SkyLLH -- A generalized Python-based tool for log-likelihood analyses in multi-messenger astronomy

Martin Wolf

SkyLLH -- A generalized Python-based tool for log-likelihood analyses in multi-messenger astronomy

Instrumentation and Methods for Astrophysics 2019-08-15 v1 High Energy Astrophysical Phenomena High Energy Physics - Experiment

Authors: Martin Wolf

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Abstract

Common analysis techniques in multi-messenger astronomy involve hypothesis tests with unbinned log-likelihood (LLH) functions using data recorded in celestial coordinates to identify sources of high-energy cosmic particles in the Universe. We present the new Python-based tool "SkyLLH" to develop such analyses in a telescope-independent framework. The main goal of the software is to provide an easy-to-use and modularized concept to implement and to execute such LLH functions efficiently on the computer with high-performance. SkyLLH can be applied on different multi-messenger data like neutrino and gamma-ray events from experiments such as the IceCube Neutrino Observatory and the Fermi-LAT. In this contribution we highlight SkyLLH's various design goals, current development status, and prospects for its wider application in multi-messenger astronomy.

Keywords

scientific software and data analysis tools virtual observatory computational physics software

Cite

@article{arxiv.1908.05181,
  title  = {SkyLLH -- A generalized Python-based tool for log-likelihood analyses in multi-messenger astronomy},
  author = {Martin Wolf},
  journal= {arXiv preprint arXiv:1908.05181},
  year   = {2019}
}

Comments

Presented at the 36th International Cosmic Ray Conference (ICRC 2019). See arXiv:1907.11699 for all IceCube contributions

SkyLLH -- A generalized Python-based tool for log-likelihood analyses in multi-messenger astronomy

Abstract

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