Abstract
refnx is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is browser based, through a Jupyter notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterized in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule etc.). The model and data are used to create an objective, which is used to calculate the residuals, log-likelihood and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple data sets and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. The software offers a choice of fitting approaches, including least-squares (global and gradient-based optimizers) and a Bayesian approach using a Markov-chain Monte Carlo algorithm to investigate the posterior distribution of the model parameters. The Bayesian approach is useful for examining parameter covariances, model selection and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in Jupyter notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses.
Highlights
The use of specular X-ray and neutron reflectometry for the morphological characterization of thin films in the approximate size range 10–5000 Ahas grown remarkably over the past few years (Wood & Clarke, 2017; Daillant & Gibaud, 2009)
These programs typically create a model of the interface, and either incrementally refine the model against the data using leastsquares methods or use Bayesian approaches (Sivia & Skilling, 2006; Kienzle et al, 2011; Hogg et al, 2010) to examine the posterior probability distribution of the parameters
We outline a new reflectometry analysis package, refnx, that helps to address the reproducibility issue for the reflectometry community
Summary
The use of specular X-ray and neutron reflectometry for the morphological characterization of thin films in the approximate size range 10–5000 Ahas grown remarkably over the past few years (Wood & Clarke, 2017; Daillant & Gibaud, 2009). We outline a new reflectometry analysis package, refnx (version number 0.1 is used in this paper; Nelson & Prescott, 2018b), that helps to address the reproducibility issue for the reflectometry community (we do not mean that other programs are irreproducible, rather that the information provided in journal articles is often lacking). It does this by creating a scripted analysis workflow that is readily deposited alongside the publication, such as we have done with this paper (see the supporting information). Algebraic relationships between Parameter objects can be applied to permit more sophisticated constraints that can cross between Component objects (e.g. the sum of the thicknesses of several layers is known to some uncertainty)
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