Pith. sign in

REVIEW 19 cited by

AtlFast3: the next generation of fast simulation in ATLAS

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2109.02551 v3 pith:MCATSCWH submitted 2021-09-06 hep-ex

AtlFast3: the next generation of fast simulation in ATLAS

classification hep-ex
keywords simulationatlasatlfast3accurateeventseverexperimentfast
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 19 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Learning Standard Model structure from LHC data with Riemannian flow matching

    hep-ph 2026-07 conditional novelty 7.0

    ShellFlow, a Riemannian flow-matching transformer fed only on-shell and invariant-mass priors and ~8×10^8 recorded ATLAS events, reproduces the SM's dilepton resonances, Weinberg angle, and top/W mass peaks in a singl...

  2. Local Conformal Predictions for Calibrated Surrogates

    hep-ph 2026-07 unverdicted novelty 7.0

    FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.

  3. Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model

    quant-ph 2026-05 unverdicted novelty 7.0

    QFAN generates calorimeter shower images autoregressively with a fixed three-qubit variational circuit per block, reproducing per-pixel distributions, correlations, and total energy on simulators and IBM hardware.

  4. Search for new scalars via $X \rightarrow SH \rightarrow b\bar{b}b\bar{b}$ in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector

    hep-ex 2026-07 accept novelty 6.0

    No excess over background is found in ATLAS's first search for X→SH→4b, which sets 95% CL upper limits of 0.7 fb–2.6 pb on the production cross-section times branching ratio.

  5. SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation

    physics.ins-det 2026-06 unverdicted novelty 6.0

    SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.

  6. CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

    hep-ex 2026-06 conditional novelty 6.0

    A one-step generative model for calorimeter showers, using MeanFlow, a learned Gaussian-mixture prior, and a physics-constrained loss, matches diffusion-model quality at far fewer evaluations.

  7. Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model

    nucl-th 2026-05 conditional novelty 6.0

    A conditional flow-matching model trained on CoLBT-hydro reproduces marginal γ-jet medium-response hadron spectra in 0–10% Pb+Pb at 5.02 TeV with ~10⁶× speedup while preserving front and diffusion-wake statistics.

  8. Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model

    nucl-th 2026-05 unverdicted novelty 6.0

    A flow-matching generative model trained on CoLBT-hydro data conditionally generates marginal final-state hadron spectra from jet-induced hydro responses in 0-10% Pb+Pb collisions at 5.02 TeV, matching training data s...

  9. Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model

    quant-ph 2026-05 unverdicted novelty 6.0

    QFAN generates calorimeter shower images block-by-block with a reusable 3-qubit circuit, reproducing pixel distributions and correlations on simulators and IBM hardware as a proof of principle.

  10. Study of $t\bar{t}H$ and $tH$ production in the $H\to\tau\tau$ channel in $pp$ collisions at $\sqrt{s}=13$ TeV and 13.6 TeV with the ATLAS detector

    hep-ex 2026-07 conditional novelty 5.0

    Simultaneous measurement of ttH (mu=1.51) and tH (mu=-0.4) in fully hadronic H→tau tau final states at 13/13.6 TeV, consistent with the Standard Model.

  11. CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

    hep-ex 2026-06 unverdicted novelty 5.0

    Presents CaloTrilogy, a unified one-step generative model for high-granularity calorimeter showers that combines velocity field integration, learned priors, and physics losses to match SOTA quality.

  12. Search for a resonance decaying into a scalar particle and a Higgs boson in the final state with two bottom quarks and two photons with 199 fb$^{-1}$ of data collected at $\sqrt{s}$=13 and 13.6 TeV with the ATLAS detector

    hep-ex 2025-10 unverdicted novelty 5.0

    Search for resonant X -> S(bb) H(gamma gamma) production finds no significant excess and sets 95% CL limits on sigma*BR ranging from 9 fb to 0.06 fb over mass ranges 170-1000 GeV for X and 15-500 GeV for S.

  13. Improved analysis of non-resonant Higgs boson pair production in the $b\bar{b}\tau^+\tau^-$ final state with $196$ fb$^{-1}$ of data collected at $\sqrt{s}$ = 13 TeV and 13.6 TeV with the ATLAS detector

    hep-ex 2026-07 accept novelty 4.5

    ATLAS finds μ_HH = 2.6^{+1.4}_{-1.0} in bbττ with Run 2+3 data, 2.6σ over background-only, and first 3.5σ evidence for ZH in this final state.

  14. Search for a resonance decaying into a scalar particle and a Higgs boson in the final state with two bottom quarks and two photons with 199 fb$^{-1}$ of data collected at $\sqrt{s}$=13 and 13.6 TeV with the ATLAS detector

    hep-ex 2025-10 accept novelty 4.0

    No significant excess observed in search for X → S(bb)H(γγ); 95% CL limits on σ×BR set from 9 fb to 0.06 fb over m_X 170-1000 GeV and m_S 15-500 GeV in 199 fb^{-1} of ATLAS data.

  15. Amplitude Uncertainties Everywhere All at Once

    hep-ph 2025-08 unverdicted novelty 4.0

    Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.

  16. Study of Higgs boson pair production in the $HH \rightarrow b \overline{b} \gamma \gamma$ final state with 308 fb$^{-1}$ of data collected at $\sqrt{s} =$ 13 TeV and 13.6 TeV by the ATLAS experiment

    hep-ex 2025-07 accept novelty 4.0

    Updated ATLAS search for HH → bbγγ with 308 fb⁻¹ yields observed μ_HH = 0.9^{+1.4}_{-1.1}, 95% CL limit μ_HH < 3.7, and κ_λ in [-1.6, 6.6].

  17. Search for a leptoquark in events with a hadronically decaying $\tau$-lepton and missing transverse momentum using $pp$ collisions at $\sqrt{s}=13$ TeV with the ATLAS detector

    hep-ex 2026-06 unverdicted novelty 3.0

    No excess above Standard Model background is observed; 95% CL limits are set on couplings of the U1 vector leptoquark model for masses 1.5-3.0 TeV.

  18. Software and computing for Run 3 of the ATLAS experiment at the LHC

    hep-ex 2024-04 unverdicted novelty 2.0

    ATLAS reports on its Run 3 software infrastructure for data management, workflows, databases, validation, and physics analysis tools at the LHC.

  19. Machine Learning for Complex Instrument Design and Optimization

    physics.ins-det 2026-07 unverdicted novelty 1.0

    A review chapter summarizing machine-learning pipelines for complex instrument operations and design, with no new experimental or theoretical contribution.