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arXiv · 2511.03663

3D Full Spectrum Fitting: Algorithm Comparison

Prashin Jethwa·Simon Hubmer·Ronny Ramlau·Glenn Van de Ven
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Abstract

Full spectrum fitting is the prevailing method for extracting stellar kinematic and population measurements from 1D galaxy spectra. 3D methods refer to analysis of Integral Field Spectroscopy (IFS) data where spatial and spectral dimensions are modelled simultaneously. While several 3D methods exist for modelling gas structures there has been less investigation into the more computationally demanding problem of 3D full spectrum fitting for stellar recoveries. This work introduces and compares two algorithms for this task: the Projected Nesterov Kaczmarz Reconstruction method (PNKR) and a version of the Bayes-LOSVD software which has been modified to account for spatial correlations. We aim to understand strengths and weaknesses of both algorithms and assess the impact of 3D methods for stellar inferences. We apply both recovery algorithms to a mock IFS data over a signal-to-noise ratio (SNR) range from 20-200 and evaluate the quality of the recoveries compared to the known ground truth. Accounting for spatial correlations in Bayes-LOSVD significantly improved the accuracy and precision of kinematic recoveries. 3D modelling with PNKR did not provide any significant improvement over 1D fits however, for SNR>40, PNKR did recover the most accurate kinematics overall. Additionally, by modelling the joint distribution over kinematics and populations, PNKR could successfully infer trends between these quantities e.g. inferring local metallicity-velocity trends, albeit with a significant bias on the absolute metallicity. Having demonstrated advantages of (i) 3D modelling with Bayes-LOSVD, and (ii) joint kinematic-population analyses with PNKR, we conclude that both methodological advances will prove useful for detecting and characterising stellar structures from IFS data.

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Prashin Jethwa, Simon Hubmer, Ronny Ramlau, Glenn Van de Ven. 2025-11-05. 3D Full Spectrum Fitting: Algorithm Comparison. https://doi.org/10.1051/0004-6361%2F202557989

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