Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer
Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer
复制标题
通过带有 Set Transformer 的二分图网络触发 sPHENIX 实验检测
DOI：
10.1007/978-3-031-26409-2_4
复制
发表时间：
2022
影响因子：
3.5
通讯作者：
Dantong Yu
中科院分区：
文献类型：
--
作者：
Tingting Xuan;Giorgian Borca;Yimin Zhu;Yu Sun;Cameron Dean;Z. Shi;Dantong Yu
。布鲁克黑文国家实验室的离子对撞机是世界尺度上最大的核物理实验之一，并且被优化以检测涉及魅力和美容的物理过程。从两个快速硅检测器中进行的几何信息。通过我们的训练实验，每个事件都可以视为图形。 ，我们的模型是parsi的，并且在我们的模型中，粒子相互作用的模型增加了精度和AUC评分。以及有效的信息交换和自适应图池。
. Trigger (interesting events) detection is crucial to high-energy and nuclear physics experiments because it improves data acquisition ef-ficiency. It also plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes involving charm and beauty quarks. These particles are produced in collisions involving two proton beams, two gold nuclei beams, or a combination of the two and give critical insights into the formation of the early universe. This paper presents a model architecture for trigger detection with geometric information from two fast silicon detectors. Transverse momentum is introduced as an intermediate feature from physics heuristics. We also prove its importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our model is parsi-monious and increases the accuracy and the AUC score by more than 15%. modeling of particle interactions in Our model the pairwise interactions between a two-way scattering and for effective information exchange and adaptive graph pooling.
影响因子：
5.5
作者：
Gregor Kasieczka;T. Plehn;A. Butter;K. Cranmer;Dipsikha Debnath;B. Dillon;M. Fairbairn;D. Faroughy;W. Fedorko;L. Gouskos;J. Kamenik;Patrick T. Komiske;Simon Leiss;A. Lister;S. Macaluso;S. Macaluso;E. Metodiev;L. Moore;B. Nachman;B. Nachman;Karl Nordström;J. Pearkes;H. Qu;Y. Rath;M. Rieger;D. Shih;J. Thompson;Sreedevi Varma
通讯作者：
Gregor Kasieczka;T. Plehn;A. Butter;K. Cranmer;Dipsikha Debnath;B. Dillon;M. Fairbairn;D. Faroughy;W. Fedorko;L. Gouskos;J. Kamenik;Patrick T. Komiske;Simon Leiss;A. Lister;S. Macaluso;S. Macaluso;E. Metodiev;L. Moore;B. Nachman;B. Nachman;Karl Nordström;J. Pearkes;H. Qu;Y. Rath;M. Rieger;D. Shih;J. Thompson;Sreedevi Varma
RHIC 能量的 pythia8 基础事件调整
影响因子：
5
作者：
Aguilar, Manny Rosales;Chang, Zilong;Elayavalli, Raghav Kunnawalkam;Fatemi, Renee;He, Yang;Ji, Yuanjing;Kalinkin, Dmitry;Kelsey, Matthew;Mooney, Isaac;Verkest, Veronica
通讯作者：
Verkest, Veronica