ARVE: Analyzing Radial Velocity Elements

I. The Code

¹ Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal
² Observatoire Astronomique de l’Université de Genève, Chemin Pegasi 51, 1290 Versoix, Switzerland

^★ Corresponding author: khaled.almoulla@astro.up.pt

Received: 31 March 2025
Accepted: 22 July 2025

Abstract

Context. To overcome the radial velocity (RV) precision barrier imposed by stellar variability, there has been a recent surge of software aimed at simulating and modeling different aspects of these activity patterns, which currently limit the feasibility of detecting Earth-like exoplanets.

Aims. We present Analyzing Radial Velocity Elements (ARVE), a Python-based software which enables RV extraction using various customizable techniques, along with a subsequent analysis of the stellar and planetary signals present in the RVs. One of ARVE’s unique features is its library of pre-computed auxiliary data, which includes synthetic spectra and spectral line masks that enable the code to efficiently perform certain routines with minimal input from the user.

Methods. ARVE is a class-based and modular code in which its functionalities are divided between four subclasses: functions, which handles general functions utilized by the other subclasses; data, which reads the input data, loads the auxiliary data, and extracts RVs from input high-resolution spectra; star, which characterizes the stellar activity components present in the RV time series; and planets, which performs fits of Keplerian signals in the data and offers injection-recovery tests of fictitious planets to determine the detection limits.

Results. We performed a demonstration of ARVE on three years of HARPS-N solar data. We investigated the evolution of granulation and supergranulation characteristic timescales with activity level. Additionally, we revealed the differences in planetary period-mass detection limits when extracting RVs with different methods.

Conclusions. As stellar activity mitigation techniques grow more diverse, we foresee that a tool such as ARVE could greatly benefit the community by offering a user-friendly and multi-functional approach to extracting and analyzing RV time series. With its current code structure, it is feasible to expand its functionality and increase compatibility by adding more spectrographs to future versions of ARVE.