Large Hadron Collider
PulseAugur coverage of Large Hadron Collider — every cluster mentioning Large Hadron Collider across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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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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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…