Check out the tools and examples in BAND Framework v0.2! And stay tuned for additional capabilities in v0.3!
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.
Edgard Bonilla, Kyle Godbey, and BAND post-doc Pablo Giuliani are co-authors on two recent publications that employ Reduced Basis Methods (RBM) to speed up the calibration and evaluation of density functionals. In Training and projecting: a reduced basis method for many-body physics they and collaborator Dean Lee lay out the foundation of the method and demonstrate its potential in some illustrative examples. In Bayes goes fast: Uncertainty quantification for a covariant energy density functional emulated by the reduced basis method Bonilla, Godbey, and Giuliani teamed up with BAND member Frederi Viens and Jorge Piekarewicz to calibrate a relativistic energy density functional using a fraction of the computational resources that would have been needed without RBM.December 5, 2022
Congratulations to BAND member Stefan Wild! Stefan took up a new position as division director of Lawrence Berkeley National Laboratory’s Applied Mathematics and Computational Research Division on December 1.November 19, 2022
BAND member Witek Nazarewicz and his 17 co-authors have just published a Reviews of Modern Physics Colloquium on Machine Learning in Nuclear Physics. You can also read a nice summary of their paper in this phys.org piece.November 5, 2022
Five BAND members–co-PI, Özge Sürer, post-docs Pablo Giuliani and Daniel Odell, graduate student Dan Liyanage, and BAND leader Daniel Phillips–gave presentations at the recently concluded 2022 Fall Meeting of the Division of Nuclear Physics of the American Physical Society. You can check out what they all said at our Presentations page.October 24, 2022
The paper Interpolating between small- and large-g expansions using Bayesian model mixing written by BAND members Alexandra Semposki, Dick Furnstahl, and Daniel Phillips has been published in Phys. Rev. C. It tests BAND's linear, bivariate, and multivariate model mixing approaches on a toy model and paves the way for applications to crucial nuclear physics problems in the very near future!October 14, 2022
BAND has released v0.2 of our software framework! This release includes two "BAND tools" intended to facilitate Bayesian analyses: surmise for model emulation & calibration and SaMBA for model mixing. It also includes three "BAND examples" where we apply these tools and methods to forefront Nuclear Physics problems: BFRESCOX for coupled-channel analyses of nuclear reactions, BRICK for R-matrix calculations, and a tutorial (QGP_Bayes) on the use of JETSCAPE_SIMS tools to analyze the quark-glion plasma. Pull the repo to get all of them, or pull some of it to get particular pieces you want, but please go ahead and play and tell us what you think when you do!