Large Hadron Collider
PulseAugur coverage of Large Hadron Collider — every cluster mentioning Large Hadron Collider across labs, papers, and developer communities, ranked by signal.
7 day(s) with sentiment data
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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 …
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New PLuM Architecture Enhances Jet Taggers with Multimodal Physics Data
Researchers have developed a new multimodal architecture called PLuM that combines particle constituents with Lund plane splittings for improved jet tagging in high-energy physics. This approach processes both types of …
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Hyper-Graph Neural Networks enhance LHC particle collision analysis
Researchers have developed a Hyper-Graph Neural Network (H-GNN) to improve the detection of $tar{t}tar{t}$ production at the Large Hadron Collider. This advanced neural network architecture represents events as hyperg…
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New neural inference method targets Higgs self-coupling at LHC
Researchers have developed a novel neural simulation-based inference (NSBI) approach to determine the Higgs trilinear self-coupling. This method combines the efficiency of matrix-element-enhanced techniques with the pra…
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LLM agents struggle with scientific reasoning; Cerebras IPO challenges Nvidia
A new benchmark, Collider-Bench, has been developed to evaluate the ability of large language model agents to reproduce scientific analyses from research papers, specifically focusing on Large Hadron Collider (LHC) data…
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New SNAC-Pack automates neural architecture co-design for FPGAs
Researchers have developed SNAC-Pack, an open-source framework designed to automate the co-design of neural architectures and their deployment on FPGAs. This package employs a multi-objective global search strategy comb…
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Transfer learning boosts AI model efficiency in high-energy physics
Researchers have explored transfer learning techniques to improve machine learning model performance in high-energy physics. By pre-training models on computationally cheaper, fast-simulated data and then adapting them …
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AI researchers compare explainability methods for jet tagging in particle physics
Researchers have developed and compared three explainable AI (XAI) methods—GNNExplainer, GNNShap, and GradCAM—to understand the predictions of graph neural networks used in jet tagging at the Large Hadron Collider. The …
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jBOT uses self-distillation to cluster jet representations for LHC data
Researchers have developed jBOT, a novel self-supervised learning method for analyzing particle physics data from the CERN Large Hadron Collider. This technique utilizes self-distillation, combining local and global dis…
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New HGQ-LUT and da4ml methods speed up DNN training and FPGA deployment
Researchers have developed HGQ-LUT, a new method for training lookup-table (LUT) based neural networks that significantly speeds up the training process, making it over 100 times faster on modern GPUs. This approach int…