Likelihood reconstruction using machine learning for arbitrary detector geometries

This repository is used for the R&D of event recosntrcution in IceCube, based on approximate likelihoods.

Use the wiki for things other than code!

It is recommended to install in-place, i.e.:

pip install --editable .[full]

This method was published as:

A flexible event reconstruction based on machine learning and likelihood principles

Philipp Eller (Munich, Tech. U.), Aaron T. Fienberg (Penn State U.), Jan Weldert (Mainz U., Inst. Phys.), Garrett Wendel (Penn State U.), Sebastian Böser (Mainz U., Inst. Phys.) et al.

e-Print: 2208.10166 DOI: 10.1016/j.nima.2023.168011 (publication) Nucl.Instrum.Meth.A 1048 (2023), 168011

Please cite as

@article{Eller:2022xvi,
    author = {Eller, Philipp and Fienberg, Aaron T. and Weldert, Jan and Wendel, Garrett and B\"oser, Sebastian and Cowen, D. F.},
    title = "{A flexible event reconstruction based on machine learning and likelihood principles}",
    eprint = "2208.10166",
    archivePrefix = "arXiv",
    primaryClass = "hep-ex",
    doi = "10.1016/j.nima.2023.168011",
    journal = "Nucl. Instrum. Meth. A",
    volume = "1048",
    pages = "168011",
    year = "2023"
}