SkyLLH -- A generalized Python-based tool for log-likelihood analyses in multi-messenger astronomy
2019
Online
report
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.
Comment: Presented at the 36th International Cosmic Ray Conference (ICRC 2019). See arXiv:1907.11699 for all IceCube contributions
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SkyLLH -- A generalized Python-based tool for log-likelihood analyses in multi-messenger astronomy
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Autor/in / Beteiligte Person: | Wolf, Martin |
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Veröffentlichung: | 2019 |
Medientyp: | report |
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