English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Particle tracking is a fundamental part of the event analysis in high energyand nuclear physics. Events multiplicity increases each year along with thedrastic growth of the experimental data which modern HENP detectors produce, sothe classical tracking algorithms such as the well-known Kalman filter cannotsatisfy speed and scaling requirements. At the same time, breakthroughs in thestudy of deep learning open an opportunity for the application ofhigh-performance deep neural networks for solving tracking problems in a denseenvironment of experiments with heavy ions. However, there are nowell-documented software libraries for deep learning track reconstruction yet.We introduce Ariadne, the first open-source library for particle tracking basedon the PyTorch deep learning framework. The goal of our library is to provide asimple interface that allows one to prepare train and test datasets and totrain and evaluate one of the deep tracking models implemented in the libraryon the data from your specific experiment. The user experience is greatlyfacilitated because of the system of gin-configurations. The modular structureof the library and abstract classes let the user develop his data processingpipeline and deep tracking model easily. The proposed library is open-source tofacilitate academic research in the field of particle tracking based on deeplearning.

Ariadne: PyTorch Library for Particle Track Reconstruction Using Deep Learning