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Efficient Low-rank Bayesian Neural Networks for Incremental Pulsar Candidate Identification

  • Authors: Yi Liu, Suxun Zhu, Jing Jin, Hongyang Zhao, Taofei Jiang, Jie Liu

Yi Liu et al 2026 The Astrophysical Journal Supplement Series 285 .

  • Provider: AAS Journals

Caption: Figure 1.

Pulsar candidate diagnostic plots: “accn_curve” shows the signal-to-noise ratio as a function of acceleration, “dm_curve” illustrates changes in signal-to-noise across dispersion measure values, “pdm_snr” visualizes the folded signal-to-noise ratio across trial period corrections and DM values, “profile” displays the pulse shape of the candidate, “sub-bands” reveals frequency-dependent signal variations, and “subintegrations” demonstrates time-dependent signal fluctuations.

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