AtlFast3: the next generation of fast simulation in ATLAS

Abstract

The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with \textsc{Geant4} is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation -- the calorimeter shower simulation -- with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.

75 pages in total, author list starting page 59, 42 figures, 6 tables. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SIMU-2018-04/

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• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
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• Calomplification -- The Power of Generative Calorimeter Models
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• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• Deep generative models for fast photon shower simulation in ATLAS
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• Towards a Deep Learning Model for Hadronization
• The MadNIS Reloaded
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• CaloFlow for CaloChallenge Dataset 1
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
• ATLAS searches for additional scalars and exotic Higgs boson decays with the LHC Run 2 dataset
• Precision-Machine Learning for the Matrix Element Method
• Normalizing Flows for High-Dimensional Detector Simulations
• A Portable Parton-Level Event Generator for the High-Luminosity LHC
• Calorimeter shower superresolution
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
• CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation
• A search for triple Higgs boson production in the $6b$ final state using $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
• Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation
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• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
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• CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
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• Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation
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• Set-Conditional Set Generation for Particle Physics
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• Study of Higgs boson pair production in the $HH\to b\overline{b}\gamma \gamma$ final state with 308 fb${}^{-1}$ of data collected at $\sqrt{s}=$ 13 TeV and 13.6 TeV by the ATLAS experiment
• Amplitude Uncertainties Everywhere All at Once
• BitHEP -- The Limits of Low-Precision ML in HEP
• Search for the Higgs boson decay to a $Z$ boson and a photon in $pp$ collisions at $\sqrt{s}=13$ TeV and $13.6$ TeV with the ATLAS detector
• Search for cascade decays of charged sleptons and sneutrinos in final states with three leptons and missing transverse momentum in $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector
• Extrapolating Jet Radiation with Autoregressive Transformers
• Fast Perfekt: Regression-based refinement of fast simulation
• Design of Detectors at the Electron Ion Collider with Artificial Intelligence
• Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses
• High Throughput Training of Deep Surrogates from Large Ensemble Runs
• Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations