4], experimental cross-section data of 100 Mo (n,2n) 99 Mo have regressed using Ridge, Lasso regression methods.…”

Section: Introductionmentioning

confidence: 99%

Regression analysis of experimental reaction cross-section data of ²⁴¹Am(n, 2n)240Am

Phatak¹,

Nair²,

Ram³

et al. 2023

EPJ Web of Conf.

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show abstract

“…3 [7]. The renormalized cross-section values are calculated as per the procedure given in [8]. In this study, two attributes, namely the half-life of sample and monitor cross section, have been chosen and updated to determine the renormalized cross section value as these were the two attributes provided by the experimenters in their research papers.…”

Section: Renormalizationmentioning

confidence: 99%

Application of Machine Learning algorithms for experimental data processing and estimation of ⁹⁶Mo(n, p)⁹⁶Nb reaction cross section

Ram¹,

Nair²,

Singh³

et al. 2023

EPJ Web of Conf.

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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.

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Evaluation of Neutron Cross-Section Data of $$ ^{241} $$Am(n,2n)$$ ^{240} $$Am Reaction Using Gaussian Process Concept

Phatak,

Nair,

Ram

et al. 2023

Algorithms for Intelligent Systems

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Application of Kalman filtering technique for evaluation of neutron cross section data of 100Mo (n, 2n) 99Mo reaction

Cited by 4 publications

References 30 publications

Regression analysis of experimental reaction cross-section data of ²⁴¹Am(n, 2n)240Am

Regression analysis of experimental reaction cross-section data of ²⁴¹Am(n, 2n)240Am

Application of Machine Learning algorithms for experimental data processing and estimation of ⁹⁶Mo(n, p)⁹⁶Nb reaction cross section

Evaluation of Neutron Cross-Section Data of $$ ^{241} $$Am(n,2n)$$ ^{240} $$Am Reaction Using Gaussian Process Concept

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Application of Kalman filtering technique for evaluation of neutron cross section data of 100Mo (n, 2n) 99Mo reaction

Cited by 4 publications

References 30 publications

Regression analysis of experimental reaction cross-section data of 241Am(n, 2n)240Am

Regression analysis of experimental reaction cross-section data of 241Am(n, 2n)240Am

Application of Machine Learning algorithms for experimental data processing and estimation of 96Mo(n, p)96Nb reaction cross section

Evaluation of Neutron Cross-Section Data of $$ ^{241} $$Am(n,2n)$$ ^{240} $$Am Reaction Using Gaussian Process Concept

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Product

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About

Regression analysis of experimental reaction cross-section data of ²⁴¹Am(n, 2n)240Am

Regression analysis of experimental reaction cross-section data of ²⁴¹Am(n, 2n)240Am

Application of Machine Learning algorithms for experimental data processing and estimation of ⁹⁶Mo(n, p)⁹⁶Nb reaction cross section