SATLAS2: An update to the package for analysis of counting data

Abstract

SATLAS2 is a Python library that enables the user to fit counting data from laser spectroscopy experiments, in particular those that measure atomic hyperfine structures. In this analysis, the user can choose how the uncertainties are treated and can also opt to generate a random walk in order to present a fuller picture of the parameter space. The major upgrade compared to the previous version of SATLAS [1] is the different architecture of the codebase, which enabled a performance boost, with speed-up factors ranging from 20 to 300 times for various use cases. For backward compatibility, a translation layer between the two architectures is available, implementing only the core functionality of SATLAS.

New version program summary

Program Title: SATLAS2

CPC Library link to program files: https://doi.org/10.17632/3hr8f5nkhb.2

Developer's repository link: https://github.com/IKS-nm/satlas2

Licensing provisions: MIT

Programming language: Python

Journal reference of previous version: Computer Physics Communications, Volume 222, 2018, Pages 286-294, ISSN 0010-4655, https://doi.org/10.1016/j.cpc.2017.09.012

Does the new version supersede the previous version?: Yes

Reasons for the new version: Improved and more stable performance

Summary of revisions: The architecture of the SATLAS package has been completely changed. Instead of operating on Models, using SumModel and LinkedModel for various analysis options, SATLAS2 works with a central Fitter object to which Sources get assigned, which themselves get assigned Models. This structure improves the performance by streamlining the code and benefits from using a pass by reference approach for changing the parameters, rather than a pass by value. Most of the functionality is maintained, with a small interface for most basic things available so SATLAS2 can be used as a drop-in replacement.

Nature of problem: The analysis of specifically counting data has some special considerations compared to more common datasets with Gaussian-distributed uncertainties. Application of Bayesian inference through random walk exploration is useful for more accurate exploration of parameter space. The fitting of multiple models with shared parameters can be desirable for either fitting multiple datasets with the same parameters or imposing additional restrictions on the parameters.

Solution method: SATLAS2 implements the correct statistical costs for fitting of both counting data and Gaussian distributed uncertainties, and allows an easy implementation of custom cost functions.. Through the implementation of multiple Sources and Models, a natural extension of the fitting is made, so multiple models can be both summed together to fit to the same data and multiple fits can be linked together through parameter linking, using the underlying LMFIT [2] library. The ability to fit simultaneously increases the possibilities for extensive statistical analyses, and the interface through LMFIT with the emcee [3] library enables Bayesian exploration of the parameter space.