Addressing Distribution Discrepancies in Pulsar Candidate Identification via Bayesian-neural-network-based Multimodal Incremental Learning

Addressing Distribution Discrepancies in Pulsar Candidate Identification via Bayesian-neural-network-based Multimodal Incremental Learning

With the advancement of astronomical observation technology and the substantial increase in data volume, traditional methods for pulsar identification are increasingly challenged by the dynamic nature of data distributions. To address this, our study introduces a multimodal incremental learning appr...

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Bibliographic Details
Main Authors: Yi Liu, Jing Jin, Hongyang Zhao, Zhenyi Wang, Yi Shen
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:The Astrophysical Journal Supplement Series
Subjects:
Pulsars
Convolutional neural networks
Radio pulsars
Astronomy data analysis
Astronomy data modeling
Astronomical object identification
Online Access:https://doi.org/10.3847/1538-4365/ad9dec
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https://doi.org/10.3847/1538-4365/ad9dec

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