arXiv Machine Learning
Machine Learning for Invisible Dark Boson Searches at the Electron-Ion Collider
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arXiv Machine Learning
Sep 14
Fast BIB simulation at a future Muon Collider with generative machine learning
The paper presents the first machine learning models for fast generation of beam‑induced background (BIB) in tracking detectors at a future Muon Collider. Two architectures are explored: a high‑fidelity tabular diffusion model and a faster circular spline flow model. Both produce BIB hits and tracks that closely match full simulation results, achieving over an order of magnitude speed‑up while requiring far less computational resources.
arXiv Machine Learning
Aug 31
Comparing Classical and Quantum Machine Learning for Regression in High Energy Physics Collision Data
The paper systematically compares four classical machine learning models—SVM, ANN, CNN, and LSTM—with their quantum equivalents—QSVM, QNN, QCNN, and QLSTM—on simulated proton‑proton collision data from CERN Open Data. Classical models, especially CNN and LSTM, slightly outperform the quantum models under current hardware and dataset constraints, but quantum models achieve comparable accuracy with far fewer trainable parameters; for example, the QCNN matches a deep classical CNN using only four qubits and a depth‑three circuit. The study also shows that the regression task is non‑trivial for shallow polynomial fits, underscoring the relevance of the architectural comparison. whyItMatters":"The work provides a realistic benchmark of classical versus quantum machine learning performance on high‑energy physics data, highlighting parameter‑efficiency advantages of quantum models for near‑term devices."
arXiv Machine Learning
Jul 17
Lecture notes on Machine Learning applications for global fits
arXiv:2604. 07520v2 Announce Type: replace-cross Abstract: These lecture notes provide a comprehensive framework for performing global statistical fits in high-energy physics using modern Machine Learning (ML) surrogates.
arXiv Machine Learning
Jun 15
Machine-learned particle flow as a foundation model for collider physics
arXiv:2606. 14373v1 Announce Type: cross Abstract: The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks.
arXiv AI
Sep 17
Learning Nuclear Structure with AI: Radii and Collectivity
The paper presents NuCLR, a multi-task neural network that learns nuclear data representations to predict charge radii and electric‑quadrupole transition strengths across hundreds of nuclides. Using held‑out ensembles, the model achieves a charge‑radius RMS deviation of 0.0147 fm and a B(E2) RMS deviation of 0.192 e²b², comparable to leading nuclear models. The authors provide error bars indicating where additional experimental data could improve predictions, positioning NuCLR as a data‑driven surveyor of nuclear structure.
arXiv Machine Learning
Aug 17
Pairton: Iterative Reconstruction of Short-Lived Particles
arXiv:2608. 14278v1 Announce Type: cross Abstract: We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events.