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.
hub Tool reference
DELPHES 3, A modular framework for fast simulation of a generic collider experiment
Tool reference. 82% of classified Pith citations use this work as a method, library, or software dependency, not as a substantive claim.
35
Pith papers citing it
2,080
external citations · Pith
Method reference
82% of classified citations
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.
hub tools
citation-role summary
method 14
background 2
baseline 1
citation-polarity summary
representative citing papers
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.
MadGraph5_aMC@NLO automates tree-level, NLO, shower-matched, and merged cross-section computations for collider processes in a unified flexible framework.
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.
No significant excess is observed in leptonic W/Z plus high-multiplicity soft-particle events, setting limits on Higgs to SUEP decays across a range of model parameters.
Higgsformer achieves AUC 0.855 on t tbar H vs t tbar classification from raw hits, matching a Delphes-based Particle Transformer at ~40% b-tagging efficiency.
Incorporating timing information from time-dependent new physics signals can improve LHC search sensitivity by up to a factor of two compared to standard time-invariant analyses.
A combined kitchen sink observable set of Energy Flow Polynomials and subjettiness variables outperforms standard baselines in sensitivity to a wide range of resonant signals, with new public benchmarks released and an attribute bagging variant reducing training cost.
LHC vector boson fusion searches enhanced by machine learning can probe substantial regions of cosmologically viable parameter space for freeze-in dark matter mediated by a spin-2 particle.
An LSTM model trained on simulated jet substructure learns to predict true jet energy loss and distinguishes quenching signatures even after realistic detector effects are applied.
Recast LHC searches yield a ~1.2 TeV lower bound on long-lived charged dark mesons and show that anomaly-driven diboson resonances can reconstruct UV parameters like dark flavor and color numbers from IR measurements.
Composite Higgs models with SU(2)_L × SU(2)_R predict spin-1 resonances mixing with electroweak bosons that remain viable at the LHC down to masses of about 1.5 TeV.
The paper identifies promising parameter regions for observing same-sign tetralepton events from charged Higgs pair and single production decaying to muons and heavy neutral leptons at μTRISTAN.
In the Type-III 2HDM, neutral and heavy charged flavor-violating Higgs decays can exceed 5 sigma significance at 300 fb^{-1} luminosity while the light charged mode is more background-limited.
A 10 TeV muon collider with 10 ab^{-1} could reach O(10^{-3}) sensitivity on top-quark FCNC couplings, improving current ATLAS/CMS bounds by more than an order of magnitude.
Parnassus faithfully reproduces the ALEPH detector response at event, jet, and particle levels for clean e+e- to Z to qqbar events.
XGBoost multivariate analysis extends the 5-sigma discovery reach for singly produced vector-like bottom quarks decaying via heavy neutral Higgs bosons to 1.6 TeV at the HL-LHC with 3 ab^{-1}.
The 10 TeV muon collider can set substantially stronger 95% CL limits on anomalous quartic gauge couplings than the LHC or projected future hadron colliders.
FCC-ee projections indicate at least 10 times better precision on the H to tau tau cross-section than the LHC through ZH and VBF channels plus improved tau reconstruction methods.
citing papers explorer
-
Proton Structure from Neural Simulation-Based Inference at the LHC
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.
-
Higgs Physics with the XFEL Compton $\boldsymbol{\gamma\gamma}$ Collider Concept at $\boldsymbol{\sqrt{s}=125}$ GeV
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: Benchmarking AI Agents with Particle Physics Analysis Reproduction
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.
-
From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs
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.
-
Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference
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.
-
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
MadGraph5_aMC@NLO automates tree-level, NLO, shower-matched, and merged cross-section computations for collider processes in a unified flexible framework.
-
Uncovering Hidden Systematics in Neural Network Models for High Energy Physics
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.
-
Low-Multiplicity Jets as Probes of GeV-Scale Light-Quark-Coupled Particles
A proposed LHC search using low-multiplicity jets plus a photon can extend sensitivity to GeV-scale particles that couple to light quarks.
-
Operator Identification in Charged Lepton-Flavor Violation: Global EFT Analysis with RG Evolution, Polarization Observables, and Bayesian Model Discrimination at Future Colliders
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.
-
Search for soft unclustered energy patterns produced in association with a W or Z boson in proton-proton collisions at $\sqrt{s}$ = 13 TeV
No significant excess is observed in leptonic W/Z plus high-multiplicity soft-particle events, setting limits on Higgs to SUEP decays across a range of model parameters.
-
Hits to Higgs: Hit-Level Higgs Classification from Raw LHC Detector Data Using Higgsformer
Higgsformer achieves AUC 0.855 on t tbar H vs t tbar classification from raw hits, matching a Delphes-based Particle Transformer at ~40% b-tagging efficiency.
-
Time-dependent signals of new physics at the LHC
Incorporating timing information from time-dependent new physics signals can improve LHC search sensitivity by up to a factor of two compared to standard time-invariant analyses.
-
Kitchen Sink Anomaly Detection
A combined kitchen sink observable set of Energy Flow Polynomials and subjettiness variables outperforms standard baselines in sensitivity to a wide range of resonant signals, with new public benchmarks released and an attribute bagging variant reducing training cost.
-
Probing Freeze-In Dark Matter via a Spin-2 Portal at the LHC with Vector Boson Fusion and Machine Learning
LHC vector boson fusion searches enhanced by machine learning can probe substantial regions of cosmologically viable parameter space for freeze-in dark matter mediated by a spin-2 particle.
