Journal Article

PUBDB-2024-04949

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png CaloClouds II: ultra-fast geometry-independent highly-granular calorimeter simulation

Buhmann, E. ; Gaede, F.DESY* ; Kasieczka, G.Extern* ; Korol, A. (Corresponding author)Extern*DESY* ; Korcari, W.Extern* ; Krüger, K.DESY* ; McKeown, P.DESY*

2024
Inst. of Physics London

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Please use a persistent id in citations: doi:10.1088/1748-0221/19/04/P04020  doi:10.3204/PUBDB-2024-04949

Report No.: DESY-23-130; arXiv:2309.05704

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 $46\times$ ($37\times$) speed-up. This constitutes the first application of consistency distillation for the generation of calorimeter showers.

Keyword(s): diffusion: model ; calorimeter: electromagnetic ; showers ; machine learning ; ILD detector ; numerical calculations ; programming ; numerical methods ; Simulation methods and programs ; Analysis and statistical methods ; Calorimeter methods

Note: 30 pages, 7 figures, 3 tables, code available at https://github.com/FLC-QU-hep/CaloClouds-2

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Contributing Institute(s):

1. Technol. zukünft. Teilchenph. Experim. (FTX)

Research Program(s):

1. 623 - Data Management and Analysis (POF4-623) (POF4-623)
2. AIDAinnova - Advancement and Innovation for Detectors at Accelerators (101004761) (101004761)
3. 05D23GU4 - Verbundprojekt 05D2022 - KISS: Künstliche Intelligenz zur schnellen Simulation von wissenschaftlichen Daten. Teilprojekt 1. (BMBF-05D23GU4) (BMBF-05D23GU4)
4. DFG project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe (390833306) (390833306)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2024

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Database coverage:
; ; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Essential Science Indicators ; IF < 5 ; JCR ; National-Konsortium ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Preprint Buhmann, E. (Corresponding author)Extern* ; Gaede, F.DESY* ; Kasieczka, G.Extern* ; Korol, A.Extern*DESY* ; Korcari, W.Extern* ; Krüger, K.DESY* ; McKeown, P.DESY*
CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
[10.3204/PUBDB-2023-05463]     Files  Fulltext by arXiv.org BibTeX | EndNote: XML, Text | RIS

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