Particle physics DL-simulation with control over generated data properties

• URL: http://arxiv.org/abs/2405.14049v1
• Date: Wed, 22 May 2024 22:39:29 GMT
• Title: Particle physics DL-simulation with control over generated data properties
• Authors: Karol Rogoziński, Jan Dubiński, Przemysław Rokita, Kamil Deja,
• Abstract summary: The research of innovative methods aimed at reducing costs and shortening the time needed for simulation has been sparked by the development of collision simulations at CERN. Deep learning generative methods including VAE, GANs and diffusion models have been used for this purpose. This work aims to mitigate this issue, by providing an alternative solution to currently employed algorithms by introducing the mechanism of control over the generated data properties.
• Score: 3.2686289567336235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The research of innovative methods aimed at reducing costs and shortening the time needed for simulation, going beyond conventional approaches based on Monte Carlo methods, has been sparked by the development of collision simulations at the Large Hadron Collider at CERN. Deep learning generative methods including VAE, GANs and diffusion models have been used for this purpose. Although they are much faster and simpler than standard approaches, they do not always keep high fidelity of the simulated data. This work aims to mitigate this issue, by providing an alternative solution to currently employed algorithms by introducing the mechanism of control over the generated data properties. To achieve this, we extend the recently introduced CorrVAE, which enables user-defined parameter manipulation of the generated output. We adapt the model to the problem of particle physics simulation. The proposed solution achieved promising results, demonstrating control over the parameters of the generated output and constituting an alternative for simulating the ZDC calorimeter in the ALICE experiment at CERN.

Related papers

• Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN [3.2686289567336235]
  We present an innovative deep learning simulation approach tailored for the proton Zero Degree Calorimeter in the ALICE experiment. Our method offers a significant speedup when comparing to the traditional Monte-Carlo based approaches.
  arXiv  Detail & Related papers  (2024-06-05T13:41:09Z)
• Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN [3.2686289567336235]
  In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Recent advancements highlight the efficacy of diffusion models as state-of-the-art generative machine learning methods. We present the first simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on diffusion models, achieving the highest fidelity compared to existing baselines.
  arXiv  Detail & Related papers  (2024-06-05T13:11:53Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN [8.980453507536017]
  Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods. We propose an alternative approach to the problem that leverages machine learning.
  arXiv  Detail & Related papers  (2023-06-23T16:45:46Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• ELSA -- Enhanced latent spaces for improved collider simulations [0.1450405446885067]
  Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.
  arXiv  Detail & Related papers  (2023-05-12T18:00:03Z)
• Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data [0.0]
  This work advances the recently developed PIESRGAN modeling approach to turbulent premixed combustion. The resulting model provides good results for a priori and a posteriori tests on direct numerical simulation data of a fully turbulent premixed flame kernel.
  arXiv  Detail & Related papers  (2022-10-28T15:27:46Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)
• Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations [58.720142291102135]
  Dynamic Mode Decomposition (DMD) is a powerful data-driven method used to extract coherent schemes. This paper proposes a strategy to enable DMD to extract from observations with different mesh topologies and dimensions.
  arXiv  Detail & Related papers  (2021-04-28T22:14:25Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)

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.