# “Full Model” Nuclear Data and Covariance Evaluation Process Using TALYS, Total Monte Carlo and Backward-forward Monte Carlo

## Abstract

The “Full model” evaluation process, that is used in CEA DAM DIF to evaluate nuclear data in the continuum region, makes extended use of nuclear models implemented in the TALYS code to account for experimental data (both differential and integral) by varying the parameters of these models until a satisfactory description of these experimental data is reached. For the evaluation of the covariance data associated with this evaluated data, the Backward-forward Monte Carlo (BFMC) method was devised in such a way that it mirrors the process of the “Full model” evaluation method. When coupled with the Total Monte Carlo method via the T6 system developed by NRG Petten, the BFMC method allows to make use of integral experiments to constrain the distribution of model parameters, and hence the distribution of derived observables and their covariance matrix. Together, TALYS, TMC, BFMC, and T6, constitute a powerful integrated tool for nuclear data evaluation, that allows for evaluation of nuclear data and the associated covariance matrix, all at once, making good use of all the available experimental information to drive the distribution of the model parameters and the derived observables.

- Authors:

- Publication Date:

- OSTI Identifier:
- 22436783

- Resource Type:
- Journal Article

- Resource Relation:
- Journal Name: Nuclear Data Sheets; Journal Volume: 123; Conference: International workshop on nuclear data covariances, Santa Fe, NM (United States), 28 Apr - 1 May 2014; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; CEA; DATA COVARIANCES; DIFFERENTIAL CROSS SECTIONS; EVALUATION; INTEGRAL CROSS SECTIONS; MONTE CARLO METHOD; NUCLEAR DATA COLLECTIONS; NUCLEAR MODELS

### Citation Formats

```
Bauge, E., E-mail: eric.bauge@cea.fr.
```*“Full Model” Nuclear Data and Covariance Evaluation Process Using TALYS, Total Monte Carlo and Backward-forward Monte Carlo*. United States: N. p., 2015.
Web. doi:10.1016/J.NDS.2014.12.035.

```
Bauge, E., E-mail: eric.bauge@cea.fr.
```*“Full Model” Nuclear Data and Covariance Evaluation Process Using TALYS, Total Monte Carlo and Backward-forward Monte Carlo*. United States. doi:10.1016/J.NDS.2014.12.035.

```
Bauge, E., E-mail: eric.bauge@cea.fr. Thu .
"“Full Model” Nuclear Data and Covariance Evaluation Process Using TALYS, Total Monte Carlo and Backward-forward Monte Carlo". United States.
doi:10.1016/J.NDS.2014.12.035.
```

```
@article{osti_22436783,
```

title = {“Full Model” Nuclear Data and Covariance Evaluation Process Using TALYS, Total Monte Carlo and Backward-forward Monte Carlo},

author = {Bauge, E., E-mail: eric.bauge@cea.fr},

abstractNote = {The “Full model” evaluation process, that is used in CEA DAM DIF to evaluate nuclear data in the continuum region, makes extended use of nuclear models implemented in the TALYS code to account for experimental data (both differential and integral) by varying the parameters of these models until a satisfactory description of these experimental data is reached. For the evaluation of the covariance data associated with this evaluated data, the Backward-forward Monte Carlo (BFMC) method was devised in such a way that it mirrors the process of the “Full model” evaluation method. When coupled with the Total Monte Carlo method via the T6 system developed by NRG Petten, the BFMC method allows to make use of integral experiments to constrain the distribution of model parameters, and hence the distribution of derived observables and their covariance matrix. Together, TALYS, TMC, BFMC, and T6, constitute a powerful integrated tool for nuclear data evaluation, that allows for evaluation of nuclear data and the associated covariance matrix, all at once, making good use of all the available experimental information to drive the distribution of the model parameters and the derived observables.},

doi = {10.1016/J.NDS.2014.12.035},

journal = {Nuclear Data Sheets},

number = ,

volume = 123,

place = {United States},

year = {Thu Jan 15 00:00:00 EST 2015},

month = {Thu Jan 15 00:00:00 EST 2015}

}