活动星系核（AGN）吸积盘中嵌入的伽马暴（GRB）喷流的高能中微子信号：动态喷流传播框架
High-energy neutrino signatures of embedded GRB jets in AGN disks: a dynamic jet-propagation framework
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中文总结 AI 辅助
本研究开发了追踪喷流传播的动态框架，结合SG和TQM盘模型分析嵌入AGN盘的GRB喷流的高能中微子信号，揭示了喷流阻滞/突破时中微子辐射的差异，强调解析喷流动力学对多信使建模的重要性。
中文摘要 AI 辅助
嵌入在活动星系核（AGN）吸积盘中的相对论喷流是极具潜力的高能中微子源，但其辐射通常仅通过单一代表性喷流状态进行估算。我们开发了一个随时间变化的框架，用于追踪喷流头部的传播、反向激波条件的演化以及粒子冷却过程，直至喷流被阻滞或突破，并将其应用于SG和TQM盘模型。对于代表性的喷流阻滞案例，中微子辐射由喷流停滞附近的高耗散阶段主导，因此停滞状态近似法可在约14%的误差内复现轨迹积分、探测器加权的事件产额。然而在突破案例中，喷流头部在陡峭的盘密度梯度上的快速加速会抑制反向激波耗散，即便考虑可用能量预算，也可能导致单状态估算过高预测注量。完整的轨迹积分还会重塑高能谱尾部，并在两种盘模型中产生超大质量黑洞（SMBH）质量和盘半径上的可探测性差异。一些低密度外盘案例会形成延伸至10-100 PeV范围的更硬尾部，这为未来的超高能中微子搜寻提供了动机。因此，解析喷流传播动力学对于评估AGN盘环境中的嵌入暂现源、避免多信使建模中的系统偏差是不可或缺的。
英文摘要
Relativistic jets embedded in active galactic nucleus (AGN) accretion disks are promising high-energy neutrino sources, but their emission is often estimated from a single representative jet state. We develop a time-dependent framework that follows jet-head propagation, evolving reverse-shock conditions, and particle cooling until the jet chokes or breaks out, and apply it to the SG and TQM disk models. For the representative choked cases, neutrino emission is dominated by the high-dissipation phase near jet stalling, allowing a stalling-state approximation to reproduce the trajectory-integrated, detector-weighted event yield within approximately $14\%$. In breakout cases, however, rapid jet-head acceleration across steep disk-density gradients suppresses reverse-shock dissipation and can cause single-state estimates to overpredict the fluence, even after accounting for the available energy budget. Full trajectory integration also reshapes the high-energy spectral tail and produces distinct detectability patterns across SMBH mass and disk radius for the two disk models. Some lower-density outer-disk cases develop harder tails extending into the 10--100 PeV range, motivating future ultra-high-energy neutrino searches. Resolving jet propagation dynamics is therefore indispensable for evaluating embedded transients across AGN disk environments and avoiding systematic biases in multi-messenger modeling.
