Luis Agustín Nieto | Searcharxiv

arXiv2026

Demographics of Close-In TESS Exoplanets Orbiting FGK Main-sequence Stars

Understanding the demographics of close-in planets is crucial for insights into exoplanet formation and evolution. We present a detailed analysis of occurrence rates for close-in (0.5-16 day) planets with radii between 2 and 20$\,R_{\oplus}$ around FGK main-sequence stars. Our study uses a comprehensive sample from four years of TESS Science Processing Operations Center full-frame image data cross-matched with Gaia, analysed through our rigorous detection, vetting, and validation pipeline. Using high-confidence planet candidates, we apply a hierarchical Bayesian model to determine occurrence rates in the two-dimensional orbital period-radius plane. Our results are presented using 10-by-10 bins across the period-radius parameter space, offering unprecedented resolution and statistical precision. We find an overall occurrence rate of $9.4^{+0.7}_{-0.6}\%$. When using identical binning, our occurrence rate posteriors distributions align with Kepler's but have a magnitude smaller uncertainties on average. For hot Jupiters, we estimate the overall occurrence rate of $0.39^{+0.03}_{-0.02}\%$. This value is consistent with the previous Kepler FGK-type result within $1σ$. We find an overall occurrence rate of Neptunian desert planets of $0.08\pm0.01\%$, to our knowledge the first such determination. Additionally, in a volume-limited Gaia subsample within 100 pc in the same parameter region, we measure an overall planet occurrence rate of $15.4^{+1.6}_{-1.5}\%$ and a hot Jupiter occurrence rate of $0.42^{+0.16}_{-0.12}\%$. Our results establishes an improved foundation for constraining theoretical models of exoplanet populations.

astro-ph.EP↗

arXiv2025

RAVEN: RAnking and Validation of ExoplaNets

We present RAVEN, a newly developed vetting and validation pipeline for TESS exoplanet candidates. The pipeline employs a Bayesian framework to derive the posterior probability of a candidate being a planet against a set of False Positive (FP) scenarios, through the use of a Gradient Boosted Decision Tree and a Gaussian Process classifier, trained on comprehensive synthetic training sets of simulated planets and 8 astrophysical FP scenarios injected into TESS lightcurves. These training sets allow large scale candidate vetting and performance verification against individual FP scenarios. A Non-Simulated FP training set consisting of real TESS candidates caused primarily by stellar variability and systematic noise is also included. The machine learning derived probabilities are combined with scenario specific prior probabilities, including the candidates' positional probabilities, to compute the final posterior probabilities. Candidates with a planetary posterior probability greater than 99% against each FP scenario and whose implied planetary radius is less than 8$R_{\oplus}$ are considered to be statistically validated by the pipeline. In this first version, the pipeline has been developed for candidates with a lightcurve released from the TESS Science Processing Operations Centre, an orbital period between 0.5 and 16 days and a transit depth greater than 300ppm. The pipeline obtained area-under-curve (AUC) scores > 97% on all FP scenarios and > 99% on all but one. Testing on an independent external sample of 1361 pre-classified TOIs, the pipeline achieved an overall accuracy of 91%, demonstrating its effectiveness for automated ranking of TESS candidates. For a probability threshold of 0.9 the pipeline reached a precision of 97% with a recall score of 66% on these TOIs. The RAVEN pipeline is publicly released as a cloud-hosted app, making it easily accessible to the community.

astro-ph.EP↗