发表机构
SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GLINT通过跨帧共识将数据集作为推断单位，解决串行晶体学小N区域盲索引难题，实现低延迟实时索引，并产出高质量合并数据。
AI 中文摘要
经典晶体学通过从单个样品采集的旋转系列来确定取向和晶胞几何结构，而串行晶体学则从许多随机取向的微晶中各获取一张静止曝光图像，并必须通过汇集整个集合中的部分测量值来重建完整数据集。在串行实验中，每帧通常仅包含一部分反射，而具有少量可靠峰值的帧（小N区域）尤其难以进行盲索引，因为多个不正确的晶格或取向可以解释稀疏的观测结果。GLINT中实现的方法通过将数据集而非单个帧作为推断单位来处理这一小N区域。为许多帧生成候选解，将弱但重复出现的晶格假设汇集到整个集合中以识别共享晶胞，然后通过比盲索引维度更低的已知晶胞取向搜索，将每帧配准到该共识晶胞。在基准测试中，GLINT在索引产出率上与比较中最强的盲索引器相当，而每帧盲索引延迟约为XGANDALF最快配置的40倍更低，且批量已知晶胞配准完全在单个GPU上运行，允许在串行数据收集过程中实时进行晶胞发现。在实验性串行数据上，由GLINT盲索引的帧合并达到晶体学质量（CC* = 0.90），同时该方法拒绝非晶体图像，而不是强制进行无根据的索引分配。这些结果表明，索引性能不仅可以通过索引产出率和吞吐量来评估，还可以通过所得合并晶体学数据的质量来评估。
英文摘要
Classical crystallography determines orientation and unit-cell geometry from a rotation series collected from a single specimen, whereas serial crystallography acquires one still exposure from each of many randomly oriented microcrystals and must reconstruct a complete dataset by pooling partial measurements across the ensemble. In serial experiments, each frame typically contains only a subset of reflections, and frames with few reliable peaks (the small-N regime) are especially difficult to index blindly, because multiple incorrect lattices or orientations can explain sparse observations. The approach implemented in GLINT addresses this small-N regime by treating the dataset, rather than the individual frame, as the unit of inference. Candidate solutions are generated for many frames, weak but recurrent lattice hypotheses are pooled across the ensemble to identify a shared unit cell, and each frame is then registered against that consensus cell through a known-cell orientation search with lower dimensionality than blind indexing. In benchmark tests, GLINT matched the strongest blind indexer in the comparison on indexing yield at roughly 40x lower per-frame blind-indexing latency against XGANDALF's fastest configuration, and batched known-cell registration ran entirely on one GPU, allowing cell discovery on the fly during serial data collection. On experimental serial data, frames indexed blind by GLINT merged to crystallographic quality (CC* = 0.90), while the method rejected non-crystal images rather than forcing unsupported indexing assignments. These results demonstrate that indexing performance can be evaluated not only by indexing yield and throughput, but also by the quality of the resulting merged crystallographic data.
Comments32 pages including Supporting Information (Secs. S1-S16), 8 figures, 12 tables
