Analysis of counting data: Development of the SATLAS Python package

For the analysis of low-statistics counting experiments, a traditional nonlinear least squares minimization routine may not always provide correct parameter and uncertainty estimates due to the assumptions inherent in the algorithm(s). In response to this, a user-friendly Python package (SATLAS) was written to provide an easy interface between the data and a variety of minimization algorithms which are suited for analyzing low, as well as high, statistics data. The advantage of this package is that it allows the user to define their own model function and then compare different minimization routines to determine the optimal parameter values and their respective (correlated) errors. Experimental validation of the different approaches in the package is done through analysis of hyperfine structure data of Fr-203 gathered by the CRIS experiment at ISOLDE, CERN. Source code: https://github.com/wouterginsIsatlasi Documentation: https://woutergins.github.io/satlas/ Program summary Program Title: SATLAS Program Files doi: http://dx.doLorg/10.17632/3hr8f5nkhb.1 Licensing provisions: MIT Programming language: Python External routines/libraries: NumPy, SciPy, LMFIT, Pandas, NumDiffTools Nature of problem: Fitting data from a counting experiment to extract parameter information. Solution method: Supply a modular library with fitting routines using pre-implemented goodness-of-fit statistics for counting data under different circumstances. (C) 2017 The Authors. Published by Elsevier B.V.