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Jintong Wang

Publications and source records attributed to Jintong Wang.

2 recordsLinked to original sources

The complex periodic variability of PKS 0301-243: hints of a supermassive black hole binary candidate

In hierarchical galaxy formation, supermassive black hole binaries are a ubiquitous consequence of frequent galaxy mergers, yet subparsec binaries remain difficult to be confirmed. Periodic flux modulations may arise from the orbital motion of black holes in close binaries, while relativistic jet precession can provide an additional source of periodic variability. Such periodic signatures therefore motivate us to identify close binary candidates. Here, we report an optical periodicity of $269^{+13}_{-14}$ days in both Zwicky Transient Facility g and r bands of the blazar PKS 0301-243, with corrected global significances of $2.75σ$ and $2.18σ$, respectively. This signal persists over 7 cycles across a baseline of ~2000 days. We interpret this period as a possible modulation associated with the orbital motion of a supermassive black hole binary. A longer optical periodicity of $889^{+47}_{-46}$ days is identified in Katzman Automatic Imaging Telescope, with corrected global significance of $1.97σ$. The longer periodicity spans approximately 5 cycles over a baseline of ~4500 days, potentially associated with jet precession. Both periodicities are independently identified using multiple methods. A supermassive black hole binary incorporating a relativistic jet and a circumbinary disc could reproduce the observed light curve ensemble. For a total mass of $10^{8.95} M_\odot$ and a mass ratio q = 0.1, the derived milli-parsec separation and a short gravitational wave driven coalescence time, $t_c = 1122^{+144}_{-144}$ years, imply that PKS 0301-243 may be a rare source of nano-hertz gravitational waves and a potential multimessenger laboratory for the terminal stages of supermassive black hole coalescence.

astro-ph.HE↗

Learning-based Seam Correspondence Reconstruction in Sewing Patterns

Digital sewing patterns typically consist of disjoint 2D panels without explicit stitch annotations, making downstream 3D modeling reliant on labor-intensive expert specification. In this paper, we present a graph-based learning framework that reconstructs two-level stitching information, coarse panel connectivity and fine-grained seam correspondence, from 2D panel geometry alone. At the coarse level, panel connectivity is inferred by predicting panel semantics associated with anatomical body regions, enforcing consistency with body structure and garment design conventions. Based on the reconstructed panel graph, fine-grained seam correspondences between panel pairs are inferred by learning latent edge representations that jointly encode local seam geometry and global garment context through graph message passing. The resulting edge embeddings are subsequently decoded into detailed seam correspondences. Our method supports complex sewing-pattern topologies, including many-to-one correspondences, intra-panel seams, and curved seams. Experiments demonstrate high stitching accuracy and strong generalization across garment styles.

cs.CV↗