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.
SkyLLH - A generalized Python-based tool for log-likelihood analyses in multi-messenger astronomy
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Pre-published on:
July 22, 2019
Published on:
July 02, 2021
Abstract
DOI: https://doi.org/10.22323/1.358.1035
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Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.