Data Analysis for Atomic Shapes in Nuclear Science

  • Mehmet Cagri KaymakEmail author
  • Hasan Metin Aktulga
  • Ron Fox
  • Sean N. Liddick
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)


We consider the problem of detecting a unique experimental signature in time-series data recorded in nuclear physics experiments aimed at understanding the shape of atomic nuclei. The current method involves fitting each sample in the dataset to a given parameterized model function. However, this procedure is computationally expensive due to the nature of the nonlinear curve fitting problem. Since data is skewed towards non-unique signatures, we offer a way to filter out the majority of the uninteresting samples from the dataset by using machine learning methods. By doing so, we decrease the computational costs for detection of the unique experimental signatures in the time-series data. Also, we present a way to generate synthetic training data by estimating the distribution of the underlying parameters of the model function with Kernel Density Estimation. The new workflow that leverages machine learned classifiers trained on the synthetic data are shown to significantly outperform the current procedures used in actual datasets.


Machine learning Density estimation Nuclear physics 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mehmet Cagri Kaymak
    • 1
    Email author
  • Hasan Metin Aktulga
    • 1
  • Ron Fox
    • 2
  • Sean N. Liddick
    • 2
    • 3
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  2. 2.National Superconducting Cyclotron LaboratoryMichigan State UniversityEast LansingUSA
  3. 3.Department of ChemistryMichigan State UniversityEast LansingUSA

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