Adaptive multi-line fitting for stable line-core intensity and Doppler velocity

TL;DR

The paper introduces LineFit, an adaptive multi-line fitting method for stable line-core intensity and Doppler velocity measurements in dense solar spectroscopic data, improving robustness in complex cases.

Contribution

It presents a novel, fully reproducible fitting approach that models spectral lines with adaptive, asymmetric Voigt profiles, outperforming traditional methods in challenging scenarios.

Findings

LineFit yields more accurate velocity and intensity time series in complex line profiles.

Benchmarking shows LineFit is most robust in split-core and asymmetric cases.

Supervised emulation accelerates LineFit processing by over three orders of magnitude.

Abstract

Next-generation solar spectrographs increasingly record dense wavelength windows in which tens to hundreds of spectral lines are sampled at each spatial location and time step. This expands the scope for multi-line, multi-height diagnostics of magnetohydrodynamic motions, but also raises a practical challenge: deriving stable line-core intensity and line-of-sight velocity time series when profiles evolve rapidly, become asymmetric, blend, or develop multi-lobed cores. Common fast estimators can perform well for simple, isolated absorption lines, yet can intermittently misidentify the core in crowded or morphologically complex cases. Even infrequent mis-tracking can leave step-like artefacts that redistribute power and bias spectral, phase, and coherence measures used in wave and dynamics analyses. We introduce LineFit, a fully reproducible adaptive multi-line fitting approach tailored to dense-window spectroscopy. LineFit models each line locally with bounded non-linear least-squares fits to a Voigt-family profile, including an asymmetric-Voigt option to accommodate unequal wing broadening, and incorporates close-pair ownership control together with conservative, per-line window adaptation and split-core-aware handling. Using a synthetic time series with unambiguous ground truth, we benchmark LineFit against four widely used fast baselines and assess both instantaneous centre errors and downstream time-series diagnostics. Several fast methods remain competitive for many lines, whereas LineFit is most robust in key stress cases involving intermittently split-core profiles and correspondingly yields power spectra that agree most closely with the truth. We also demonstrate a proof-of-principle that benchmarks hybrid acceleration of the LineFit software via supervised emulation, offering at least three orders-of-magnitude improvement in processing time.