CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

• URL: http://arxiv.org/abs/2309.05704v2
• Date: Mon, 26 Feb 2024 15:15:02 GMT
• Title: CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Authors: Erik Buhmann, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Kr\"uger, and Peter McKeown
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion 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 $6\times$ speed-up over Geant4 on a single CPU ($5\times$ over CaloClouds). We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up. This constitutes the first application of consistency distillation for the generation of calorimeter showers.

Related papers

• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation [22.42342223406944]
  We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge. We study state-of-the-art generative models on four calorimeter shower datasets of increasing dimensionality.
  arXiv  Detail & Related papers  (2024-10-28T23:28:07Z)
• One-Step Diffusion Distillation through Score Implicit Matching [74.91234358410281]
  We present Score Implicit Matching (SIM) a new approach to distilling pre-trained diffusion models into single-step generator models. SIM shows strong empirical performances for one-step generators. By applying SIM to a leading transformer-based diffusion model, we distill a single-step generator for text-to-image generation.
  arXiv  Detail & Related papers  (2024-10-22T08:17:20Z)
• TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models [52.454274602380124]
  Diffusion models heavily depend on the time-step $t$ to achieve satisfactory multi-round denoising. We propose a Temporal Feature Maintenance Quantization (TFMQ) framework building upon a Temporal Information Block. Powered by the pioneering block design, we devise temporal information aware reconstruction (TIAR) and finite set calibration (FSC) to align the full-precision temporal features.
  arXiv  Detail & Related papers  (2023-11-27T12:59:52Z)
• 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)
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation [0.0]
  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.
  arXiv  Detail & Related papers  (2023-05-08T16:44:15Z)
• 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)
• Score-based Generative Models for Calorimeter Shower Simulation [2.0813318162800707]
  We introduce CaloScore, a score-based generative model for collider physics applied to calorimeter shower generation. CaloScore is the first application of a score-based generative model in collider physics and is able to produce high-fidelity calorimeter images for all datasets.
  arXiv  Detail & Related papers  (2022-06-17T18:01:02Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• Semi-Supervised Video Deraining with Dynamic Rain Generator [59.71640025072209]
  This paper proposes a new semi-supervised video deraining method, in which a dynamic rain generator is employed to fit the rain layer. Specifically, such dynamic generator consists of one emission model and one transition model to simultaneously encode the spatially physical structure and temporally continuous changes of rain streaks. Various prior formats are designed for the labeled synthetic and unlabeled real data, so as to fully exploit the common knowledge underlying them.
  arXiv  Detail & Related papers  (2021-03-14T14:28:57Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)

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.