The Impact of Non-Gaussian Line Spread Functions on Stellar Kinematic Recovery: Consequences for Dynamical Models
Abstract
The line spread function (LSF) of a spectrograph encodes the inherent broadening of a single spectral line. It is typically reported as a single number, the resolving power R = λ/Δλ with Δλ the FWHM of the LSF. In standard pipelines for extracting stellar kinematics the LSF is assumed to be a wavelength dependent Gaussian. However, detailed LSF measurements from real integral field spectrographs reveal a variety of shapes, some close to Gaussian, others with large wings or that appear boxy. I have studied the impact that these non-Gaussian LSF profiles have on the recovery of the stellar kinematics of a mock spectrum based on MUSE ($σ_{\rm inst} = 51 $ km s−1) and find that even in the high dispersion case of 300 km s−1, there is up to a 7 percent bias in the dispersion due to non-Gaussian LSF profiles. Additionally, higher order Gauss-Hermite moments h3 and h4 can be biased by up to ±0.1. To resolve this bias, I developed a method to match the LSF of the template spectra to the LSF of a target spectrum when the LSF of either one or both is non-Gaussian and show that it can reduce bias in the dispersion to less than a percent down to the instrumental resolution. A Python implementation of this method has been made publicly available.
Versions
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BibTeX
@misc{simon2026impact,
title = {{The Impact of Non-Gaussian Line Spread Functions on Stellar Kinematic Recovery: Consequences for Dynamical Models}},
author = {David A. Simon},
howpublished = {arXiv},
year = {2026},
month = aug,
doi = {10.48550/arxiv.2606.30129},
eprint = {2606.30129},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.30129},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - The Impact of Non-Gaussian Line Spread Functions on Stellar Kinematic Recovery: Consequences for Dynamical Models
AU - David A. Simon
PY - 2026
DA - 2026/08/27
PB - arXiv
DO - 10.48550/arxiv.2606.30129
UR - https://arxiv.org/abs/2606.30129
AB - The line spread function (LSF) of a spectrograph encodes the inherent broadening of a single spectral line. It is typically reported as a single number, the resolving power R = λ/Δλ with Δλ the FWHM of the LSF. In standard pipelines for extracting stellar kinematics the LSF is assumed to be a wavelength dependent Gaussian. However, detailed LSF measurements from real integral field spectrographs reveal a variety of shapes, some close to Gaussian, others with large wings or that appear boxy. I have studied the impact that these non-Gaussian LSF profiles have on the recovery of the stellar kinematics of a mock spectrum based on MUSE ($σ_{\rm inst} = 51 $ km s^−1) and find that even in the high dispersion case of 300 km s^−1, there is up to a 7 percent bias in the dispersion due to non-Gaussian LSF profiles. Additionally, higher order Gauss-Hermite moments h_3 and h_4 can be biased by up to ±0.1. To resolve this bias, I developed a method to match the LSF of the template spectra to the LSF of a target spectrum when the LSF of either one or both is non-Gaussian and show that it can reduce bias in the dispersion to less than a percent down to the instrumental resolution. A Python implementation of this method has been made publicly available.
ER -