A public HARPS radial velocity database corrected for systematic errors (2001.05942v1)

Abstract: Context. The HARPS spectrograph provides state-of-the-art stellar radial velocity (RV) measurements with a precision down to 1 m/s. The spectra are extracted with a dedicated data-reduction software (DRS) and the RVs are computed by CCF with a numerical mask. Aims. The aim of this study is three-fold: (i) Create easy access to the public HARPS RV data set. (ii) Apply the new public SERVAL pipeline to the spectra, and produce a more precise RV data set. (iii) Check whether the precision of the RVs can be further improved by correcting for small nightly systematic effects. Methods. For each star observed with HARPS, we downloaded the publicly available spectra from the ESO archive and recomputed the RVs with SERVAL. We then computed nightly zero points (NZPs) by averaging the RVs of quiet stars. Results. Analysing the RVs of the most RV-quiet stars, whose RV scatter is < 5 m/s, we find that SERVAL RVs are on average more precise than DRS RVs by a few percent. We find three significant systematic effects, whose magnitude is independent of the software used for the RV derivation: (i) stochastic variations with a magnitude of 1 m/s; (ii) long-term variations, with a magnitude of 1 m/s and a typical timescale of a few weeks; and (iii) 20-30 NZPs significantly deviating by a few m/s. In addition, we find small (< 1 m/s) but significant intra-night drifts in DRS RVs before the 2015 intervention, and in SERVAL RVs after it. We confirm that the fibre exchange in 2015 caused a discontinuous RV jump, which strongly depends on the spectral type of the observed star: from 14 m/s for late F-type stars, to -3 m/s for M dwarfs. Conclusions. Our NZP-corrected SERVAL RVs can be retrieved from a user-friendly, public database. It provides more than 212 000 RVs for about 3000 stars along with many auxiliary information, NZP corrections, various activity indices, and DRS-CCF products.

A Public HARPS Radial Velocity Database Corrected for Systematic Errors

This document introduces a critical contribution to the field of exoplanet research by creating a comprehensive database of radial velocity (RV) measurements from the High Accuracy Radial velocity Planet Searcher (HARPS) spectrograph. The paper addresses the limitations imposed by systematic errors in the original datasets and seeks to improve the precision of RV measurements, which are pivotal for detecting exoplanets.

Key Objectives and Methodology

The primary aim is to make HARPS RV data more accessible and accurate for the scientific community. This is achieved by:

1. Compiling a Public Data Set: Gathering HARPS spectra from the European Southern Observatory (ESO) archive for about 3,000 stars.
2. RV Recalculation using SERVAL: Employing the SpEctrum Radial Velocity AnaLyser (SERVAL) pipeline, originally designed to analyze spectra from M-type stars, to recompute RVs. SERVAL enhances precision through a template matching method rather than relying on cross-correlation functions with pre-defined masks.
3. Correcting Systematic Errors: Addressing systematic errors by calculating nightly zero-points (NZP) and making intra-night drift corrections to the RV data, hence providing a more reliable baseline.

Results and Significance

• Improved RV Precision: The SERVAL recalculated RVs show improved precision over the original DRS RVs. For pre-2015 data, the improvement is approximately 5%, while post-2015, the precision enhances by about 15%.
• Systematic Error Correction: Three systemic effects are identified and corrected: stochastic shifts (~1 m s$^{-1}$), long-term variations (~1 m s$^{-1}$), and systematic significant deviations in NZPs on approximately 20 to 30 epochs.
• Stability and Transitions: The research also highlights a spectral-type-dependent RV offset due to the 2015 upgrade of HARPS' optical fibers - noting shifts from ~14 m s$^{-1}$ for F-type to ~-3 m s$^{-1}$ for M dwarfs, which were successfully stabilized.

Implications and Future Work

This corrected database not only serves as an improved tool for confirming exoplanetary candidates, particularly for those identified by missions such as the Transiting Exoplanet Survey Satellite (TESS), but also aids in precision RV measurements. Further studies could extend this work by applying similar corrections to other data sets, perhaps using alternate methods such as machine learning to identify systematics more efficiently.

Looking forward, this framework can be adapted to newer instruments such as the Extremely Large Telescope (ELT), potentially offering insights into Earth-like exoplanets. Moreover, as spectrograph technology continues to evolve, consistent recalibration and error correction practices, as exemplified by this paper, will remain central to advancing our understanding of distant planetary systems.

By enhancing the fidelity of RV measurements and providing free access to this enriched dataset, this work fortifies the tools available for the exoplanet research community and nourishes the exploration and characterization of planetary systems beyond our solar system.