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Modern Machine Learning for LHC Physicists
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Modern Machine Learning for LHC Physicists
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Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data. In any case, it is crucial for young researchers to stay on top of this development and apply cutting-edge methods and tools to all LHC physics tasks. These lecture notes lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications. They start with an LHC-specific motivation and a non-standard introduction to neural networks and then cover classification, unsupervised classification, generative networks, data representations, and inverse problems. Three themes defining much of the discussion are statistically defined loss functions, uncertainties, and accuracy. To understand the applications, the notes include some aspects of theoretical LHC physics. All examples are chosen from particle physics publications of the last few years, and many of them come with corresponding tutorials.
Forward citations
Cited by 13 Pith papers
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Simplex Demixing: Disentangling Multiple Light-Flavor Jets at Colliders
Simplex demixing recovers T mutually irreducible jet-flavor topics from M mixed samples via the (T−1)-simplex geometry of a multi-category classifier, demonstrated on Pythia dijets.
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Neural Control Variates at LO and NLO
Signed neural control variates from normalizing flows, combined with neural importance sampling, reduce weight ranges and negative weights for LO and NLO phase-space integration and event generation.
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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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Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer that dynamically generates variable-multiplicity parton showers matching Monte Carlo references for non-global logarithm resummation in the large-Nc limit.
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Nested-GPT for variable-multiplicity parton showers: A case study in the resummation of non-global logarithms
Nested-GPT is an autoregressive Transformer surrogate that generates variable-multiplicity parton showers while enforcing ordered Markovian branching and matches reference Monte Carlo results for leading-log non-globa...
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Probing Neutral Triple Gauge Couplings via $ZZ$ Production at $e^+e^-$ Colliders with Machine Learning
Formulates nTGC form factors from dim-8 SMEFT operators compatible with electroweak symmetry breaking and applies machine learning to fermion angular distributions to probe new physics scales up to multi-TeV in ZZ pro...
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Agentic Re-Casting using Agentic Re-Simulations
An agentic AI system with a physicist in the loop re-casts an ATLAS ttZ measurement into a global top-quark SMEFT fit and recovers injected coloron Wilson coefficients in a repeatable benchmark.
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RooAgent: An LLM Agent for Root-Based High Energy Physics Analysis
RooAgent provides an LLM agent interface that translates natural-language prompts into calls to PyROOT analysis functions for high energy physics tasks, with support for multiple AI backends and tested on ZH simulatio...
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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.
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Explicit or Implicit? Encoding Physics at the Precision Frontier
On three precision classification tasks — reweighting-based unfolding, likelihood-ratio estimation, and weakly supervised anomaly detection — a Lorentz-equivariant transformer and a pretrained foundation model perform...
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Machine Learning in the 2HDM2S model for Dark Matter
A 2HDM extended by two real scalar singlets is scanned with evolutionary strategies to locate regions satisfying vacuum, unitarity, oblique-parameter, collider and dark-matter constraints.
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EasyScan_HEP 2: Agent-Ready Parameter Scans for High-Energy Physics
EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.
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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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