Large Hadron Collider
PulseAugur coverage of Large Hadron Collider — every cluster mentioning Large Hadron Collider across labs, papers, and developer communities, ranked by signal.
- subsidiary of CERN 100%
- used by CMS experiment 90%
- instance of top quark 90%
- used by Standard Model 80%
- affiliated with CMS experiment 70%
- used by hls4ml 70%
- used by High Luminosity Large Hadron Collider 70%
- instance of Standard Model 60%
- affiliated with High Luminosity Large Hadron Collider 60%
- used by top quark 60%
- 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
5 day(s) with sentiment data
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Deep Sets model enhances jet rejection at Large Hadron Collider · 1 source tracked
Researchers have developed DIPz, a machine learning model utilizing Deep Sets to improve jet rejection in high-energy physics experiments. This model estimates the origin of jets by analyzing charged particle tracks, co…
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New ML method enhances particle search at Large Hadron Collider
Researchers have developed a new machine learning method called MASSIB (Model-Agnostic Search with Smearing and Imperfect Background) to improve the search for new particles at the Large Hadron Collider. This method can…
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Foundation models distilled for efficient AI applications in physics and recommendations
Researchers are exploring methods to distill large foundation models into smaller, more efficient versions for specific applications. One study focuses on distilling models for jet tagging at the Large Hadron Collider, …
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CERN and Mistral AI partner to advance AI for science
CERN and Mistral AI have formed a strategic partnership to advance artificial intelligence in scientific research. This collaboration aims to develop foundation models for scientific discovery, initially focusing on hig…
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CERN and Mistral AI partner on Large Physics Model for LHC data
CERN and Mistral AI have announced a strategic partnership to develop a Large Physics Model, an AI foundation model designed for scientific applications. This collaboration aims to leverage AI to handle the massive data…
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New AI method enhances anomaly detection for new physics at HL-LHC
Researchers have developed a new method for anomaly detection in particle physics experiments at the High Luminosity Large Hadron Collider (HL-LHC). This approach uses learned representations of collision events to iden…
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NYC AC batteries, US missile detection ends, and diverse science news roundup
This cluster aggregates several distinct science and technology news items. One article details New York City's free battery program for window air conditioning units, noting the popularity of Every Electric's batteries…
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New self-supervised pre-training method for LHC foundation models unveiled
Researchers have developed a novel self-supervised pre-training method for foundation models at the Large Hadron Collider (LHC). This data-driven approach utilizes the energy mover's distance (EMD) to pair events based …
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Optimal Transport Maps Calibrate ML Simulations for Particle Physics
Researchers have developed a novel calibration approach using optimal transport maps to address discrepancies between machine learning simulations and experimental data in particle physics. This method, applied to high-…
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New ML method speeds up uncertainty quantification for particle accelerators
Researchers have developed a novel empirical Bayes method to efficiently tune hyperparameters for flexible, heteroscedastic Spectral-normalized Neural Gaussian Processes. This technique significantly reduces computation…
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New AI method detects anomalies in particle collision data
Researchers have developed a new unsupervised anomaly detection method for dijet events in particle physics using neural spline flow density estimation. This technique trains a normalizing flow model on high-dimensional…
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LHC limits integrated into BSM physics global fits using symbolic regression
Researchers have developed a method to incorporate Large Hadron Collider (LHC) exclusion limits into online global fits for Beyond the Standard Model (BSM) physics. This approach uses symbolic regression to derive mathe…
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Vision Transformers outperform CNNs for jet classification at Large Hadron Collider
Researchers have explored the application of Vision Transformers (ViTs) for classifying quark-gluon jets using calorimeter images from the Large Hadron Collider. Their study, which utilized simulated CMS Open Data, cons…
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AI accelerates Large Hadron Collider detector simulations with Normalizing Flows
Researchers have developed a new method using fine-tuned Normalizing Flows (NFs) to speed up the simulation of particle detector responses at the Large Hadron Collider. This approach addresses the computational expense …
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Self-supervised ViT framework enhances neutrino detector analysis
Researchers have developed a new self-supervised pre-training framework using a sparse Vision Transformer (ViT) to create reusable representations for heterogeneous neutrino detectors. This approach, evaluated on simula…
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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…