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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Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
OmniMol transfers a billion-jet pre-trained PET foundation model from HEP to molecular dynamics via an interaction-matrix attention bias, delivering strong performance on the oMol dataset with minimal fine-tuning and fast inference.
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 simulations and ATLAS open data.
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 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.
Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.
EasyScan_HEP 2 adds AI-agent interfaces to a HEP parameter scan framework for natural-language to .ini config translation and new sampler integration.
DNN classifiers with mass-dependent thresholds reduce expected 95% CL upper limits on H to mu tau cross sections by 36-46% versus collinear mass baseline, while a regression network improves mass resolution by up to 21%.
The report reviews progress since 2021 in fixed-order computations for LHC applications and identifies processes requiring missing higher-order corrections to match anticipated experimental precision.
A primer by six leading developers maps the full Monte Carlo chain (matrix elements, parton showers, hadronisation, detector simulation, tuning, analysis) and the computing and reproducibility issues that come with it.
A summary of expert opinions on AI's impact on the research-education environment in physics from a KITP discussion session.
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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Symbolic Classification-Enabled LHC Limits Online BSM Global Fits
Symbolic regression produces an approximate classifier for LHC exclusion limits that enables their direct inclusion during pMSSM global fits.
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OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers
OmniMol transfers a billion-jet pre-trained PET foundation model from HEP to molecular dynamics via an interaction-matrix attention bias, delivering strong performance on the oMol dataset with minimal fine-tuning and fast inference.
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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 simulations and ATLAS open data.
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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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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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What exactly did the Transformer learn from our physics data?
Transformers trained on cosmic ray simulations learn physically plausible features in positional encodings for symmetric air showers and in attention mechanisms for galaxy-origin particles.
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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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Deep Neural Networks for Heavy Lepton-Flavor-Violating Higgs Searches at the LHC
DNN classifiers with mass-dependent thresholds reduce expected 95% CL upper limits on H to mu tau cross sections by 36-46% versus collinear mass baseline, while a regression network improves mass resolution by up to 21%.
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Les Houches 2023 -- Physics at TeV Colliders: Report on the Standard Model Precision Wishlist
The report reviews progress since 2021 in fixed-order computations for LHC applications and identifies processes requiring missing higher-order corrections to match anticipated experimental precision.
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The Monte Carlo Ecosystem in High-Energy Physics: A Primer
A primer by six leading developers maps the full Monte Carlo chain (matrix elements, parton showers, hadronisation, detector simulation, tuning, analysis) and the computing and reproducibility issues that come with it.
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AI and the Research-Education Environment of Physics
A summary of expert opinions on AI's impact on the research-education environment in physics from a KITP discussion session.