ARVE: Analyzing Radial Velocity Elements

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 .

Authors

Publication Details

Journal
Astronomy and Astrophysics
Published
2025-08-22
DOI
https://doi.org/10.1051/0004-6361/202554897
Citations
1
Primary Topic
Mechanical Engineering and Vibrations Research
Type
article
Field-Weighted Citation Impact
0.71

Funders

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article

ARVE: Analyzing Radial Velocity Elements

K. Al Moulla
1 citations
Astronomy and Astrophysics
Mechanical Engineering and Vibrations Research
0.71
article

ARVE: Analyzing Radial Velocity Elements

K. Al Moulla
article en
1 citations

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 .

Astronomy and AstrophysicsVol. 701
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, European Research Council
Openalex Percentile: Top 28%
Mechanical Engineering and Vibrations Research
0.71
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