A. M. Silva, K. Al Moulla
Abstract
Context. The radial velocity (RV) method is a widely used technique to infer planetary masses and orbital parameters. One of the most widely used methods to compute RVs is based on the alignment of a high signal-to-noise ratio (S/N), data-driven, stellar model with individual observations, commonly referred to as template matching. Typically, the alignment is performed through an χ2 minimization, but some approaches rely on the derivative of the stellar template to do so, which is often the case in line-by-line methods. Aims. In this paper we aim to explore the limitations of derivative-based methods for RV extraction in the case of using low S/N stellar models. Methods. We use simulated Gaussian profiles to investigate the effect of computing a numerical derivative of the stellar template, in comparison with the usage of an analytical profile. The impact on RV and associated uncertainty is then analysed as a function of the S/N of the line. Then, using real observations we compare the residuals between a derivative-based RV extraction and a classical template-matching implementation, as a function of the S/N of the stellar template. Results. We find that on simulated Gaussian profiles the use of the numerical approach leads to an RV residual at the level of metre per second, at an S/N regime per pixel of 100. An increase in the S/N leads to a decrease in this impact, falling below the current noise-floor of state-of-the-art spectrographs at S/N>1000 for a single spectral line. The inclusion of multiple spectral lines in the simulations leads to an overall decrease in contamination, across all S/N regimes. The application to a real dataset presents a decrease in the RV impact with the increase of the template's S/N, although it still presents a 12.5 cm s−1 difference when including 78 observations in the stellar template.
Keywords
methods: data analysis / methods: numerical / methods: observational / techniques: radial velocities
Astronomy & Astrophysics
Volume 712, Article Number A62, Number of pages 7
2026 August
