FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Aadet al.(ATLAS), Comput
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SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
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.
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.
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.
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.
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
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].
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.
ATLAS reports on its Run 3 software infrastructure for data management, workflows, databases, validation, and physics analysis tools at the LHC.
citing papers explorer
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation
SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
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.
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Study of jet-induced hydro response in high-energy heavy-ion collisions with a flow-matching generative model
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.
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Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model
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.
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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
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.
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Amplitude Uncertainties Everywhere All at Once
Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.
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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
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].
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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
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.
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Software and computing for Run 3 of the ATLAS experiment at the LHC
ATLAS reports on its Run 3 software infrastructure for data management, workflows, databases, validation, and physics analysis tools at the LHC.