ARVE: Analyzing Radial Velocity Elements. I. The Code

TL;DR

ARVE is a versatile Python software that extracts and analyzes radial velocity signals from stellar spectra, aiding in the detection of exoplanets amidst stellar activity noise.

Contribution

ARVE introduces a modular, user-friendly tool with pre-computed auxiliary data for efficient RV analysis and stellar activity characterization.

Findings

Demonstrated on three years of HARPS-N solar data.

Analyzed the evolution of granulation and supergranulation timescales.

Compared planetary detection limits using different RV extraction methods.

Abstract

Context. In order to overcome the radial velocity (RV) precision barrier imposed by stellar variability, there has been a surge of software aimed at simulating and modeling these activity patterns. Aims. We present Analyzing Radial Velocity Elements (ARVE), a Python-based software which enables RV extraction using various customizable techniques, and 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, allowing 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. Demonstrations of ARVE are performed on three years of HARPS-N solar data. We show the evolution of granulation and supergranulation characteristic timescales with activity level, and we investigate 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 like ARVE could greatly benefit the community by offering a user-friendly and multi-functional approach to extract and analyze RV time series. With its current code structure, expanded functionality and increased compatibility with more spectrographs should be easily addable to future versions of ARVE.