Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

• URL: http://arxiv.org/abs/2010.01835v1
• Date: Mon, 5 Oct 2020 07:48:45 GMT
• Title: Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
• Authors: Cheng Chen, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini
• Abstract summary: 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.
• Score: 4.666011151359189
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train a neural network to model detector resolution effects as a transfer function acting on an analysis-specific set of relevant features, computed at generation level, i.e., in absence of detector effects. Based on this model, we propose a novel fast-simulation workflow that starts from a large amount of generator-level events to deliver large analysis-specific samples. The adoption of this approach would result in about an order-of-magnitude reduction in computing and storage requirements for the collision simulation workflow. This strategy could help the high energy physics community to face the computing challenges of the future High-Luminosity LHC.

Related papers

• Deep Generative Models for Ultra-High Granularity Particle Physics Detector Simulation: A Voyage From Emulation to Extrapolation [0.0]
  This thesis aims to overcome this challenge for the Pixel Vertex Detector (PXD) at the Belle II experiment. This study introduces, for the first time, the results of using deep generative models for ultra-high granularity detector simulation in Particle Physics.
  arXiv  Detail & Related papers  (2024-03-05T23:12:47Z)
• Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss [0.0]
  We discuss Lamarr, a framework to speed-up the simulation production parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Deep Generative Models powered by several algorithms and strategies are employed to effectively parameterize the high-level response of the single components of the LHCb detector.
  arXiv  Detail & Related papers  (2023-03-20T20:18:04Z)
• On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver [59.13397937903832]
  We introduce a deep learning-based corrector called Neural Vector (NeurVec) NeurVec can compensate for integration errors and enable larger time step sizes in simulations. Our experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability.
  arXiv  Detail & Related papers  (2022-08-07T09:02:18Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Towards Reliable Neural Generative Modeling of Detectors [0.45671221781968335]
  We discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. Results are based on the Geant4 simulation of the LHCb Cherenkov detector.
  arXiv  Detail & Related papers  (2022-04-21T08:14:24Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Deep Transformer Networks for Time Series Classification: The NPP Safety Case [59.20947681019466]
  An advanced temporal neural network referred to as the Transformer is used within a supervised learning fashion to model the time-dependent NPP simulation data. The Transformer can learn the characteristics of the sequential data and yield promising performance with approximately 99% classification accuracy on the testing dataset.
  arXiv  Detail & Related papers  (2021-04-09T14:26:25Z)
• 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)
• Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks [0.0]
  In this work, we demonstrate a novel approach to speed up the simulation of the Time Projection Chamber tracker of the MPD experiment at the NICA accelerator complex. Our method is based on a Generative Adrial Network - a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. To evaluate the quality of the proposed model, we integrate it into the MPD software stack and demonstrate that it produces high-quality events similar to the detailed simulator.
  arXiv  Detail & Related papers  (2020-12-08T17:57:37Z)
• Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction [79.81193813215872]
  We develop a hybrid (graph) neural network that combines a traditional graph convolutional network with an embedded differentiable fluid dynamics simulator inside the network itself. We show that we can both generalize well to new situations and benefit from the substantial speedup of neural network CFD predictions.
  arXiv  Detail & Related papers  (2020-07-08T21:23:19Z)

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.