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Going Nuclear: Notes from the officially unofficial book tour
I work in the analytical labs at one of Europe’s oldest and largest nuclear sites: Sellafield, in northwestern England. I spend my days at the fume hood front, pipette in one hand and radiation probe in the other (and dosimeter pinned to my chest, of course). Outside the lab, I have a second job: I moonlight as a writer and public speaker. My new popular science book—Going Nuclear: How the Atom Will Save the World—came out last summer, and it feels like my life has been running at full power ever since.
Vladimir Sobes, Luiz Leal, Goran Arbanas, Benoit Forget
Nuclear Science and Engineering | Volume 183 | Number 3 | July 2016 | Pages 347-355
Technical Paper | doi.org/10.13182/NSE15-50
Articles are hosted by Taylor and Francis Online.
This project seeks to allow coupling of differential and integral data evaluation in a continuous-energy framework and to use the generalized linear least-squares (GLLS) methodology in the TSURFER module of the SCALE code package to update the parameters of a resolved resonance region evaluation. Recognizing that the GLLS methodology in TSURFER is identical to the mathematical description of a Bayesian update in SAMMY, the SAMINT code was created to use the mathematical machinery of SAMMY to update resolved resonance parameters based on integral data. Traditionally, SAMMY used differential experimental data to adjust nuclear data parameters. Integral experimental data, such as in the International Criticality Safety Benchmark Experiments Project, remain a tool for validation of completed nuclear data evaluations. SAMINT extracts information from integral benchmarks to aid the nuclear data evaluation process. Later, integral data can be used to resolve any remaining ambiguity between differential data sets, highlight troublesome energy regions, determine key nuclear data parameters for integral benchmark calculations, and improve the nuclear data covariance matrix evaluation. SAMINT is not intended to bias nuclear data toward specific integral experiments but should be used to supplement the evaluation of differential experimental data. Using GLLS ensures proper weight is given to the differential data.