CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation

• URL: http://arxiv.org/abs/2305.04847v2
• Date: Mon, 26 Feb 2024 15:27:26 GMT
• Title: CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Authors: Erik Buhmann, Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Kr\"uger, Peter McKeown
• Abstract summary: Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. This work achieves a major breakthrough by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) Using recent improvements in generative modeling we apply a diffusion model to generate photon showers as high-cardinality point clouds. ii) These point clouds of up to $6,000$ space points are largely geometry-independent as they are down-sampled from initial even higher-resolution point clouds of up to $40,000$ so-called Geant4 steps. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions.

Related papers

• Geometric Trajectory Diffusion Models [58.853975433383326]
  Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
  arXiv  Detail & Related papers  (2024-10-16T20:36:41Z)
• A Comprehensive Evaluation of Generative Models in Calorimeter Shower Simulation [0.0]
  "Fast Simulation" has been pivotal in overcoming computational bottlenecks. The use of deep-generative models has sparked a surge of interest in surrogate modeling for detector simulations. Our evaluation revealed that the CaloDiffusion and CaloScore generative models demonstrate the most accurate simulation of particle showers.
  arXiv  Detail & Related papers  (2024-06-08T11:17:28Z)
• CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation [0.0]
  Generative machine learning models have been shown to speed up and augment the traditional simulation chain in physics analysis. A major advancement is the recently introduced CaloClouds model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD) In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for a 25-step sampling with comparable fidelity to CaloClouds while yielding a $6times$ speed-up over Geant4 on a single CPU.
  arXiv  Detail & Related papers  (2023-09-11T18:00:02Z)
• Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation [48.26243807950606]
  Two state-of-the-art score based models are trained on the same set of calorimeter simulation and directly compared. generative models are a new class of generative models that have been shown to accurately generate high dimensional calorimeter datasets.
  arXiv  Detail & Related papers  (2023-07-10T08:20:45Z)
• StarNet: Style-Aware 3D Point Cloud Generation [82.30389817015877]
  StarNet is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network. Our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks.
  arXiv  Detail & Related papers  (2023-03-28T08:21:44Z)
• MeshDiffusion: Score-based Generative 3D Mesh Modeling [68.40770889259143]
  We consider the task of generating realistic 3D shapes for automatic scene generation and physical simulation. We take advantage of the graph structure of meshes and use a simple yet very effective generative modeling method to generate 3D meshes. Specifically, we represent meshes with deformable tetrahedral grids, and then train a diffusion model on this direct parametrization.
  arXiv  Detail & Related papers  (2023-03-14T17:59:01Z)
• Controllable Mesh Generation Through Sparse Latent Point Diffusion Models [105.83595545314334]
  We design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. Our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability.
  arXiv  Detail & Related papers  (2023-03-14T14:25:29Z)
• Geometry-aware Autoregressive Models for Calorimeter Shower Simulations [6.01665219244256]
  We develop a geometry-aware autoregressive model on a range of calorimeter geometries. This is a key proof-of-concept step towards building a model that can generalize to new unseen calorimeter geometries. Such a model can replace the hundreds of generative models used for calorimeter simulation in a Large Hadron Collider experiment.
  arXiv  Detail & Related papers  (2022-12-16T01:45:17Z)
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds [10.089611750812391]
  Most computationally expensive simulations involve calorimeter showers. Deep generative modelling has opened the possibility of generating realistic calorimeter showers orders of magnitude more quickly than physics-based simulation. We propose modelling calorimeter showers first by learning their manifold structure, and then estimating the density of data across this manifold.
  arXiv  Detail & Related papers  (2022-11-23T19:00:03Z)
• SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics [0.0]
  SUPA is an algorithm and software package for generating data by simulating simplified particle propagation, scattering and shower development in matter. The proposed simulator generates thousands of particle showers per second on a desktop machine, a speed up of up to 6 orders of magnitudes over Geant4.
  arXiv  Detail & Related papers  (2022-02-10T13:14:12Z)
• Photon detection probability prediction using one-dimensional generative neural network [62.997667081978825]
  We propose a one-dimensional generative model which efficiently generates features using an OuterProduct-layer. This model bypasses photon transport simulation and predicts the number of photons detected by particular photon detectors at the same level of detail as theGeant4simulation. This generative model can be used to quickly predict photon detection probability in huge liquid argon detectors like ProtoDUNE or DUNE.
  arXiv  Detail & Related papers  (2021-09-11T01:43:12Z)

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.