Neural simulation-based inference on unbinned top-quark pair data at 13 TeV yields improved gluon PDF precision over traditional binned analyses while incorporating experimental and theoretical uncertainties.
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DELPHES 3, A modular framework for fast simulation of a generic collider experiment
Tool reference. 79% of classified Pith citations use this work as a method, library, or software dependency, not as a substantive claim.
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abstract
The version 3.0 of the DELPHES fast-simulation is presented. The goal of DELPHES is to allow the simulation of a multipurpose detector for phenomenological studies. The simulation includes a track propagation system embedded in a magnetic field, electromagnetic and hadron calorimeters, and a muon identification system. Physics objects that can be used for data analysis are then reconstructed from the simulated detector response. These include tracks and calorimeter deposits and high level objects such as isolated electrons, jets, taus, and missing energy. The new modular approach allows for greater flexibility in the design of the simulation and reconstruction sequence. New features such as the particle-flow reconstruction approach, crucial in the first years of the LHC, and pile-up simulation and mitigation, which is needed for the simulation of the LHC detectors in the near future, have also been implemented. The DELPHES framework is not meant to be used for advanced detector studies, for which more accurate tools are needed. Although some aspects of DELPHES are hadron collider specific, it is flexible enough to be adapted to the needs of electron-positron collider experiments.
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representative citing papers
RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
PLuM multimodal transformer improves top and H->bb jet tagging by jointly processing particle constituents and Lund plane splittings, yielding 25% higher background rejection at 25% di-Higgs efficiency.
An XFEL Compton gamma-gamma collider at 125 GeV with a set transformer deep learning classifier on particle-flow point clouds can achieve high-precision Higgs measurements across hadronic, semi-leptonic, and leptonic final states including H to strange quarks.
Collider-Bench is a new benchmark showing that current LLM agents cannot reliably reproduce LHC analyses at the level of a physicist-in-the-loop.
Higher-order Fisher tensors in exponential-family coordinates of binned energy correlators are simultaneously local KL coefficients, connected cumulants, and hyperedge weights, enabling hypergraph constructions for jet substructure analysis.
Template-Adapted Mixture Model uses many biased simulations for data-driven estimates of signal and background distributions, yielding unbiased signal fraction estimates with well-calibrated uncertainties.
Neural refinement of Monte Carlo sample weights via phase-space scaling and a new resampling protocol that maintains averages and uncertainties.
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The Minimum Resolution Likelihood method defines a fiducial signal region to convert ML-induced systematic effects into statistical uncertainties for unbiased signal strength estimation in collider analyses.
Parnassus releases a unified PyTorch framework offering flow-matching neural and Delphes-style parametric models for CMS, ATLAS, and ALEPH detector simulation that run on GPUs without ROOT.
Damped oscillations alleviate LN violation suppression for pseudo-Dirac HNLs, improving collider sensitivities and allowing distinction from the double-Majorana limit.
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Proof-of-principle for hadron-in-fat-jet AI tagging that yields an expected 95% CL limit of B(W±→π±γ) < 2.78×10^{-5} at 450 fb^{-1}.
DNN analysis of pp → WR → ℓNR → ℓℓjj at LHC Run 2 and HL-LHC improves exclusion limits on m_WR and m_NR for unmixed, maximal-mixing, and PMNS-like scenarios over cut-based methods and probes the |Ve1|–|Vμ1| plane.
Pointwise metrics compress marginal spectra in multimodal inverse problems, and a three-part protocol using CRPS, spectrum fidelity, and calibration reverses model rankings on synthetic and particle-physics benchmarks.
Neural networks for HEP tasks can be fooled at significant rates by subtle perturbations inside uncertainty envelopes, revealing hidden systematics not captured by conventional methods.
A proposed LHC search using low-multiplicity jets plus a photon can extend sensitivity to GeV-scale particles that couple to light quarks.
Global EFT analysis of charged lepton-flavor violation at future colliders incorporates RG running, polarization, and Bayesian discrimination to identify operators, finding 10-30% shifts from tree-level mappings at multi-TeV scales.
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A new model with SU(2)_D symmetry and vector-like muons mediates vector dark matter, simultaneously addressing relic abundance and muon g-2 while identifying an off-resonance suppression mechanism for light DM and deriving collider bounds.
Novel multilepton signatures from Higgs decays to a light pseudoscalar decaying to e-mu pairs in type-III 2HDM can set stronger limits on LFV couplings than low-energy experiments.
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Introduces pseudo-marginal MCMC with unbiased Poisson likelihood estimator for exact inference despite noisy collider Monte Carlo simulations.
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Lepton flavor violating top quark FCNC processes at the $\mu$TRISTAN
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USMEFT as a tool for discovery of universal new physics at high luminosity LHC
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Possibility of Probing an Extra Higgs Boson at the Compact Linear Collider
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Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
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Searches for light exotic scalar decays at the e$^+$e$^-$ Higgs factory
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Doubly charged Higgs production within the Higgs triplet model at future electron-positron colliders
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Event shapes and Inclusive Hadron Spectra at FCC-ee energies
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Search for Dark Matter in 2HDMS at LHC and future Lepton Colliders
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Open LHC Monte Carlo Event Generation
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Constraints on the Higgs-gluon effective coupling through $h \to \gamma\gamma$ decays at the LHC
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VBSCan Thessaloniki 2018 Workshop Summary
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The Monte Carlo Ecosystem in High-Energy Physics: A Primer
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