AI 中文总结
本研究针对CMS探测数据分辨率低的问题，通过对对象及其周围粒子云训练的回归模型，将探测数据与离线数据的分辨率差距缩小至离线校准不确定度内，提升了探测数据的实用性。
AI 中文摘要
CMS合作组于2011年提出的数据探测（Data scouting）技术，可拓展实验的探测范围，突破全事件存储带宽的限制：该技术不存储完整的事件记录，仅持久化高等级触发（HLT）已重建的物理对象，这使得记录的数据量比标准物理流多O(10)倍成为可能。这种速率提升的代价是实验分辨率的下降：在线重建通常具有更低的分辨率和更差的响应，主要源于为加快重建速度而采取的捷径，以及使用了非最优的校准常数。因此，探测数据大多在分辨率损失可忽略的情况下（如缪子），或相对于离线分辨率而言可接受的情况下（如喷注）使用。无论如何，推导特定的在线到离线修正的需求一直是一个限制因素。本文展示，对给定对象及其周围粒子云训练的回归模型，可将在线与离线分辨率之间的差距缩小至离线数据的典型不确定度范围内。该过程可应用于探测监测流，即同时进行离线重建的那部分探测数据。我们在CMS Run-1开放数据上验证了这一点，这些数据通过对相同事件进行配对的HLT和离线通道重新处理得到。一种架构无需修改即可应用于电子和喷注，它在多种量（运动学、隔离度、能量组成）上将残余偏差抑制了1至2个数量级，并实现了分辨率提升。按横动量（p_T）微分测量，修正后的对象落在CMS为该数据集提供的离线校准不确定度范围内，而未修正的HLT对象则超出了该范围：修正后的对象可使用标准的、中心提供的校准，这使得探测数据在实际用途中更接近离线数据。
英文摘要
Data scouting, introduced by the CMS collaboration in 2011, is a technique to extend the reach of the experiment beyond what full-event storage bandwidth allows: rather than the complete event record, only the physics objects already reconstructed by the High-Level Trigger (HLT) are persisted, which makes it affordable to record O(10) times more data than the standard physics stream. The price for this rate increase is in experimental resolution: online reconstruction has typically a lower resolution and a worse response, mostly due to shortcuts taken to speed up the reconstruction and to the use of suboptimal calibration constants. For this reason, scouting data has mostly been used when the resolution loss is negligible in absolute terms (muons) or with respect to the offline resolution (jets). In any case, the need to derive specific online-to-offline corrections has been a limiting factor. We show that a regression trained on a given object and the surrounding particle cloud can bring the gap between online and offline resolution within the typical uncertainty band of the offline data. This procedure could be applied to the scouting monitoring stream, the subset of scouting data also reconstructed offline. We demonstrate this on CMS Run-1 Open Data, reprocessed through paired HLT and offline passes over the same events. One architecture, applied unchanged to electrons and jets, suppresses residual biases by one to two orders of magnitude on various quantities (kinematics, isolation, energy composition) and achieves a resolution improvement. Measured differentially in p_T, the corrected objects fall inside the offline calibration uncertainties CMS quotes for this dataset, where the uncorrected HLT objects lie outside them: a corrected object can be treated with the standard, centrally provided calibration, bringing scouting data much closer to offline data for practical purposes.
Comments24 pages, 7 figures, 6 tables
