ARTUS - A Framework for Event-based Data Analysis in High Energy Physics
November 03, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Joram Berger, Fabio Colombo, Raphael Friese, Dominik Haitz, Thomas Hauth, Thomas MΓΌller, GΓΌnter Quast, Georg Sieber
arXiv ID
1511.00852
Category
hep-ex
Cross-listed
cs.SE
Citations
3
Venue
arXiv.org
Last Checked
3 months ago
Abstract
ARTUS is an event-based data-processing framework for high energy physics experiments. It is designed for large-scale data analysis in a collaborative environment. The architecture design choices take into account typical challenges and are based on experiences with similar applications. The structure of the framework and its advantages are described. An example use case and performance measurements are presented. The framework is well-tested and successfully used by several analysis groups.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β hep-ex
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Parameterized Machine Learning for High-Energy Physics
R.I.P.
π»
Ghosted
A Convolutional Neural Network Neutrino Event Classifier
R.I.P.
π»
Ghosted
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
R.I.P.
π»
Ghosted
Jet Constituents for Deep Neural Network Based Top Quark Tagging
R.I.P.
π»
Ghosted
Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted