Lyman-alpha Blobs

     

I developed pyBIA, a machine-learning framework for the classification of high-redshift Lyman-alpha blobs in multi-band broadband imaging data. The pipeline is composed of several machine learning algorithms operating in sequence, and includes integrated source detection, morphological segmentation, anomaly detection, and deep learning classification. The program enables the automated reduction of massive astronomical catalogs into high-probability targets for spectroscopic follow-up. The initial paper outlining program development and presenting a catalog of Lyman-alpha blob candidates in the Boötes field is available here. An additional catalog of candidates in the COSMOS field will be presented in an upcoming publication (and available now upon request).