The SkyLLH framework for IceCube point-source search
Tomas Kontrimas, Martin Wolf (for the IceCube Collaboration)
TL;DR
SkyLLH is a flexible Python framework that enhances IceCube neutrino point-source searches by supporting various analysis types and KDE-based probability functions, improving multi-messenger astronomy data analysis.
Contribution
The paper introduces SkyLLH, a modular Python tool that streamlines and extends unbinned likelihood analyses for IceCube and multi-messenger astronomy.
Findings
Supports kernel density estimation for probability functions
Enables stacked and time-variable source searches
Improves analysis flexibility and efficiency
Abstract
Hypothesis tests based on unbinned log-likelihood (LLH) functions are a common technique used in multi-messenger astronomy, including IceCube's neutrino point-source searches. We present the general Python-based tool "SkyLLH", which provides a modular framework for implementing and executing log-likelihood functions to perform data analyses with multi-messenger astronomy data. Specific SkyLLH framework features for a new and improved time-integrated IceCube point-source analysis are highlighted, including the support for kernel density estimation (KDE) based probability density functions. In addition, the support for a variety of point-source analysis types, such as stacked and time-variable searches, will be presented.
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Taxonomy
TopicsAstrophysics and Cosmic Phenomena · Radio Astronomy Observations and Technology · Computational Physics and Python Applications