Boost recall in QSO selection from highly imbalanced photometric datasets

Context. The identification of bright QSOs is of great importance to probe the intergalactic medium and address open questions in cosmology. Several approaches have been adopted to find such sources in currently available photometric surveys, including machine learning methods. However, the rarity o...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Calderone, Giorgio, Guarneri, Francesco, Porru, Matteo, Cristiani, Stefano, Grazian, Andrea, Nicastro, Luciano, Bischetti, Manuela, Boutsia, Konstantina, Cupani, Guido, D'Odorico, Valentina, Feruglio, Chiara, Fontanot, Fabio
Format: Article
Language:English
Subjects:
Classification
Completeness
Datasets
Galaxies
Intergalactic media
Machine learning
Photometry
Recall
Online Access:Get full text
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Summary:Context. The identification of bright QSOs is of great importance to probe the intergalactic medium and address open questions in cosmology. Several approaches have been adopted to find such sources in currently available photometric surveys, including machine learning methods. However, the rarity of bright QSOs at high redshifts compared to contaminating sources (such as stars and galaxies) makes the selection of reliable candidates a difficult task, especially when high completeness is required. Aims. We present a novel technique to boost recall (i.e., completeness within the considered sample) in the selection of QSOs from photometric datasets dominated by stars, galaxies, and low-z QSOs (imbalanced datasets). Methods. Our method operates by iteratively removing sources whose probability of belonging to a noninteresting class exceeds a user-defined threshold, until the remaining dataset contains mainly high-z QSOs. Any existing machine learning method can be used as underlying classifier, provided it allows for a classification probability to be estimated. We applied the method to a dataset obtained by cross-matching PanSTARRS1, Gaia, and WISE, and identified the high-z QSO candidates using both our method and its direct multi-label counterpart. Results. We ran several tests by randomly choosing the training and test datasets, and achieved significant improvements in recall which increased from 50% to 85% for QSOs with z>2.5, and from 70% to 90% for QSOs with z>3. Also, we identified a sample of 3098 new QSO candidates on a sample of 2.6x10^6 sources with no known classification. We obtained follow-up spectroscopy for 121 candidates, confirming 107 new QSOs with z>2.5. Finally, a comparison of our candidates with those selected by an independent method shows that the two samples overlap by more than 90% and that both methods are capable of achieving a high level of completeness.
ISSN:2331-8422