Bayesian Methods in Nuclear Physics

A workshop on Bayesian Methods in Nuclar Physics was held at the Institute for Nuclear Theory at the University of Washington in Seattle from June 13 to July 8, 2016. These pages continue the discussion initiated at this program. The workshop was the number 4 of the ISNET (Information and Statistics in Nuclear Experiments and Theory) family of meetings. Talks given at ISNET-3 and ISNET-5 are also listed here.

Goal: For statisticians and nuclear practitioners to jointly explore how Bayesian inference can enable progress on the frontiers of nuclear physics and open up new directions for the field.

Information about Participants

All participants in the program are encouraged to provide information about their research specialty, statistics connection, and up to five relevant references. Send your info to furnstahl.1 at osu.edu or awsteiner at utk.edu (or you can edit this page directly via github if you have joined the project).

Entries are in alphabetical order by last name.

Steffen A. Bass, Duke University, bass at phy.duke.edu
Nuclear Theory. Modeling and phenomenology of relativistic heavy-ion collisions. Model-to-data comparisons using Bayesian inference.
  • Constraining the initial state granularity with bulk observables in Au+Au collisions H. Petersen, C.E. Coleman-Smith, S.A. Bass and R.L. Wolpert, J. Phys. G38 045102 (2011)[arXiv:1012.4629]
  • Quantifying properties of hot and dense QCD matter through systematic model-to-data comparison, J.E. Bernhard, P.W. Marcy, C.E. Coleman-Smith, S. Huzurbazar, R.L. Wolpert and S.A. Bass, Phys. Rev. C91 054910 (2015) [arXiv:1502.00339].
  • Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium, J.E. Bernhard, J.S. Moreland, S.A. Bass, J. Liu and U.W. Heinz, [arXiv:1605.03954]
Jonah Bernhard, Duke University, jeb65 at phy.duke.edu
Computational nuclear physics; extracting properties of hot and dense QCD matter from relativistic heavy-ion collision data.
  • Quantifying properties of hot and dense QCD matter through systematic model-to-data comparison, J. E. Bernhard, P. W. Marcy, C. E. Coleman-Smith, S. Huzurbazar, R. L. Wolpert, S.A. Bass, Phys. Rev. C91 054910 (2015), [arXiv:1502.00339].
  • Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium, J. E. Bernhard, J. S. Moreland, S. A. Bass, J. Liu, U. W. Heinz, [arXiv:1605.03954].
Derek Bingham, Simon Fraser University, dbingham at stat.sfu.ca
Bayesian methods, computer experiments, design of experiments, uncertainty quantification.
  • Prediction and Computer Model calibration using outputs from multi-fidelity simulators J. Goh, D. Bingham, J.P Holloway, M.J. Grosskopf, C.C Kuranz, and E. Rutter, , Technometrics, 2013, 55(4), 501-512 •
  • Efficient emulators of computer experiments using compactly supported correlation functions, with an application to cosmology, • C.G. Kaufman, D. Bingham, S. Habib, K. Heitmann, and J.A. Frieman, , Annals of Applied Statistics, 2011, 2470-2492.
Roberto Capote, IAEA Nuclear Data Section, r.capotenoy at iaea.org
Nuclear Data modeling and nuclear data evaluation. Bayesian evaluation methods in nuclear data.
Dick Furnstahl, Ohio State University, furnstahl.1 at osu.edu
Low-energy nuclear physics theory; applying Bayesian methods to effective field theory, including parameter estimation and model selection.
Harald W. Griesshammer, George Washington University, hgrie at gwu.edu
Nuclear and particle theory: the Effective Field Theories describing one- and few-nucleon systems at low energies, emphasising theory consistency, data consistency and consistency between the two.
Michael Grosskopf, Simon Fraser University, mgrossko at sfu.ca
Bayesian methods, Calibration and emulation of computer models, Statistical learning, Uncertainty quantification
