activity
20222026
most citedExoplANNET: A deep learning algorithm to detect and identify planetary signals in radial velocity data

6 citations · 12 across the 5 of their papers we have counts for

collaborators

5 papers

astro-ph.EP2026

Automatic search for transiting planets in TESS-SPOC FFIs with RAVEN: over 100 newly validated planets and over 2000 vetted candidates

M. Lafarga, D. J. Armstrong, K. Cui +7

Space-based missions such as TESS are identifying a wealth of short-period ( d) transiting planets. Despite the growing number of confirmed and candidate planets, the s…

astro-ph.EP2026★ 3 cited

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

Kaiming Cui, David J. Armstrong, Andreas Hadjigeorghiou +5

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 (…

astro-ph.EP2025

RAVEN: RAnking and Validation of ExoplaNets

Andreas Hadjigeorghiou, David J. Armstrong, Kaiming Cui +5

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…

astro-ph.EP2023★ 6 cited

ExoplANNET: A deep learning algorithm to detect and identify planetary signals in radial velocity data

L. A. Nieto, R. F. Díaz

The detection of exoplanets with the radial velocity method consists in detecting variations of the stellar velocity caused by an unseen sub-stellar companion. Instrumental errors,…

astro-ph.EP2022★ 3 cited

Null transit detections of 68 radial velocity exoplanets observed by TESS

F. V. Lovos, R. F. Díaz, L. A. Nieto

In recent years the number of exoplanets has grown considerably. The most successful techniques in these detections are the radial velocity (RV) and planetary transits techniques,…