Progress towards an improved particle flow algorithm at CMS with machine learning

• URL: http://arxiv.org/abs/2303.17657v1
• Date: Thu, 30 Mar 2023 18:41:28 GMT
• Title: Progress towards an improved particle flow algorithm at CMS with machine learning
• Authors: Farouk Mokhtar, Joosep Pata, Javier Duarte, Eric Wulff, Maurizio Pierini, Jean-Roch Vlimant
• Abstract summary: 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.
• Score: 8.3763093941108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity. In recent years, the machine learned particle-flow (MLPF) algorithm, a graph neural network that performs PF reconstruction, has been explored in CMS, with the possible advantages of directly optimizing for the physical quantities of interest, being highly reconfigurable to new conditions, and being a natural fit for deployment to heterogeneous accelerators. We discuss progress in CMS towards an improved implementation of the MLPF reconstruction, now optimized using generator/simulation-level particle information as the target for the first time. This paves the way to potentially improving the detector response in terms of physical quantities of interest. We describe the simulation-based training target, progress and studies on event-based loss terms, details on the model hyperparameter tuning, as well as physics validation with respect to the current PF algorithm in terms of high-level physical quantities such as the jet and missing transverse momentum resolutions. We find that the MLPF algorithm, trained on a generator/simulator level particle information for the first time, results in broadly compatible particle and jet reconstruction performance with the baseline PF, setting the stage for improving the physics performance by additional training statistics and model tuning.

Related papers

• KFD-NeRF: Rethinking Dynamic NeRF with Kalman Filter [49.85369344101118]
  We introduce KFD-NeRF, a novel dynamic neural radiance field integrated with an efficient and high-quality motion reconstruction framework based on Kalman filtering. Our key idea is to model the dynamic radiance field as a dynamic system whose temporally varying states are estimated based on two sources of knowledge: observations and predictions. Our KFD-NeRF demonstrates similar or even superior performance within comparable computational time and state-of-the-art view synthesis performance with thorough training.
  arXiv  Detail & Related papers  (2024-07-18T05:48:24Z)
• 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)
• Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations [59.84561168501493]
  The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes.
  arXiv  Detail & Related papers  (2022-07-12T17:07:46Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• 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)
• Explaining machine-learned particle-flow reconstruction [0.0]
  The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision. A graph neural network (GNN) model, known as the machine-learned particle-flow (MLPF) algorithm, has been developed to substitute the rule-based PF algorithm.
  arXiv  Detail & Related papers  (2021-11-24T23:20:03Z)
• PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer [55.936527926778695]
  Recent deep learning approaches focus on mining subtle r clues using convolutional neural networks with limited-temporal receptive fields. In this paper, we propose the PhysFormer, an end-to-end video transformer based architecture.
  arXiv  Detail & Related papers  (2021-11-23T18:57:11Z)
• MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks [0.0]
  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.
  arXiv  Detail & Related papers  (2021-01-21T12:47:54Z)
• Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning [4.666011151359189]
  We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. We propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples.
  arXiv  Detail & Related papers  (2020-10-05T07:48:45Z)
• Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector [99.19847674810079]
  Fuzzy C-Means (FCM) is one of the most frequently used methods to construct information granules. In this paper, we augment the FCM-based degranulation mechanism by introducing a vector of fuzzification factors. Experiments completed for both synthetic and publicly available datasets show that the proposed approach outperforms the generic data reconstruction approach.
  arXiv  Detail & Related papers  (2020-04-13T04:17:30Z)
• Learning to Simulate Complex Physics with Graph Networks [68.43901833812448]
  We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains. Our framework---which we term "Graph Network-based Simulators" (GNS)--represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.
  arXiv  Detail & Related papers  (2020-02-21T16:44:28Z)

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.