  • Prediction and Computer Model calibration using outputs from multi-fidelity simulators J. Goh, D. Bingham, J.P Holloway, M.J. Grosskopf, C.C Kuranz, and E. Rutter, , Technometrics, 2013, 55(4), 501-512
  • Calibrating a Large Computer Experiment Simulating Radiative Shock Hydrodynamics, Robert B. Gramacy, Derek Bingham, James Paul Holloway, Michael J. Grosskopf, Carolyn C. Kuranz, Erica Rutter, Matt Trantham, and R. Paul Drake, The annals of applied statistics, 2015, Vol.9(3), p.1141-1168
  • Conceptual design of a Rayleigh-Taylor experiment to study bubble merger in two dimensions on NIF, Malamud, G, Grosskopf, MJ, Drake, RP, High Energy Density Physics,Vol. 11, p. 17-25, DOI: 10.1016/j.hedp.2014.01.001
Dave Ireland, University of Glasgow, David.Ireland at glasgow.ac.uk
Experimental hadron physics, baryon spectroscopy. Bayesian methods for experimental data analysis. Use of information theory.
Natalie Klco, University of Washington, klcon at uw.edu
Low-energy nuclear physics theory; applying Bayesian methods to effective field theory, including parameter estimation and model selection.
Earl Lawrence, Statistician, Los Alamos National Laboratory, earl at lanl.gov
Bayesian methods, methods for computationally intensive models, applications of statistics to physics.
Witold Nazarewicz, Michigan State University, witek at frib.msu.edu
Nuclear structure and reactions theory; uncertainty quantification of nuclear models, including parameter estimation, model selection, and information content of experimental data assessment.
Denise Neudecker, Los Alamos National Laboratory, dneudecker at lanl.org
Uncertainty quantification of nuclear data. Bayesian evaluation methods in nuclear data.
Daniel Phillips, Ohio University, phillid1 at ohio.edu
Theory and phenomenology of few-nucleon systems, especially electromagnetic reactions; Universality in atomic & nuclear physics; Theory of reactions for nuclei near the dripline; Applications of Effective Field Theory and Bayesian Methods to these topics. 
Chong Qi, KTH Royal Institute of Technology, Stockholm, chongq at kth.se
Low energy nuclear structure theory. Structure and decay properties of intermediate-mass and heavy nuclei. Large-scale shell model configuration interaction calculations.
Nicolas Schunck, LLNL schunck1 at llnl.gov
Nuclear theory. Structure of heavy nuclei and nuclear fission. High-performance computing for nuclear density functional theory.
Donald L. Smith, Argonne National Laboratory (r), DonaldLarnedSmith at gmail.com
Nuclear physics experiments and nuclear data evaluation. Bayesian evaluation methods in nuclear data.
Ron Soltz, LLNL soltz1 at llnl.gov
Experimental Nuclear Physics and modeling Relativistic Heavy Ion Collisions. Interest in applying Bayesian methods to improve jet finding in high multiplicity backgrounds.
  • Constraining the initial temperature and shear viscosity in a hybrid hydrodynamic model of sqrt(s_NN)=200 GeV Au+Au collisions using pion spectra, elliptic flow, and femtoscopic radii, R.A. Soltz et al, Phys. Rev. C 87, 044901 (2013) [arXiv:1208.0897].
Andrew W. Steiner, UTK/ORNL, awsteiner at utk.edu,
Theoretical nuclear astrophysics, especially combining nuclear theory and nuclear data with astronomical observations using Bayesian inference. Also, open-source scientific computing.
Dario Vretenar, University of Zagreb, vretenar at phy.hr
Low-energy nuclear physics theory; nuclear structure models, nuclear energy density functionals, nuclear fission.
Sarah Wesolowski, Ohio State University, sarahcwesolowski at gmail.com
Low-energy nuclear physics theory; applying Bayesian methods to effective field theory, including parameter estimation and model selection.

Navigation

Questions for discussion

References

Participant information

Photos from the program

Statistics (Bayesian and other) humor

Workshop page at the INT

Organizers:
Nicolas Schunck (lead)
Dick Furnstahl
Dave Higdon
Andrew Steiner