灯笼：量热计模拟中用于物理引导扩散模型的冲突感知梯度融合
Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation
- University of Virginia（弗吉尼亚大学）
- Biocomplexity Institute, University of Virginia（弗吉尼亚大学生物复杂性研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对量热计模拟中扩散模型物理错误及标准度量忽略相关结构问题，引入CFD，编码软样本结构为两个物理感知辅助损失，通过GradBlend结合去噪得到Lantern，实验表明其能提高相关指标，消融实验揭示不同损失对调度的敏感性。
AI中文摘要：
量热计簇射的蒙特卡罗模拟是高亮度大型强子对撞机的主要瓶颈，扩散模型已成为快速、高保真的替代方法。然而，其去噪目标纯粹是统计性的：模型可以在物理错误的情况下最小化它。现有的物理信息生成方法无法弥合这一差距，因为它们假设了一种封闭形式的定律、一个控制偏微分方程残差或一个硬样本约束，而簇射并不提供这些。标准度量忽略了量热计层和体素之间的相关结构，仅在物理特征空间中比较簇射。我们引入了相关弗罗贝尼乌斯距离（CFD），用于在层和体素尺度上评估相关保真度。然后，我们将簇射中可用的软样本结构编码为两个物理感知辅助损失：基于计数统计的方差稳定体素残差损失和探测器几何结构上的图拉普拉斯损失。我们通过GradBlend将两者与去噪相结合，得到了物理引导的扩散替代模型Lantern。在CaloChallenge数据集2上，通过PCGrad、GradNorm、IMTL - G和ConFIG等任务对称规则注入物理损失，相对于仅去噪，FPD会增加2 - 100倍，而GradBlend无需回归即可接受相同信号，并且结合拉普拉斯损失，Lantern提高了FPD和CFD。我们对辅助损失调度器的消融实验表明，体素残差损失的梯度与去噪冲突，需要一个仅去噪的终端阶段来保持簇射保真度，而非冲突的拉普拉斯损失对调度不敏感。
英文摘要：
Monte Carlo simulation of calorimeter showers is a principal bottleneck for the High-Luminosity LHC, and diffusion models have emerged as fast, high-fidelity surrogates. Their denoising objective is purely statistical, however: a model can minimize it while placing the physics wrong. Existing physics-informed generative methods cannot close this gap, because they assume a closed-form law, a governing PDE residual or a hard per-sample constraint, that a shower does not supply: no per-sample PDE governs a stochastic cascade, and energy conservation fixes only one scalar per shower. Standard metrics ignore the correlation structure across calorimeter layers and voxels, comparing showers only in a physics feature space. We address both gaps. We introduce the Correlation Frobenius Distance (CFD), a single normalized score for correlation fidelity at layer-wise and voxel-wise scales. We then encode the soft per-sample structure available in a shower as two physics-aware auxiliary losses: a variance-stabilized voxel residual loss grounded in counting statistics, and a graph Laplacian loss over the detector geometry. We combine both with denoising through GradBlend, which anchors the step magnitude to the denoising gradient while letting the auxiliary steer its direction, yielding Lantern, a physics-guided diffusion surrogate. On CaloChallenge Dataset 2, injecting the physics losses through task-symmetric rules such as PCGrad, GradNorm, IMTL-G, and ConFIG inflates FPD by 2-100x relative to denoising alone, whereas GradBlend admits the same signal without regression and, with the Laplacian loss, Lantern improves both FPD and CFD. Our ablation on the auxiliary loss scheduler shows that the voxel residual loss, whose gradient conflicts with denoising, requires a terminal denoising-only phase to preserve shower fidelity, whereas the non-conflicting Laplacian loss is insensitive to the schedule.
