Machine learning is becoming ubiquitous in high energy physics for many tasks, including classification, regression, reconstruction, and simulations. To facilitate development in this area, and to make such research more accessible and reproducible, we present the open source Python JetNet library with easy to access and standardised interfaces for particle cloud datasets, implementations for HEP evaluation and loss metrics, and more useful tools for ML in HEP
Title: Exploring jet calibration with machine learning techniques Author: Patrik Novotný Institute: ...
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. Thi...
In this talk, I will discuss machine learning tasks used in high energy physics. I will talk about s...
International audienceThe Higgs boson discovery at the Large Hadron Collider in 2012 relied on boost...
The effective utilization at scale of complex machine learning (ML) techniques for HEP use cases pos...
In these proceedings we perform a brief review of machine learning (ML) applications in theoretical ...
© Published under licence by IOP Publishing Ltd. Machine learning is an important applied research a...
The scientific success of the LHC experiments at CERN highly depends on the availability of computin...
Machine learning is an important applied research area in particle physics, beginning with applicati...
International audienceThe rapidly-developing intersection of machine learning (ML) with high-energy ...
Data collection rates in high energy physics (HEP), particularly those at the Large Hadron Collider ...
Nowadays Machine Learning (ML) techniques are widely adopted in many areas of High Energy Physics (H...
Machine Learning (ML) techniques in the High-Energy Physics (HEP) domain are ubiquitous and will pla...
International audienceParticle physics or High Energy Physics (HEP) studies the elementary constitue...
JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consis...
Title: Exploring jet calibration with machine learning techniques Author: Patrik Novotný Institute: ...
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. Thi...
In this talk, I will discuss machine learning tasks used in high energy physics. I will talk about s...
International audienceThe Higgs boson discovery at the Large Hadron Collider in 2012 relied on boost...
The effective utilization at scale of complex machine learning (ML) techniques for HEP use cases pos...
In these proceedings we perform a brief review of machine learning (ML) applications in theoretical ...
© Published under licence by IOP Publishing Ltd. Machine learning is an important applied research a...
The scientific success of the LHC experiments at CERN highly depends on the availability of computin...
Machine learning is an important applied research area in particle physics, beginning with applicati...
International audienceThe rapidly-developing intersection of machine learning (ML) with high-energy ...
Data collection rates in high energy physics (HEP), particularly those at the Large Hadron Collider ...
Nowadays Machine Learning (ML) techniques are widely adopted in many areas of High Energy Physics (H...
Machine Learning (ML) techniques in the High-Energy Physics (HEP) domain are ubiquitous and will pla...
International audienceParticle physics or High Energy Physics (HEP) studies the elementary constitue...
JetClass is a new large-scale dataset to facilitate deep learning research in jet physics. It consis...
Title: Exploring jet calibration with machine learning techniques Author: Patrik Novotný Institute: ...
Modern analysis of HEP data needs advanced statistical tools to separate signal from background. Thi...
In this talk, I will discuss machine learning tasks used in high energy physics. I will talk about s...