Govorkova Ekaterina, Puljak Ema, Aarrestad Thea, Pierini Maurizio, Woźniak Kinga Anna, Ngadiuba Jennifer
European Organization for Nuclear Research (CERN), CH-1211, Geneva 23, Switzerland.
University of Vienna, Vienna, Austria.
Sci Data. 2022 Mar 29;9(1):118. doi: 10.1038/s41597-022-01187-8.
In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments ATLAS and CMS reduce this overwhelming data volume to a sustainable level, by deciding in real-time whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events that emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised new physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.
在大型强子对撞机的粒子探测器中，每秒会产生数亿次质子-质子碰撞。如果能够存储这些碰撞产生的全部数据流，那么每秒将有数十太字节的数据被写入磁盘。通用实验ATLAS和CMS通过实时决定每个碰撞事件是应保留以供进一步分析还是丢弃，将这一庞大的数据量减少到可承受的水平。我们引入了一个质子碰撞事件数据集，该数据集模拟了由这样一个实时处理系统收集的典型数据流，并通过要求至少存在一个电子或μ子进行了预筛选。该数据集可用于开发新颖的事件选择策略，并评估它们对新现象的敏感性。特别是，我们打算激发基于社区的努力，以设计用于执行无监督新物理探测的新颖算法，这些算法经过定制以适应典型粒子探测器实时事件选择系统的带宽、延迟和计算资源限制。
