Copertina

Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Barbetti, Matteo
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	High Energy Physics - Experiment Machine Learning Instrumentation and Detectors
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

The LHCb ultra-fast simulation option, Lamarr: design and validation
di: Anderlini, Lucio, et al.
Pubblicazione: (2023)

Refining fast simulation using machine learning
di: Bein, Samuel, et al.
Pubblicazione: (2023)

Applying generative neural networks for fast simulations of the ALICE (CERN) experiment
di: Wojnar, Maksymilian
Pubblicazione: (2024)

New simulation software technologies at the LHCb Experiment at CERN
di: Mazurek, Michal, et al.
Pubblicazione: (2021)

A universal vision transformer for fast calorimeter simulations
di: Favaro, Luigi, et al.
Pubblicazione: (2026)

Real-time monitoring of LHCb interaction region with a fast trackless methodology
di: Cordova, Giulio, et al.
Pubblicazione: (2025)

The LHCb upgrade I
di: LHCb collaboration, et al.
Pubblicazione: (2023)

The LHCb VELO Upgrade Module Construction
di: Akiba, K., et al.
Pubblicazione: (2024)

Computing and software for LHCb Upgrade II
di: LHCb Collaboration
Pubblicazione: (2025)

Technology developments for LHCb Upgrade II
di: LHCb collaboration
Pubblicazione: (2025)

Reweighting simulated events using machine-learning techniques in the CMS experiment
di: CMS Collaboration
Pubblicazione: (2024)

High-density gas target at the LHCb experiment
di: Garcia, O. Boente, et al.
Pubblicazione: (2024)

Deep generative models for fast photon shower simulation in ATLAS
di: ATLAS Collaboration
Pubblicazione: (2022)

Mighty Tracker -- Performance Studies of the MightyPix for LHCb
di: Schmitz, Hannah, et al.
Pubblicazione: (2024)

A highly-compact and ultra-fast homogeneous electromagnetic calorimeter based on oriented lead tungstate crystals
di: Bandiera, L., et al.
Pubblicazione: (2025)

Challenges and strategies in verification of FastRICH ASIC for the LHCb RICH detector
di: Lupi, M., et al.
Pubblicazione: (2025)

A Comprehensive Bandwidth Testing Framework for the LHCb Upgrade Trigger System
di: Grazette, Luke, et al.
Pubblicazione: (2025)

Metal Foil Detectors assembly for the beam and background monitoring in the LHCb experiment
di: Pugatch, V., et al.
Pubblicazione: (2025)

SPIDER, a Waveform Digitizer ASIC for picosecond timing in LHCb PicoCal
di: Alvado, Ludovic, et al.
Pubblicazione: (2025)

Performance of the FARICH-based particle identification at charm superfactories using machine learning
di: Chadeeva, M., et al.
Pubblicazione: (2025)

CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation
di: Giannelli, Michele Faucci, et al.
Pubblicazione: (2023)

Summary of the trigger systems of the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
di: Albrecht, Johannes, et al.
Pubblicazione: (2024)

Technical challenges designing a prototype common readout board for LHCb future upgrades
di: Langouët, Julien Jiro
Pubblicazione: (2024)

Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector
di: CMS Collaboration
Pubblicazione: (2026)

AI-based particle track identification in scintillating fibres read out with imaging sensors
di: Bührer, Noemi, et al.
Pubblicazione: (2024)

LHC beam monitoring via real-time hit reconstruction in the LHCb VELO pixel detector
di: Passaro, Daniele, et al.
Pubblicazione: (2024)

Preliminary design and simulation for CEPC fast luminosity monitor detector based on 4H-SiC
di: Li, Yanpeng, et al.
Pubblicazione: (2025)

OmniJet-$α_C$: Learning point cloud calorimeter simulations using generative transformers
di: Birk, Joschka, et al.
Pubblicazione: (2025)

Comparative Analysis of FPGA and GPU Performance for Machine Learning-Based Track Reconstruction at LHCb
di: Giasemis, Fotis I., et al.
Pubblicazione: (2025)

ParaFlow: fast calorimeter simulations parameterized in upstream material configurations
di: Erdmann, Johannes, et al.
Pubblicazione: (2025)

A Downstream and vertexing algorithm for Long Lived Particles (LLP) selection at the first High-level trigger (HLT1) of LHCb
di: Kholoimov, V., et al.
Pubblicazione: (2025)

AllShowers: One model for all calorimeter showers
di: Buss, Thorsten, et al.
Pubblicazione: (2026)

TomOpt: Differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
di: Strong, Giles C., et al.
Pubblicazione: (2023)

Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC
di: Shenoy, Rohan, et al.
Pubblicazione: (2023)

Trees versus Neural Networks for enhancing tau lepton real-time selection in proton-proton collisions
di: Yaary, Maayan, et al.
Pubblicazione: (2023)

Symbolic Regression on FPGAs for Fast Machine Learning Inference
di: Tsoi, Ho Fung, et al.
Pubblicazione: (2023)

Photon detection probability prediction using one-dimensional generative neural network
di: Mu, Wei, et al.
Pubblicazione: (2021)

A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
di: Mishra, Aashwin, et al.
Pubblicazione: (2025)

SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
di: Tsoi, Ho Fung, et al.
Pubblicazione: (2024)

Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
di: Jahin, Md Abrar, et al.
Pubblicazione: (2024)