Nuclear Science, Beta Decay, and Machine Learning

S.N. Liddick [1, 2]

[1] Department of Chemistry, Michigan State University, USA
[2] National Superconducting Cyclotron Laboratory, Michigan State University, USA

Jun 24, 2020


This section of material is intended to provide a simple, high-level background necessary to understand the purpose of the experimental measurement and the goals for improvement in the data analysis. The context will provide guidance on the appropriate look of extract experimental spectra from the application of machine learning techniques.

  1. Overview
  2. Nuclear Science
    1. Introduction to Shell Structure
    2. Isomers
    3. E0 transitions
  3. Isotope Production
    1. Production, delivery and characterization of rare isotopes
  4. Decay Spectroscopy
    1. Introduction
    2. Beta decay
    3. Gamma-ray decay and internal conversion electron emission
    4. Integrating the decays
  5. Detector Operation
    1. Scintillator Operation
    2. Practical Implementation
  6. Introduction to Data
    1. 1D time dimension
    2. 2D energy distribution
© 2020, S.N. Liddick. Released under CC Attribution-NonCommercial 4.0 license