-
Validating a Machine Learning Approach to Identify Quenched Jets in Heavy-Ion Collisions
An LSTM model trained on simulated jet substructure learns to predict true jet energy loss and distinguishes quenching signatures even after realistic detector effects are applied.
-
Stopping Dark Mesons in Their Tracks with Long-Lived Particle and Resonant Signatures
Recast LHC searches yield a ~1.2 TeV lower bound on long-lived charged dark mesons and show that anomaly-driven diboson resonances can reconstruct UV parameters like dark flavor and color numbers from IR measurements.
-
Phenomenology of electroweak spin-1 resonances
Composite Higgs models with SU(2)_L × SU(2)_R predict spin-1 resonances mixing with electroweak bosons that remain viable at the LHC down to masses of about 1.5 TeV.
-
Same-Sign Tetralepton Signature at $\mu$TRISTAN
The paper identifies promising parameter regions for observing same-sign tetralepton events from charged Higgs pair and single production decaying to muons and heavy neutral leptons at μTRISTAN.
-
Probing Flavor-Violating Higgs Decays in the Type-III Two-Higgs-Doublet Model at the LHC and HL-LHC
In the Type-III 2HDM, neutral and heavy charged flavor-violating Higgs decays can exceed 5 sigma significance at 300 fb^{-1} luminosity while the light charged mode is more background-limited.
-
Sensitivity to top-quark FCNC interactions at future muon colliders
A 10 TeV muon collider with 10 ab^{-1} could reach O(10^{-3}) sensitivity on top-quark FCNC couplings, improving current ATLAS/CMS bounds by more than an order of magnitude.
-
An AI-based Detector Simulation and Reconstruction Model for the ALEPH Experiment at LEP
Parnassus faithfully reproduces the ALEPH detector response at event, jet, and particle levels for clean e+e- to Z to qqbar events.
-
Probing Heavy Neutral Higgs Bosons via Single Vector-Like Bottom Quark Production at the HL-LHC
XGBoost multivariate analysis extends the 5-sigma discovery reach for singly produced vector-like bottom quarks decaying via heavy neutral Higgs bosons to 1.6 TeV at the HL-LHC with 3 ab^{-1}.
-
Constraints on Anomalous Quartic Gauge Couplings via $\gamma\gamma$ and $Z\gamma$ Vector Boson Scattering at Muon Colliders
The 10 TeV muon collider can set substantially stronger 95% CL limits on anomalous quartic gauge couplings than the LHC or projected future hadron colliders.
-
Projections of H$\to\tau\tau$ cross-section at FCC-ee
FCC-ee projections indicate at least 10 times better precision on the H to tau tau cross-section than the LHC through ZH and VBF channels plus improved tau reconstruction methods.
-
Octet scalars shaping LHC distributions in 4-jet final states
A color-octet scalar Θ pair-produced via gluons and decaying to quark pairs can account for the CMS excess in equal-mass dijet pairs at 0.95 TeV with a 65 fb cross section for the real case.
-
Interpretation of LHC excesses at 95 GeV and 152 GeV in an extended Georgi-Machacek model
A minimally extended Georgi-Machacek model accommodates the LHC excesses at 95 GeV and 152 GeV while preserving the 125 GeV Higgs signal and the electroweak rho parameter near unity.
-
Prospects of searches for invisible $B$-meson decays at FCC-ee
With 6×10^{12} Z bosons at FCC-ee, invisible B-meson branching fractions above 7.6×10^{-9} could be excluded at 90% CL using rectangular cuts and a multiclass BDT on simulated signal and background.
-
Lepton flavor violating top quark FCNC processes at the $\mu$TRISTAN
Projected constraints on four-fermion operators for μ e → t q at 346 GeV improve current LHC bounds by roughly an order of magnitude at 100 fb⁻¹ and more at 1 ab⁻¹.
-
USMEFT as a tool for discovery of universal new physics at high luminosity LHC
Universal SMEFT fits to pseudo-data from neutral and charged Drell-Yan processes at HL-LHC can detect universal new physics and extract its properties stably across EFT truncation orders.
-
Possibility of Probing an Extra Higgs Boson at the Compact Linear Collider
CLIC can probe an additional neutral Higgs boson H in the Two Higgs Doublet Model through the process e+e- to H nu nu-bar with H decaying to WW in the dilepton channel.
-
Jarvis-HEP: A lightweight Python framework for workflow composition and parameter scans in high-energy physics
Jarvis-HEP introduces a YAML-based Python framework for composing workflows and performing parameter scans in high-energy physics.
-
Searches for light exotic scalar decays at the e$^+$e$^-$ Higgs factory
Expected cross-section limits for light exotic scalars in bb, tau+tau-, and invisible channels are derived from ILD full simulation and Delphes fast simulation for 250 GeV ILC running.
-
Machine Learning Study on Single Production of a Singlet Vector-like Lepton at the Large Hadron Collider
XGBoost machine learning improves discrimination in LHC searches for singlet vector-like leptons, yielding projected 2σ mass exclusion limits of 620 GeV (three-lepton) and 490 GeV (four-lepton) at 14 TeV with 3000 fb^{-1}.
-
Doubly charged Higgs production within the Higgs triplet model at future electron-positron colliders
CLIC offers superior discovery potential for doubly charged Higgs bosons in the Higgs triplet model compared to HL-LHC, reaching masses up to 1.2 TeV in gauge-like scenarios and high significance in Yukawa-like regions via same-sign lepton decays.
-
Open LHC Monte Carlo Event Generation
A review of initiatives to make LHC Monte Carlo event generations available as open data to minimize redundant simulations and resource use.