Large Hadron Collider
PulseAugur coverage of Large Hadron Collider — every cluster mentioning Large Hadron Collider across labs, papers, and developer communities, ranked by signal.
- 2026-06-29 research_milestone The Large Hadron Collider is being upgraded to the High-Luminosity LHC, which will deliver ten times the luminosity. source
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New lightweight foundation model NEXUS adapts collider physics AI for broader scientific use
Researchers have developed NEXUS, a lightweight foundation model designed for collider physics that utilizes pre-trained learning from the Large Hadron Collider. This model, with approximately 3 million parameters, demo…
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New ML framework 'Simplex Demixing' tackles jet flavor identification at LHC
Researchers have developed a new machine-learning framework called "simplex demixing" to address the long-standing challenge of identifying multiple jet flavors in collider physics. This method allows for the extraction…
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AI model learns Standard Model physics directly from LHC collision data
Researchers have developed a transformer-based generative model called ShellFlow that can learn the structure of the Standard Model of particle physics directly from data collected at the Large Hadron Collider. This mod…
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New methods forecast generative amplification in LHC simulations
Researchers have developed two new methods to estimate the statistical precision of generative networks used in Large Hadron Collider (LHC) simulations. These methods, 'averaging amplification' and 'differential amplifi…
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New Large Tabular Models Aim to Conquer Structured Data Challenges
A new category of AI models, known as large tabular models (LTMs), is emerging to address the limitations of current large language models (LLMs) in analyzing structured data. While LLMs excel at text and image generati…
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New framework analyzes neural network training for PDF fitting
Researchers have developed a theoretical framework using the Neural Tangent Kernel (NTK) to analyze the training dynamics of neural networks used in Parton Distribution Function (PDF) fitting. This approach offers an an…
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AI enables brain-to-word communication; LHC shuts down; e-scooters defy UK ban
A new technology has been developed that enables communication through brain waves without the need for surgery. This innovation utilizes AI to interpret brain signals and translate them into words, potentially revoluti…
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Large Hadron Collider upgraded to High-Luminosity successor
The Large Hadron Collider (LHC) is undergoing a significant upgrade to its successor, the High-Luminosity LHC (HL-LHC). While largely retaining its original structure, the enhanced machine is designed to deliver ten tim…
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Reinforcement Learning Optimizes Real-Time Triggers at Large Hadron Collider
Researchers have developed a novel application of reinforcement learning (RL) for real-time event filtering at the Large Hadron Collider (LHC). By adapting Group-Filtered Policy Optimization (GFPO) to streaming data, th…
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AI framework streamlines detector design optimization using distributed computing
Researchers have developed a new AI-assisted framework for optimizing detector designs, leveraging the Production and Distributed Analysis (PanDA) system. This framework integrates multi-objective Bayesian optimization …
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New ML algorithm Profile OmniFold enhances particle physics data correction
Researchers have developed a new machine learning algorithm called Profile OmniFold to improve the accuracy of unfolding, a process used in particle physics to correct measured data for detector effects. This new method…
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Reinforcement learning optimizes Large Hadron Collider triggers in real-time
Researchers have developed a reinforcement learning agent capable of optimizing trigger thresholds in real-time at the Large Hadron Collider. This system, adapted from Group-Filtered Policy Optimization (GFPO), aims to …
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Dark matter search expands as neutrino interference clouds WIMP detection
Physicists are broadening their search for dark matter as current experiments, like those using liquid xenon detectors deep underground, are increasingly detecting neutrinos instead of the elusive particles. This "neutr…
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New research explores adversarial methods for neural network analysis
Researchers have developed new methods for understanding and manipulating neural networks. One approach, Adversarial Dependence Minimization (ADM), uses an adversarial game to create statistically independent feature re…
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Transformer models optimized for jet tagging on AMD Versal AI Engine
Researchers have developed a method to deploy transformer models for jet tagging on the AMD Versal AI Engine, a component of the Large Hadron Collider's trigger system. This approach quantizes the models to use only int…
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Transformer models optimized for CERN jet tagging on AMD AI Engines
Researchers have developed a method to deploy transformer models for jet tagging on the AMD Versal AI Engine, a task crucial for the CERN Large Hadron Collider's trigger systems. This approach involves a quantized, inte…
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New self-supervised method enhances jet tagging in high-energy physics
Researchers have developed JetParticle-JEPA (JP-JEPA), a novel self-supervised learning method for jet tagging in high-energy physics. This approach, built on a Particle Transformer, learns meaningful representations di…
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SNAC-Pack automates neural architecture search for FPGAs
Researchers have developed SNAC-Pack, an open-source framework designed to automate the process of neural architecture search (NAS) specifically for FPGAs. This package addresses the limitations of existing NAS methods …
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Quantum-Inspired Methods Boost Machine Learning Representations
Researchers have developed new methods to enhance machine learning models by integrating quantum computing principles. One approach, QUIVER, uses quantum Fisher views to capture higher-order correlations in data, improv…
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Machine Learning Enhances Dark Matter Detection at LHC
Researchers have developed a machine learning approach to enhance the detection of dark matter candidates at the Large Hadron Collider (LHC). This method specifically targets WIMP dark matter within the Next-to-Minimal …