MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

• URL: http://arxiv.org/abs/2101.08578v2
• Date: Wed, 10 Mar 2021 15:18:59 GMT
• Title: MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• Authors: Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria Spiropulu
• Abstract summary: In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a particle-level view of the event. We introduce a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, scalable, and graph neural networks. We report the physics and computational performance of the algorithm on a Monte Carlo dataset of top quark-antiquark pairs produced in proton-proton collisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton-proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural networks optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark-antiquark pairs produced in proton-proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

Related papers

• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Quantum-Annealing-Inspired Algorithms for Track Reconstruction at High-Energy Colliders [0.0]
  Track reconstruction can be formulated as a quadratic unconstrained binary optimization problem. We show that simulated bifurcation algorithms can be employed to solve the particle tracking problem.
  arXiv  Detail & Related papers  (2024-02-22T17:19:03Z)
• Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors [1.4609888393206634]
  We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid operations while achieving realistic reconstruction. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm.
  arXiv  Detail & Related papers  (2023-09-13T08:16:15Z)
• Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL [106.82295532402335]
  Existing reinforcement learning algorithms suffer from computational intractability, strong statistical assumptions, and suboptimal sample complexity. We provide the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics.
  arXiv  Detail & Related papers  (2023-04-12T14:51:47Z)
• Progress towards an improved particle flow algorithm at CMS with machine learning [8.3763093941108]
  particle-flow (PF) is of central importance to event reconstruction in the CMS experiment at the CERN LHC. In recent years, the machine learned particle-flow (MLPF) algorithm, a graph neural network that performs PF reconstruction, has been explored in CMS. We discuss progress in CMS towards an improved implementation of the algorithmF reconstruction, now optimized using generator/simulation-level particle information. This paves the way to potentially improving the detector response in terms of physical quantities of interest.
  arXiv  Detail & Related papers  (2023-03-30T18:41:28Z)
• Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation [59.45669299295436]
  We propose a Monte Carlo PDE solver for training unsupervised neural solvers. We use the PDEs' probabilistic representation, which regards macroscopic phenomena as ensembles of random particles. Our experiments on convection-diffusion, Allen-Cahn, and Navier-Stokes equations demonstrate significant improvements in accuracy and efficiency.
  arXiv  Detail & Related papers  (2023-02-10T08:05:19Z)
• Efficient Dataset Distillation Using Random Feature Approximation [109.07737733329019]
  We propose a novel algorithm that uses a random feature approximation (RFA) of the Neural Network Gaussian Process (NNGP) kernel. Our algorithm provides at least a 100-fold speedup over KIP and can run on a single GPU. Our new method, termed an RFA Distillation (RFAD), performs competitively with KIP and other dataset condensation algorithms in accuracy over a range of large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-21T15:56:13Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Machine Learning for Particle Flow Reconstruction at CMS [7.527568379083754]
  We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction.
  arXiv  Detail & Related papers  (2022-03-01T10:11:44Z)
• A Quantum Graph Neural Network Approach to Particle Track Reconstruction [1.087475836765689]
  We present an improved model with an iterative approach to overcome the low accuracy of the initial oversimplified Tree Network (TTN) model. We aim to leverage the capability of quantum computing to evaluate a very large number of states simultaneously and thus to effectively search a large parameter space.
  arXiv  Detail & Related papers  (2020-07-14T07:25:24Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.