ProMC: Input-output data format for HEP applications using varint encoding

Abstract

A new data format for Monte Carlo (MC) events, or any structural data, including experimental data, is discussed. The format is designed to store data in a compact binary form using variable-size integer encoding as implemented in the Google's Protocol Buffers package. This approach is implemented in the ProMC library which produces smaller file sizes for MC records compared to the existing input-output libraries used in high-energy physics (HEP). Other important features of the proposed format are a separation of abstract data layouts from concrete programming implementations, self-description and random access. Data stored in ProMC files can be written, read and manipulated in a number of programming languages, such C++, JAVA, FORTRAN and PYTHON.

17 pages, 2 figures

References in corpus (7)

• PYTHIA 6.4 Physics and Manual
• DELPHES 3, A modular framework for fast simulation of a generic collider experiment
• ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization
• A standard format for Les Houches Event Files
• HepSim: a repository with predictions for high-energy physics experiments
• Next generation input-output data format for HEP using Google's protocol buffers
• The A4 project: physics data processing using the Google protocol buffer library

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• EasyScan_HEP: a tool for connecting programs to scan the parameter space of physics models
• Sensitivity to new high-mass states decaying to ttbar at a 100 TeV collider
• ProIO: An Event-Based I/O Stream Format for Protobuf Messages
• Cross-platform validation and analysis environment for particle physics
• ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks
• Jas4pp -- a Data-Analysis Framework for Physics and Detector Studies
• Public repository with Monte Carlo simulations for high-energy particle collision experiments