Nuclear physicists seek an accurate description of the properties of atomic nuclei, collisions between nuclei, and extreme environments such as the first few seconds of our universe or the interior of a neutron star. These situations involve many particles interacting through complex forces. They’re each described by a number of different models: these typically do a good job of explaining the results of experiments that have already occurred. The models don’t do as well predicting what will happen in future experiments or in environments that are inaccessible here on Earth.

The Bayesian Analysis of Nuclear Dynamics (BAND) Framework will use advanced statistical methods to produce forecasts for as-yet-unexplored situations that combine nuclear-physics models in an optimal way. These will be more reliable than the predictions of any individual model. BAND’s forefront computer codes will be widely available and will facilitate the design of nuclear-physics experiments that can deliver the largest gain in understanding. The adoption of BAND’s tools in other sciences dealing with “model uncertainty” could spur broad scientific innovation. Undergraduate and graduate students working on BAND will gain a broad range of technical skills in data science, machine learning, nuclear physics, and high-performance computing.

News

September 24, 2020

Registration for Virtual ISNET 8 and for the First Annual BAND Camp is now open. Information and registration can be found here. The website also lists confirmed speakers for both events.

September 17, 2020

Opening for Post-doctoral Research Associate in BAND collaboration at Ohio University (see online ad to apply). Review begins November 24, 2020.

August 26, 2020

Paper on Efficient emulators for scattering using eigenvector continuation is online in Physics Letters B 809, 135719 (2020) [also on arXiv].

July 1, 2020

The BAND Framework project officially begins! Press releases from Ohio University, The Ohio State University, and Michigan State University.

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