Ponente
Descripción
The era of large space-based photometric surveys (Kepler, TESS) has required the use of automated algorithms for the massive detection of exoplanets. However, these methods, while necessary to handle enormous amounts of data, often overlook subtle signals present within transits, such as starspot crossings, flares, mutual planet-planet phenomena, or transit duration variations. To recover this valuable astrophysical information, we present the DIPS-OjOs (Detecting Irregular Photometric Signals - Ojímetro Survey) project, a direct Pro-Am collaboration between ICTEA researchers and amateur astronomers. We have developed an interactive web platform that superimposes theoretical global fit models onto individual transits, allowing for a detailed visual inspection of the residuals. In our pilot phase, each team member blindly inspected around a thousand transits (both real data from the Kepler mission and simulated data). The results demonstrate a high signal recovery efficiency by the collaborators, successfully identifying in the real data around twenty starspot crossing events, 7 in-transit flares, and dozens of timing anomalies. This work demonstrates that, in the era of Big Data and artificial intelligence, visual scrutiny by trained collaborators is an irreplaceable tool, and it lays the methodological groundwork to scale the project to a larger number of light curves from different missions as well as a larger number of collaborators.