Salvato in:
| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.23134 |
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Sommario:
- The Fermi Large Area Telescope (Fermi-LAT) has detected more than 7,000 gamma-ray sources, a significant fraction of which are identified as blazars, while a comparable number remain classified as blazars of uncertain type (BCUs) or are unassociated with counterparts at other wavelengths. The absence of complete multi-wavelength spectral information presents a major obstacle to robust source classification, despite such data providing the most reliable means of understanding blazar properties. In this work, we focus on classifying BCUs into the two primary blazar subclasses, flat-spectrum radio quasars (FSRQs) and BL Lacertae objects (BL Lacs), using a feed-forward artificial neural network (ANN) trained on multi-wavelength observational parameters. We first identify the most informative features by quantifying their information content and then use these features to train the ANN, whose performance is evaluated using a k-fold cross-validation strategy to ensure robust generalization. The trained model is subsequently applied to classify BCUs into BL Lacs and FSRQs. Our results demonstrate that machine learning-based classification using a carefully selected set of multi-wavelength parameters offers an efficient and reliable approach for resolving the nature of BCUs and improving the completeness of the gamma-ray blazar population in Fermi-LAT catalogs.