Lab Home | Phone | Search
Center for Nonlinear Studies  Center for Nonlinear Studies
 Colloquia Archive 
 Postdoc Seminars Archive 
 Quantum Lunch 
 Quantum Lunch Archive 
 CMS Colloquia 
 Q-Mat Seminars 
 Q-Mat Seminars Archive 
 P/T Colloquia 
 Kac Lectures 
 Kac Fellows 
 Dist. Quant. Lecture 
 Ulam Scholar 
 CNLS Fellowship Application 
 Student Program 
 Past Visitors 
 History of CNLS 
 Maps, Directions 
 CNLS Office 
Thursday, December 19, 2019
2:00 PM - 3:00 PM
CNLS Conference Room (TA-3, Bldg 1690)

Postdoc Seminar

Learning Nuclear Masses with Mixture Density Networks

Amy Lovell

The mass of each nucleus is a basic quantity but gives rise to many other properties of nuclear structure and reactions, including binding energies, separation energies, and Q-values. Close to stability, masses can be measured extremely precisely through various experimental techniques. Farther away from stability, in regions of the nuclear chart necessary for many studies including for fission or astrophysics, masses must be calculated. There have been several recent studies using various statistical techniques to correct these mass models based on available experimental data and to provide quantified uncertainties that can be propagated to other quantities of interest. Here, we expand this type of calculation, using a rather novel machine learning technique, the Mixture Density Network, to directly predict the nuclear masses with uncertainties. We show preliminary calculation in the context of previous machine learning methods and then discuss future work.

Host: Tillmann Weisser