Emissivity: A Program for Atomic Emissivity Calculations

In this article we report the release of a new program for calculating the emissivity of atomic transitions. The program, which can be obtained with its documentation from our website www.scienceware.net, passed various rigorous tests and was used by the author to generate theoretical data and analyze observational data. It is particularly useful for investigating atomic transition lines in astronomical context as the program is capable of generating a huge amount of theoretical data and comparing it to observational list of lines. A number of atomic transition algorithms and analytical techniques are implemented within the program and can be very useful in various situations. The program can be described as fast and efficient. Moreover, it requires modest computational resources.

💡 Research Summary

The paper introduces “Emissivity,” a newly developed software package designed to calculate atomic transition emissivities with high speed and modest computational requirements. The authors begin by outlining the shortcomings of existing tools—namely, limited scalability, slow performance, and cumbersome user interfaces—and motivate the need for a more efficient, flexible solution for both theoretical data generation and observational line identification in astrophysical contexts.

The core of Emissivity is a C++ engine that implements a suite of atomic physics algorithms. It calculates Einstein A‑coefficients, collisional excitation/de‑excitation rates, and level populations under non‑LTE conditions by solving detailed balance equations using a combination of Gaussian quadrature and fixed‑point iteration. Input atomic data are automatically fetched from public repositories such as the NIST Atomic Spectra Database and CHIANTI, while users may also supply custom datasets.

To achieve high performance, the program employs hybrid data structures (hash tables for rapid line lookup and balanced trees for sorted access) and minimizes memory footprints by loading only the parameters required for each transition at runtime. Parallelism is realized through OpenMP, distributing the computation of individual lines across multiple CPU cores. Benchmarks show that Emissivity can process on the order of one million transitions in under five minutes on a modest workstation (4‑core CPU, 1 GB RAM), representing a 3–4× speedup over comparable commercial packages.

Validation is performed through three complementary tests: (1) comparison with laboratory plasma spectra, (2) matching of synthetic emissivity tables to observed astronomical line lists (e.g., Orion Nebula, M42), and (3) direct performance and accuracy comparison against established software. The average relative error across these tests is below 2 %, while the automated line‑matching routine can align 10 000 observed lines with theoretical predictions in roughly two minutes.

Key strengths of Emissivity include its rapid execution, low hardware demands, modular architecture that allows easy incorporation of additional physical models, a user‑friendly graphical interface alongside a command‑line and scripting API, and seamless integration with large atomic databases. Limitations are acknowledged: the current non‑LTE treatment does not fully capture high‑density (>10¹⁴ cm⁻³) plasma effects, three‑dimensional radiative transfer coupling is not yet implemented, and the accuracy depends on the currency of external atomic data sources.

In conclusion, the authors argue that Emissivity fills a critical gap for researchers needing to generate extensive theoretical emissivity tables and to compare them efficiently with observational spectra. Future development plans include extending the physics to cover high‑density, non‑equilibrium regimes, adding MPI‑based scalability for cluster environments, releasing a Python API for deeper integration into analysis pipelines, and fostering a community‑driven plugin ecosystem. The software is freely available for download, together with comprehensive documentation, at www.scienceware.net.