15th International Conference on Nuclear Data for Science and Technology (ND2022)

→ US/Pacific
Virtual meeting

Virtual meeting
C.M. Mattoon (LLNL), I. Thompson (Lawrence Livermore National Laboratory), J. Escher (LLNL), R. Vogt (Lawrence Livermore National Laboratory)
Description

ATTENTION: The ND2022 site has moved to https://indico.frib.msu.edu/e/nd2022.  The BNL site is no longer maintained!

 

ND2022 is the latest in a series of conferences held every three years since 1978, most recently in Bruges, Belgium (ND2016) and Beijing, China (ND2019). This conference brings together international experts involved in generating and using nuclear data for a week of presentations and in-depth discussion.

ND2022 is being organized by Lawrence Livermore National Laboratory. The conference was planned for July 24-29, 2022 in Sacramento, California, but due to ongoing uncertainty related to the Covid19 pandemic it has been converted to a virtual event. More information about the event timeline will be coming soon.

Thanks all who submitted abstracts!  Abstract submission has closed, but registration for ND2022 is still open! We invite everyone interested in attending to complete the registration. Authors will be notified of abstract acceptance in early 2022.

Conference topics include:

  • Nuclear reaction measurements,
  • Nuclear mass, structure and decay measurements,
  • Theoretical nuclear physics,
  • Special focus on fission including theory, measurements and modeling,
  • Development of new experimental capabilities and facilities,
  • Generating, improving and validating evaluated nuclear data libraries,
  • Processing nuclear data to prepare evaluated libraries for use in application codes,
  • Improving data formats, tools and quality assurance,
  • Uncertainty quantification and covariances,
  • Applying machine learning to the nuclear data pipeline, and
  • Applications of nuclear data to fields ranging from reactor design to medical isotope production to nuclear non-proliferation to space exploration and stellar nucleosynthesis.
ND2022_first_circular.pdf
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