3 papers
astro-ph.IM2026
Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
Raphaël Bonnet-Guerrini, Bruno Sanchez, Dominique Fouchez +5
Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while communi…
astro-ph.EP2026
New substellar candidates identified through deep learning in the F150 sample of the large-scale SHINE direct imaging survey
Carles Cantero Mitjans, Mariam Sabalbal, Olivier Absil +3
Context. The SPHERE High-contrast Imaging survey for Exoplanets (SHINE) represents one of the largest direct imaging campaigns, targeting over 400 young, nearby stars with the goal…
astro-ph.IM2026
Enhanced detection limits in the SHINE F150 survey through the Regime Switching Model. Optimizing thresholds and investigating environmental noise
Mariam Sabalbal, Olivier Absil, Carl-Henrik Dahlqvist +1
In high-contrast imaging, a novel detection algorithm for angular differential imaging (ADI) sequences has recently been introduced: the Regime Switching Model (RSM). In this study…