AI Insight
This study examines how non-Gaussian line spread functions (LSFs) in spectrographs affect the accuracy of measuring stellar velocities and velocity dispersions. The author found that assuming a Gaussian LSF when the actual shape differs can introduce biases of up to 7 percent in velocity dispersion measurements and significant errors in higher-order kinematic moments, even at high velocity dispersions of 300 km/s. A new correction method was developed and made publicly available that reduces these biases to less than 1 percent.
Why it matters
Accurate stellar kinematics are essential for understanding galaxy dynamics and constructing reliable models of galaxy structure and dark matter distributions. This work provides a practical solution to improve the precision of kinematic measurements from integral field spectrographs like MUSE, which are widely used in modern astronomical surveys.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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 $R = lambda/Deltalambda$ with $Delta lambda$ 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 ($sigma_{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 $pm$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.