Enhancing the Interpretability of Radial Velocity Analysis Using Leave-one-out Cross-validation
Abstract
Radial velocity (RV) observations are reaching increasingly high precision, enabling the targeting of low-amplitude signals. Assessing results obtained at the limits of our capabilities requires understanding the relationship between data, models and inference. Leave-one-out cross-validation (LOO-CV) has become an increasingly popular tool for interpretable model criticism in astronomical data analysis. In this work, we demonstrate how LOO-CV can provide insight into the RV analysis of exoplanetary systems through three case studies. We first investigate the anomalously high mass of HD 119130 b inferred from CARMENES RVs, later revised significantly downward after follow-up observations with other instruments. We show that this high mass was strongly driven by two individual measurements, whose removal significantly reduces the tension between data sets. Next, we use LOO-CV as a model selection tool for the GJ 4276 system. Here, LOO-CV prefers an eccentric single-planet model over a two-planet model, and identifies the orbital phases at which follow-up observations would best discriminate between them. Finally, we extend LOO-CV to models with correlated noise, applying it to the GJ 357 system, whose RVs are modeled with a Gaussian process. We find no predictive support for a fourth planetary signal that is moderately favored by the Bayesian evidence, strengthening the interpretation of a three-planet architecture previously reported in the literature. We provide practical guidance for implementing LOO-CV in RV analysis, together with an online tutorial. Such methods will become ever more important as future RV surveys push the limits of planet detection and characterization.
Related
- VELOcities of CEpheids (VELOCE) III. Reconstructing radial velocity curves of classical Cepheids
- Radial velocity follow-up of astrometric substellar companions: 6 confirmed brown dwarfs and 13 impostor binaries Gaia
- Companions of hot Jupiters: Two decades of radial velocity monitoring with CORALIE
- The GAPS programme at TNG. LXXIX. New mass constraints for the infant planets in the V1298,Tau system through a long-term radial-velocity monitoring with HARPS-N
- The Long-Term Radial Velocity Stability of the COS G130M/1291 Mode
BibTeX
@misc{nixon2026enhancing,
title = {{Enhancing the Interpretability of Radial Velocity Analysis Using Leave-one-out Cross-validation}},
author = {Matthew C. Nixon and Ritvik Basant and Rafael Luque and Luis Welbanks and Pierrot Lamontagne},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07181},
eprint = {2610.07181},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07181},
}
RIS
TY - UNPB
TI - Enhancing the Interpretability of Radial Velocity Analysis Using Leave-one-out Cross-validation
AU - Matthew C. Nixon
AU - Ritvik Basant
AU - Rafael Luque
AU - Luis Welbanks
AU - Pierrot Lamontagne
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.07181
UR - https://arxiv.org/abs/2610.07181
AB - Radial velocity (RV) observations are reaching increasingly high precision, enabling the targeting of low-amplitude signals. Assessing results obtained at the limits of our capabilities requires understanding the relationship between data, models and inference. Leave-one-out cross-validation (LOO-CV) has become an increasingly popular tool for interpretable model criticism in astronomical data analysis. In this work, we demonstrate how LOO-CV can provide insight into the RV analysis of exoplanetary systems through three case studies. We first investigate the anomalously high mass of HD 119130 b inferred from CARMENES RVs, later revised significantly downward after follow-up observations with other instruments. We show that this high mass was strongly driven by two individual measurements, whose removal significantly reduces the tension between data sets. Next, we use LOO-CV as a model selection tool for the GJ 4276 system. Here, LOO-CV prefers an eccentric single-planet model over a two-planet model, and identifies the orbital phases at which follow-up observations would best discriminate between them. Finally, we extend LOO-CV to models with correlated noise, applying it to the GJ 357 system, whose RVs are modeled with a Gaussian process. We find no predictive support for a fourth planetary signal that is moderately favored by the Bayesian evidence, strengthening the interpretation of a three-planet architecture previously reported in the literature. We provide practical guidance for implementing LOO-CV in RV analysis, together with an online tutorial. Such methods will become ever more important as future RV surveys push the limits of planet detection and characterization.
ER -