Cargando…

Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations

Mostrar otras versiones (1)

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alte...

Descripción completa

Detalles Bibliográficos
Autores principales:	Rehm, Florian, Vallecorsa, Sofia, Borras, Kerstin, Krücker, Dirk
Lenguaje:	eng
Publicado:	2021
Materias:	cs.LG Computing and Computers hep-ex Particle Physics - Experiment
Acceso en línea:	https://dx.doi.org/10.1051/epjconf/202125103042 http://cds.cern.ch/record/2767379

Ejemplares similares

Towards Optimal Compression: Joint Pruning and Quantization
por: Zandonati, Ben, et al.
Publicado: (2023)

Technical Report of Participation in Higgs Boson Machine Learning Challenge
por: Ahmad, S. Raza
Publicado: (2015)

Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations
por: Rehm, Florian, et al.
Publicado: (2021)

Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
por: Chang, Su Yeon, et al.
Publicado: (2021)

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark
por: Borras, Hendrik, et al.
Publicado: (2022)

Improving Parametric Neural Networks for High-Energy Physics (and Beyond)
por: Anzalone, Luca, et al.
Publicado: (2022)

Running the Dual-PQC GAN on noisy simulators and real quantum hardware
por: Chang, Su Yeon, et al.
Publicado: (2023)

Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
por: Chang, Su Yeon, et al.
Publicado: (2021)

Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics
por: Migliorini, Matteo, et al.
Publicado: (2019)

Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
por: Belayneh, Dawit, et al.
Publicado: (2019)

Variational Dropout Sparsification for Particle Identification speed-up
por: Ryzhikov, Artem, et al.
Publicado: (2020)

Benchmark of Generative Adversarial Networks for Fast HEP Calorimeter Simulations
por: Rehm, Florian, et al.
Publicado: (2021)

Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment
por: Paganini, Michela
Publicado: (2017)

The Tracking Machine Learning challenge : Throughput phase
por: Amrouche, Sabrina, et al.
Publicado: (2021)

Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
por: Grosso, Gaia, et al.
Publicado: (2023)

Lorentz Group Equivariant Autoencoders
por: Hao, Zichun, et al.
Publicado: (2022)

Triggering Dark Showers with Conditional Dual Auto-Encoders
por: Anzalone, Luca, et al.
Publicado: (2023)

Lightweight Jet Reconstruction and Identification as an Object Detection Task
por: Pol, Adrian Alan, et al.
Publicado: (2022)

Hashing and metric learning for charged particle tracking
por: Amrouche, Sabrina, et al.
Publicado: (2021)

Anomaly Detection With Conditional Variational Autoencoders
por: Pol, Adrian Alan, et al.
Publicado: (2020)

Particle Cloud Generation with Message Passing Generative Adversarial Networks
por: Kansal, Raghav, et al.
Publicado: (2021)

Autoencoders for Real-Time SUEP Detection
por: Chhibra, Simranjit Singh, et al.
Publicado: (2023)

Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing
por: Gomez Ramirez, A., et al.
Publicado: (2018)

Unravelling physics beyond the standard model with classical and quantum anomaly detection
por: Schuhmacher, Julian, et al.
Publicado: (2023)

Quantum Machine Learning for HEP Detector Simulations
por: Rehm, Florian, et al.
Publicado: (2021)

The Quantum Path Kernel: A Generalized Neural Tangent Kernel for Deep Quantum Machine Learning
por: Incudini, Massimiliano, et al.
Publicado: (2023)

Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
por: Tüysüz, Cenk, et al.
Publicado: (2021)

Conditional Born machine for Monte Carlo event generation
por: Kiss, Oriel, et al.
Publicado: (2022)

Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC
por: Wulff, Eric, et al.
Publicado: (2023)

Learning new physics efficiently with nonparametric methods
por: Letizia, Marco, et al.
Publicado: (2022)

Generative Models for Fast Calorimeter Simulation: the LHCb case
por: Chekalina, Viktoria, et al.
Publicado: (2019)

Variational Autoencoders for New Physics Mining at the Large Hadron Collider
por: Cerri, Olmo, et al.
Publicado: (2018)

Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier
por: Grönroos, Sonja, et al.
Publicado: (2023)

Mixed Quantum–Classical Method for Fraud Detection With Quantum Feature Selection
por: Grossi, Michele, et al.
Publicado: (2022)

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC
por: Bhimji, Wahid, et al.
Publicado: (2017)

Evaluating generative models in high energy physics
por: Kansal, Raghav, et al.
Publicado: (2022)

Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML
por: Loncar, Vladimir, et al.
Publicado: (2021)

Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
por: Rehm, Florian
Publicado: (2021)

QONNX: Representing Arbitrary-Precision Quantized Neural Networks
por: Pappalardo, Alessandro, et al.
Publicado: (2022)

Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
por: Rehm, Florian, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_p99fioab7qnoupendkjd09kans