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arXiv AI By Junle Li, Zhaoxiang Wu, Guanda Gong, Zhaohan Li, Wuming Luo, Jiahui Wei, Wenxing Fang, Hehe Fan

Suppression of $^{14}\mathrm{C}$ photon hits in large liquid scintillator detectors via spatiotemporal deep learning

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The paper presents three deep‑learning models— a gated spatiotemporal graph neural network and two Transformer‑based architectures— designed to identify and tag photons from $^{14}$C beta decay in large liquid scintillator detectors. By applying these models to simulated events containing both a $^{14}$C decay and a low‑energy positron, the authors achieve $^{14}$C recall rates between 25% and 48% while keeping positron misidentification below 1%. This selective tagging improves the total charge resolution for events where the $^{14}$C and positron photon hits overlap in space and time.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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