A study on the use of a machine learning algorithm for the level 1 trigger decision in the JUNO experiment ispresented. JUNO is a medium baseline neutrino experiment in construction in China, with the main goal of determining the neutrino mass hierarchy. A large liquid scintillator (LS)volume will detect the electron antineutrinos issued from nuclear reactors. The LS detector is instrumented by around 20000 large photomultiplier tubes. The hit information from each PMT will be collected into a center trigger unit for the level 1 trigger decision. The current trigger algorithm used to select a neutrino signal event is based on a fast vertex reconstruction. We propose to study an alternative level 1 (L1) trigger in order to achieve a similar performance as the vertex fitting trigger but with less logic resources by using firmware implemented machine learning model at the L1 trigger level. We treat the trigger decision as a classification problem and train a Multi-Layer Perceptron (MLP)model to distinguish the signal events with an energy higher than a certain threshold from noise events. We use JUNO software to generate datasets which include 100K physics events with noise and 100K pure noise events coming from PMT dark noise.For events with energy higher than 100 keV, the L1 trigger based on the converged MLP model can achieve an efficiency higher than 99%. After the training performed on simulations,we successfully implemented the trained model into a Kintex 7FPGA. We present the technical details of the neural network development and training, as well as its implementation in the hardware with the FPGA programming. Finally the performance of the L1 trigger MLP implementation is discussed.
Real-Time Wiener Deconvolution for feature reconstruction in JUNO
Institute of High Energy Physics, Chinese Academy of science（中国科学院高能物理研究所）；University of Chinese Academy of science（中国科学院大学）；Laboratori Nazionali dell'INFN di Frascati, Frascati, Italy（意大利国家核物理研究所弗拉斯卡蒂国家实验室）；Tsinghua University（清华大学）
Development of a Highly Selective First-Level Muon Trigger for ATLAS at HL-LHC Exploiting Precision Muon Drift-Tube Data
高亮度大型强子对撞机（HL-LHC）将提供一个独特的机会来探索物理学的本质，超越强和电弱相互作用的标准模型。高度选择性的第一级触发器对于HL-LHC的ATLAS实验的物理方案至关重要，其中瞬时光度将超过LHC Run 1的瞬时光度约一个数量级。ATLAS第一级μ子触发率主要由低动量μ子控制，这是因为RPC和TGC触发室的中等动量分辨率而被接受的。利用精密μ子漂移管（MDT）室的数据进行一级触发决策，可以克服这一局限性。这需要将MDT命中连续快速传输到检测器外触发逻辑和快速跟踪重建算法。μ子触发率的减少，可实现与建议的新的触发概念，一种新的快速跟踪重建算法的性能，和第一个硬件演示的方案与μ子测试束数据在欧洲核子研究中心伽马辐照设施进行了讨论。
Implementation of a Low-Temperature Monitoring and Alarm System for the Taishan Neutrino Experiment
Wuyi University（五邑大学）；Institute of High Energy Physics, Chinese Academy of Sciences（中国科学院高能物理研究所）；Sun Yat-sen University（中山大学）；Zhengzhou University（郑州大学）
Hardware Implementation of a Fast Algorithm for the Reconstruction of Muon Tracks in ATLAS Muon Drift-Tube Chambers for the First-Level Muon Trigger at the HL-LHC
