high energy physics
PulseAugur coverage of high energy physics — every cluster mentioning high energy physics across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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Symbolic regression automates function discovery for high energy physics data
Researchers have developed a new method using symbolic regression to automatically discover parametric functions for modeling high energy physics (HEP) data. This approach automates the previously manual and intuitive p…
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Quantum Autoencoders Accelerated on FPGAs for Real-Time Anomaly Detection
Researchers have developed a method to accelerate quantum autoencoder models for real-time anomaly detection in high energy physics experiments. These models, capable of processing complex collider data, were synthesize…
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Machine Learning applied to high-energy physics fits in new lecture notes
Researchers have developed new lecture notes detailing the application of Machine Learning (ML) surrogates for statistical fits in high-energy physics. These notes outline a comprehensive ML workflow, including the use …
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New AI agent automates complex lattice QCD research workflows
Researchers have developed LQCDMaster, an agentic scientific computing tool designed to automate lattice quantum chromodynamics (LQCD) research. This system converts natural-language research tasks into executable PyQUD…
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Machine learning framework enhances parameter inference in physics and cosmology
Researchers have developed a new machine learning framework to emulate complex likelihood landscapes in high energy physics and cosmology. This framework utilizes XGBoost to efficiently explore high-dimensional paramete…
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Machine learning enhances data reconstruction for silicon sensors in high energy physics
Researchers have developed machine learning techniques to improve data reconstruction and compression for resistive silicon sensors used in high energy physics. The study explores recurrent neural networks, specifically…
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Graph Neural Networks applied to optimization and physics problems · 2 sources tracked
Researchers are exploring the application of graph neural networks (GNNs) beyond their traditional roles in combinatorial optimization and theoretical physics. One study demonstrates that GNNs can function as effective …
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Factorizable Normalizing Flows introduced for parameter-dependent density morphing · 2 sources tracked
Researchers have introduced Factorizable Normalizing Flows (FNFs), a novel method designed to model how probability densities change with continuous parameters. This approach addresses the intractability of learning sep…
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HEPTv2 Transformer Achieves State-of-the-Art in Particle Reconstruction
Researchers have developed HEPTv2, an end-to-end point-transformer architecture designed for efficient charged particle reconstruction in high-energy physics. This new model bypasses traditional graph construction and a…
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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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Neural networks in physics are vulnerable to hidden systematic errors
Researchers have identified a significant vulnerability in neural network models used for high-energy physics analyses. These models, while powerful, can be systematically misled by subtle input perturbations that remai…
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New frameworks enable asynchronous human-AI collaboration in complex scientific workflows
Researchers have developed HepScript, a Domain-Specific Language (DSL) designed to facilitate human-AI collaboration in high-energy physics data analysis. This DSL abstracts complex analysis logic into a formal syntax t…
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New physics framework links information geometry, jet substructure, and hypergraphs
Researchers have introduced a novel framework that bridges information geometry with jet substructure analysis in high-energy physics. This work demonstrates a triality between cumulant tensors, energy correlators, and …