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

## TL;DR

SkyLLH is a flexible, high-performance Python tool designed for unbinned log-likelihood analyses in multi-messenger astronomy, compatible with data from neutrino and gamma-ray experiments.

## Contribution

It introduces a modular, telescope-independent Python framework for efficient log-likelihood analyses in multi-messenger astronomy, enhancing analysis flexibility and performance.

## Key findings

- SkyLLH supports data from IceCube and Fermi-LAT.
- The tool is designed for high-performance and ease of use.
- It aims to facilitate broader application in multi-messenger studies.

## 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.