ML-aided selected Lyalpha candidates in COSMOS2020

To ensure balanced performance, the models were trained on carefully selected datasets, with similar redshift and i-band magnitude distributions. Additionally, the models were tested for robustness by perturbing the photometric data using the associated observational uncertainties. Our classification models achieved F1-scores ~87%, successfully identifying around 7000 objects with unanimous agreement across all models.

This more than doubles the number of LAEs identified in the COSMOS field compared with the SC4K dataset. We managed to spectroscopically confirmed 60 of these LAEs candidates using the publicly available catalogs in the COSMOS field. These results highlight the potential of machine learning in efficiently identifying LAEs candidates, laying foundations for application to larger photometric surveys, such as Euclid and LSST.

By complementing traditional approaches and providing robust pre-selection capabilities, our models facilitate the analysis of these objects, crucial to increase our knowledge of the overall LAE population.