Spectral-function features fed to a dense network improve expected gluino-mass reach by ~150 GeV over ATLAS Gtt-0L-C and ~250 GeV over kinematics-only ML for the fully hadronic channel.
Searching for Exotic Particles in High-Energy Physics with Deep Learning
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abstract
Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine learning approaches are often used. Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Using benchmark datasets, we show that deep learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8\% over the best current approaches. This demonstrates that deep learning approaches can improve the power of collider searches for exotic particles.
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Proof-of-concept for NLO matrix element method via POWHEG projections applied to fully leptonic WW production in SMEFT, demonstrating near-optimal classification of BSM versus SM events using lepton correlations.
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.
Nyström approximation of MMD enables scalable two-sample testing with permutation p-values and a finite-sample power bound matching the minimax optimal separation rate.
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
Graph neural network achieves AUC of 0.883 for up versus anti-up quark jet charge discrimination in controlled QCD simulations.
Machine learning models trained on known hadron data and an extended Gürsey-Radicati mass formula predict masses for triply heavy baryons and numerous pentaquark states, agreeing with available data and forecasting unobserved states.
The document reports the first year of activity of the VBSCan COST Action network on vector-boson scattering phenomenology and experiments from a 2018 workshop.
citing papers explorer
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Machine learning fully hadronic events with spectral functions
Spectral-function features fed to a dense network improve expected gluino-mass reach by ~150 GeV over ATLAS Gtt-0L-C and ~250 GeV over kinematics-only ML for the fully hadronic channel.
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Matrix element method at NLO: A fine proof of concept in POWHEG
Proof-of-concept for NLO matrix element method via POWHEG projections applied to fully leptonic WW production in SMEFT, demonstrating near-optimal classification of BSM versus SM events using lepton correlations.
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Probing lepton flavor mixing in $W_R$ searches with machine learning at the LHC
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.
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A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations
Nyström approximation of MMD enables scalable two-sample testing with permutation p-values and a finite-sample power bound matching the minimax optimal separation rate.
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Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noise contamination.
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Application of Deep Learning to Jet Charge Discrimination
Graph neural network achieves AUC of 0.883 for up versus anti-up quark jet charge discrimination in controlled QCD simulations.
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Comprehensive Mass Predictions: From Triply Heavy Baryons to Pentaquarks
Machine learning models trained on known hadron data and an extended Gürsey-Radicati mass formula predict masses for triply heavy baryons and numerous pentaquark states, agreeing with available data and forecasting unobserved states.
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VBSCan Thessaloniki 2018 Workshop Summary
The document reports the first year of activity of the VBSCan COST Action network on vector-boson scattering phenomenology and experiments from a 2018 workshop.