arXiv subjects

Pablo A. Peña

Publications and source records attributed to Pablo A. Peña.

3 recordsLinked to original sources

arXiv2026

Cold giant discoveries from a joint radial-velocity and astrometry framework

The population of long-period giant planets shapes planetary system architectures and formation pathways, but these cold Jupiters remain relatively unexplored. Radial velocity (RV) surveys lose sensitivity at multi-AU separations, while transit surveys have poor detection probability at long periods. Absolute astrometry from the Hipparcos and Gaia missions offer an additional source for stellar motion that can break the orbital inclination degeneracy and strengthen detection confidence. This is especially timely ahead Gaia DR4/DR5, expected to enable routine astrometric vetting and true-mass measurements for long-period RV planets. Extending the Chile-Hertfordshire ExoPlanet Survey (CHEPS) by combining RVs spanning up to 16 years with absolute astrometry, we search for and characterise cold giants around metal-rich FGK stars. We upgrade the EMPEROR framework, incorporating astrometric differencing to jointly fit RVs and astrometry for five CHEPS targets, performing Bayesian model comparison and quantify the astrometric contribution. Our analysis characterises orbital parameters for two known planets in HIP 21850 and detects five new: a warm Jupiter--HIP 10090c, orbital period $P=321.8 \pm 0.5$ d and mass $M=0.85 \pm 0.08$ $M_J$, and four Jupiter analogues--HIP 8923b, with $P=14.1 \pm 0.06$ yr and $M=9.98\pm 0.47 M_J$, HIP 10090b with $P=8.1\pm 0.3$ yr and $M=3.87\pm 0.63$ $M_J$, HIP 39330b with $P=12.7\pm 0.7$ yr and $M=1.68\pm 0.15$ $M_J$, and HIP 98599b with $P=7.3\pm 0.1$ yr and $M=6.85\pm 0.16$ $M_J$. Adding astrometry reduces period and mass uncertainties by factors between 3 and 10 and increases the Bayes factor by up to 60. The synergy of long-baseline RVs and absolute astrometry provides a robust pathway to discover and characterise cold giant planets. Our results demonstrate that astrometry meaningfully improves detection confidence and converts minimum masses into true masses.

astro-ph.EP↗

arXiv2025

EMPEROR I. Exoplanet MCMC parallel tempering for RV orbit retrieval

We present EMPEROR, an open-source Python framework designed for efficient exoplanet detection and characterisation with radial velocities (RV). EMPEROR integrates Dynamic Nested Sampling (DNS) and Adaptive Parallel Tempering (APT) Markov Chain Monte Carlo (MCMC), supporting multiple noise models such as Gaussian Processes (GPs) and Moving Averages (MA). The framework enables systematic model comparison using statistical metrics, including Bayesian evidence ($\ln{\mathcal{Z}}$) and Bayesian Information Criterion (BIC), while providing automated, publish-ready visualisations. EMPEROR is evaluated across three distinct systems to assess its capabilities in different detection scenarios. Sampling performance, model selection, and the search for Earth-mass planets are evaluated in data for 51 Pegasi, HD 55693 and Barnard's Star (GJ 699). For 51 Pegasi, APT achieves an effective sampling increase over DNS by a factor 3.76, while retrieving tighter parameter estimates. For HD 55693 the stellar rotation $P_{\text{rot}}=29.72^{+0.01}_{-0.02}$ and magnetic cycle $P_{\text{mag}}=2557.0^{+70.1}_{-36.7}$ are recovered, while demonstrating the sensitivity of $\ln{\mathcal{Z}}$ to prior selection. For Barnard's star, several noise models are compared, and the confirmed planet parameters are successfully retrieved with all of them. The best model shows a period of 3.1536$\pm$0.0003~d, minimum mass of 0.38$\pm$0.03 M$_{\rm{\oplus}}$, and semi-major axis of 0.02315$\pm$0.00039~AU. Purely statistical inference might be insufficient on its own for robust exoplanet detection. Effective methodologies must integrate domain knowledge, heuristic criteria, and multi-faceted model comparisons. The versatility of EMPEROR in handling diverse noise structures, its systematic model selection, and its improved performance make it a valuable tool for RV exoplanetary studies.

astro-ph.EP↗

arXiv2025

Closing the Evidence Gap: reddemcee, a Fast Adaptive Parallel Tempering Sampler

Markov Chain Monte Carlo (MCMC) excels at sampling complex posteriors but traditionally lags behind nested sampling in accurate evidence estimation, which is crucial for model comparison in astrophysical problems. We introduce reddemcee, an Adaptive Parallel Tempering Ensemble Sampler, aiming to close this gap by simultaneously presenting next-generation automated temperature-ladder adaptation techniques and robust, low-bias evidence estimators. reddemcee couples an affine-invariant stretch move with five interchangeable ladder-adaptation objectives, Uniform Swap Acceptance Rate, Swap Mean Distance, Gaussian-Area Overlap, Small Gaussian Gap, and Equalised Thermodynamic Length, implemented through a common differential update rule. Three evidence estimators are provided: Curvature-aware Thermodynamic Integration (TI+), Geometric-Bridge Stepping Stones (SS+), and a novel Hybrid algorithm that blends both approaches (H+). Performance and accuracy are benchmarked on n-dimensional Gaussian Shells, Gaussian Egg-box, Rosenbrock Functions, and exoplanet radial-velocity time-series of HD 20794. Across Shells up to 15 dimensions, reddemcee presents roughly 7 times the effective sampling speed of the best dynamic nested sampling configuration. The TI+, SS+ and H+ estimators recover estimates under 3 percent error and supply realistic uncertainties with as few as six temperatures. In the HD 20794 case study, reddemcee reproduces literature model rankings and yields tighter yet consistent planetary parameters compared with dynesty, with evidence errors that track run-to-run dispersion. By unifying fast ladder adaptation with reliable evidence estimators, reddemcee delivers strong throughput and accurate evidence estimates, often matching, and occasionally surpassing, dynamic nested sampling, while preserving the rich posterior information which makes MCMC indispensable for modern Bayesian inference.

astro-ph.IM↗