Marina Lafarga Magro
University of Warwick

Abstract
The ever-growing volume of data from space-based missions such as TESS is opening new opportunities to discover and characterise planetary systems and their demographics. However, identifying genuine planetary signals among millions of observed stars remains a major challenge. In this talk, I will present our application of the new pipeline RAVEN to uniformly search and validate transiting planet candidates in TESS data. RAVEN identifies transiting planet candidates, classifies them into transiting planets and false positives using machine-learning models trained with realistic simulations, and performs statistical validation. Applying RAVEN to over two million main-sequence stars, we identified and validated more than 100 planets and compiled a sample of over 2000 promising candidates. I will discuss how RAVEN identifies reliable signals, the importance of homogeneous analyses, and how these efforts help us improve the TESS candidate sample and build a more complete picture of the planetary population.

2026 November 10, 11:00

IA
Online broadcast (Zoom)