Copertina

Neutrino Reconstruction in TRIDENT Based on Graph Neural Network

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Mo, Cen, Zhang, Fuyudi, Li, Liang
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	High Energy Physics - Experiment Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Probing Dark Matter annihilation in the Galactic Centre with TRIDENT
di: Wang, Yingwei, et al.
Pubblicazione: (2026)

The Light Source of the TRIDENT Pathfinder Experiment
di: Li, Wenlian, et al.
Pubblicazione: (2023)

A Cost Effective Optimization of the hybrid-DOM Design for TRIDENT
di: Shao, Hengbin, et al.
Pubblicazione: (2025)

Probing SMEFT Operators through $t\bar{t}t\bar{t}$ Production with Hyper-Graph Neural Networks at the LHC
di: Subba, Amir, et al.
Pubblicazione: (2026)

Hybrid-graph neural network method for muon fast reconstruction in neutrino telescopes
di: Mo, Cen, et al.
Pubblicazione: (2025)

First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference
di: Tame-Narvaez, Karla, et al.
Pubblicazione: (2026)

Guided Graph Compression for Quantum Graph Neural Networks
di: Casals, Mikel, et al.
Pubblicazione: (2025)

Improving Neutrino Oscillation Measurements through Event Classification
di: Ellis, Sebastian A. R., et al.
Pubblicazione: (2025)

Uncertainty Quantification From Scaling Laws in Deep Neural Networks
di: Elsharkawy, Ibrahim, et al.
Pubblicazione: (2025)

Graph Neural Network for Neutrino Physics Event Reconstruction
di: Hewes, V, et al.
Pubblicazione: (2024)

DeepQuark: A Deep-Neural-Network Approach to Multiquark Bound States
di: Wu, Wei-Lin, et al.
Pubblicazione: (2025)

Design and Evaluation of a PMT High-Voltage system for Deepsea Neutrino Telescope
di: Mao, Zhu, et al.
Pubblicazione: (2025)

Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques
di: Zhang, Yulei, et al.
Pubblicazione: (2024)

AI Agents, Language, Deep Learning and the Next Revolution in Science
di: Li, Ke, et al.
Pubblicazione: (2026)

NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes
di: Orsoe, Rasmus F., et al.
Pubblicazione: (2025)

Improving Neutrino Energy Reconstruction with Machine Learning
di: Kopp, Joachim, et al.
Pubblicazione: (2024)

Event Reconstruction for Radio-Based In-Ice Neutrino Detectors with Neural Posterior Estimation
di: Heyer, Nils, et al.
Pubblicazione: (2025)

Multi-Modal Track Reconstruction using Graph Neural Networks at Belle II
di: Reuter, Lea, et al.
Pubblicazione: (2026)

Optimising Underwater Neutrino Telescopes for All-Flavour Point Source Sensitivity
di: Morton-Blake, Iwan, et al.
Pubblicazione: (2025)

Variable Rate Neural Compression for Sparse Detector Data
di: Huang, Yi, et al.
Pubblicazione: (2024)

Atmospheric Neutrino Charged-Current Interactions at Large Liquid-Scintillator Detectors: I. Physics of Neutrino-Antineutrino Discrimination
di: He, Xinhai, et al.
Pubblicazione: (2026)

Refine Neutrino Events Reconstruction with BEiT-3
di: Li, Chen, et al.
Pubblicazione: (2023)

A case study of sending graph neural networks back to the test bench for applications in high-energy particle physics
di: Pfeffer, Emanuel, et al.
Pubblicazione: (2024)

Agentic AI -- Physicist Collaboration in Experimental Particle Physics: A Proof-of-Concept Measurement with LEP Open Data
di: Badea, Anthony, et al.
Pubblicazione: (2026)

Another Fit Bites the Dust: Conformal Prediction as a Calibration Standard for Machine Learning in High-Energy Physics
di: Araz, Jack Y., et al.
Pubblicazione: (2025)

Observation of a rare beta decay of the charmed baryon with a Graph Neural Network
di: BESIII Collaboration, et al.
Pubblicazione: (2024)

Surrogate Neural Architecture Codesign Package (SNAC-Pack)
di: Weitz, Jason, et al.
Pubblicazione: (2026)

Surrogate Neural Architecture Codesign Package (SNAC-Pack)
di: Weitz, Jason, et al.
Pubblicazione: (2025)

Entanglement and Bell Nonlocality in $τ^+ τ^-$ at the LHC using Machine Learning for Neutrino Reconstruction
di: Zhang, Yulei, et al.
Pubblicazione: (2025)

Jet Reconstruction with Mamba Networks in Collider Events
di: Li, Jinmian, et al.
Pubblicazione: (2025)

Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
di: Wang, Aaron, et al.
Pubblicazione: (2026)

End-to-end event reconstruction for precision physics at future colliders
di: Garcia, Dolores, et al.
Pubblicazione: (2026)

Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses
di: Huang, Yi, et al.
Pubblicazione: (2023)

eLog analysis for accelerators: status and future outlook
di: Sulc, Antonin, et al.
Pubblicazione: (2025)

GRACE: an Agentic AI for Particle Physics Experiment Design and Simulation
di: Hill, Justin, et al.
Pubblicazione: (2026)

Application of Neural Networks for the Reconstruction of Supernova Neutrino Energy Spectra Following Fast Neutrino Flavor Conversions
di: Abbar, Sajad, et al.
Pubblicazione: (2024)

Direct Vertex Reconstruction of $Λ$ Baryons from Hits in CLAS12 using Graph Neural Networks
di: Menkce, Keegan, et al.
Pubblicazione: (2025)

On Massive Neutrinos and Coherence in Neutrino Oscillations
di: Tureanu, Anca
Pubblicazione: (2025)

Exploring DHCAL design and performance with Graph Neural Networks
di: Borysova, M., et al.
Pubblicazione: (2024)

Neutrino Interaction Vertex Reconstruction in DUNE with Pandora Deep Learning
di: DUNE Collaboration, et al.
Pubblicazione: (2025)