Computational Physics and Python Applications
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Abstract
Abstract
Nonlinear least-squares fitting of parametric line shapes is routine in the analysis of spectra, and the analyst often knows relationships between the parameters before the fit starts, such as a shared width or a fixed intensity ratio. SpectraFit-Core is an open-source curve-fitting library in which this knowledge is part of the model. A model is a graph of named components, and each relationship is an expression edge on that graph, within one spectrum or across several spectra fitted jointly in one call. On a measured Fe L2,3 energy-loss spectrum, four such relationships leave the fitted values unchanged and make the edge-step amplitude 28 times more precise. Because the model is a description rather than code, the same graph can be fitted by other implementations and their parameters compared name by name. The parameters and standard errors of the library are checked against certified values: on 22 of the 27 nonlinear-regression problems of the NIST Statistical Reference Datasets, the least accurately recovered parameter agrees to more than six significant figures, and the standard errors agree nearly as well except on Lanczos1. Kernels, analytic Jacobians, and solver iteration run in compiled Rust behind a typed Python interface, which also makes the library faster than lmfit, SciPy, and JAX on its benchmark. A companion repository regenerates the results from the published release. SpectraFit-Core is released under the MIT license, distributed on PyPI and archived on Zenodo.
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@article{Hahn2026SpectraFit,
title = {SpectraFit-Core: a Rust-cored nonlinear curve-fitting library validated against the NIST Statistical Reference Datasets},
author = {Anselm W. Hahn},
journal = {ChemRxiv},
year = {2026},
doi = {10.26434/chemrxiv.15010416/v2},
url = {https://doi.org/10.26434/chemrxiv.15010416/v2}
}FAQ
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