Using holistic event information in the trigger

2018/08/02 by Dylan Bourgeois, C. Fitzpatrick, Bourgeois, Dylan +3
Medicine · Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det) #Medical Imaging Techniques and Applications #Particle Detector Development and Performance #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.1808.00711

openalex publication_date 2018/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In order to achieve the data rates proposed for the future Run 3 upgrade of the LHCb detector, new processing models must be developed to deal with the increased throughput. For this reason, we aim to investigate the feasibility of purely data-driven holistic methods, with the constraint of introducing minimal computational overhead, hence using only raw detector information. These filters should be unbiased - having a neutral effect with respect to the studied physics channels. In particular, the use of machine learning based methods seems particularly suitable, potentially providing a natural formulation for heuristic-free, unbiased filters whose objective would be to optimize between throughput and bandwidth.

Citations

• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
• Adam: A Method for Stochastic Optimization
• Estimating the Support of a High-Dimensional Distribution
• Neural Machine Translation by Jointly Learning to Align and Translate
• Training a Support Vector Machine in the Primal
• Order Matters: Sequence to sequence for sets
• The Mythos of Model Interpretability
• Scalable Learning of Non-Decomposable Objectives
• Convolutional Sequence to Sequence Learning
• SparseMAP: Differentiable Sparse Structured Inference
• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
• Stable Recurrent Models
• Prediction with a Short Memory
• Learning where to Attend with Deep Architectures for Image Tracking
• Machine Learning in High Energy Physics Community White Paper
• Mining gold from implicit models to improve likelihood-free inference

Related

• Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities
• Machine Learning for the LHCb Simulation
• An Automated Bandwidth Division for the LHCb Upgrade Trigger
• A Downstream and vertexing algorithm for Long Lived Particles (LLP) selection at the first High-level trigger (HLT1) of LHCb
• Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures