ELFO: A Python Package for Emission Line Fitting Optimization in Integral Field Spectroscopy Data

Integral field spectroscopy (IFS) provides spatially resolved spectra, enabling detailed studies that address the physical and kinematic properties of the interstellar medium. A critical step in analyzing IFS data is the decomposition of emission lines, where different velocity components are often modeled with Gaussian profiles. However, conventional fitting methods that treat each spectrum independently often yield spatial discontinuities in the fitting results. Here, we present Emission Line Fitting Optimization (ELFO), a Python package for IFS spectral fitting. ELFO uses the results of neighboring spectra to determine multiple initial guesses and selects the result that exhibits spatial smoothness. We tested ELFO on IFS data of two quasars obtained from the Multi-Unit Spectroscopic Explorer, where it successfully corrected anomalous fits, revealed previously unresolved substructures, and made large-scale kinematic structures more evident. With minor modifications, this method can also be easily adapted to other IFS data and different emission lines.

💡 Research Summary

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The paper introduces ELFO (Emission Line Fitting Optimization), a Python package designed to improve the fitting of emission‑line spectra in integral‑field spectroscopy (IFS) data by enforcing spatial smoothness. Traditional IFS analyses treat each spaxel’s spectrum independently, assigning fixed initial guesses for Gaussian components and selecting the fit with the lowest reduced χ². This approach often yields unphysical discontinuities in velocity and line‑width maps, especially in low‑signal‑to‑noise regions, because neighboring spaxels are physically correlated but the fitting algorithm ignores this information.

ELFO addresses the problem in three stages. First, it generates initial guesses for a given spaxel from the best (lowest χ²_red) solutions of its k nearest neighbours in a user‑defined direction (rows or columns). The fitting proceeds sequentially from a central row (or column) outward, so that each new spaxel benefits from the most recent, locally consistent parameter set. Second, the package runs the fitting multiple times using different traversal orders (e.g., row‑first then column‑first, and the reverse) to produce several candidate solutions for every spaxel. All fits use the same physical constraints: narrow‑line widths are bounded by the instrumental resolution and a maximum of 1200 km s⁻¹, doublet flux ratios (