The SkyLLH framework for IceCube point-source search

The IceCube Collaboration

The SkyLLH framework for IceCube point-source search

The IceCube Collaboration

Physics and Astronomy

Technical University of Munich
RWTH Aachen University
Loyola University Chicago
German Electron Synchrotron
University of Canterbury
Université libre de Bruxelles
University of Copenhagen
Oskar Klein Centre
University of Geneva
Karlsruhe Institute of Technology
University of Delaware
Harvard University
Marquette University
Pennsylvania State University
Friedrich-Alexander University Erlangen-Nürnberg
University of Wisconsin-Madison
Massachusetts Institute of Technology
South Dakota School of Mines & Technology
University of California at Irvine
University of California at Berkeley
Ohio State University
University of Wuppertal
Ruhr University Bochum
University of Rochester
University of Maryland, College Park
University of Padua
University of Kansas
Moscow Engineering Physics Institute
Lawrence Berkeley National Laboratory
Johannes Gutenberg University Mainz
Uppsala University

Research output: Contribution to journal › Conference article › peer-review

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.

Original language	English
Article number	1073
Journal	Proceedings of Science
Volume	395
State	Published - Mar 18 2022
Event	37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany Duration: Jul 12 2021 → Jul 23 2021

Cite this

@article{43938de9a47d494496a81724c2311cf4,

title = "The SkyLLH framework for IceCube point-source search",

abstract = "Hypothesis tests based on unbinned log-likelihood (LLH) functions are a common technique used in multi-messenger astronomy, including IceCube{\textquoteright}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.",

author = "\{The IceCube Collaboration\} and Tomas Kontrimas and Martin Wolf and R. Abbasi and M. Ackermann and J. Adams and Aguilar, \{J. A.\} and M. Ahlers and M. Ahrens and C. Alispach and Alves, \{A. A.\} and Amin, \{N. M.\} and R. An and K. Andeen and T. Anderson and G. Anton and C. Arg{\"u}elles and Y. Ashida and S. Axani and X. Bai and Balagopal, \{A. V.\} and A. Barbano and Barwick, \{S. W.\} and B. Bastian and V. Basu and S. Baur and R. Bay and Beatty, \{J. J.\} and Becker, \{K. H.\} and \{Becker Tjus\}, J. and C. Bellenghi and S. BenZvi and D. Berley and E. Bernardini and Besson, \{D. Z.\} and G. Binder and D. Bindig and E. Blaufuss and S. Blot and M. Boddenberg and F. Bontempo and J. Borowka and S. B{\"o}ser and O. Botner and J. B{\"o}ttcher and E. Bourbeau and F. Bradascio and J. Braun and S. Bron and J. Brostean-Kaiser and J. Kiryluk",

note = "Publisher Copyright: {\textcopyright} Copyright owned by the author(s) under the terms of the Creative Commons.; 37th International Cosmic Ray Conference, ICRC 2021 ; Conference date: 12-07-2021 Through 23-07-2021",

year = "2022",

month = mar,

day = "18",

language = "English",

volume = "395",

journal = "Proceedings of Science",

issn = "1824-8039",

}

TY - JOUR

T1 - The SkyLLH framework for IceCube point-source search

AU - The IceCube Collaboration

AU - Kontrimas, Tomas

AU - Wolf, Martin

AU - Abbasi, R.

AU - Ackermann, M.

AU - Adams, J.

AU - Aguilar, J. A.

AU - Ahlers, M.

AU - Ahrens, M.

AU - Alispach, C.

AU - Alves, A. A.

AU - Amin, N. M.

AU - An, R.

AU - Andeen, K.

AU - Anderson, T.

AU - Anton, G.

AU - Argüelles, C.

AU - Ashida, Y.

AU - Axani, S.

AU - Bai, X.

AU - Balagopal, A. V.

AU - Barbano, A.

AU - Barwick, S. W.

AU - Bastian, B.

AU - Basu, V.

AU - Baur, S.

AU - Bay, R.

AU - Beatty, J. J.

AU - Becker, K. H.

AU - Becker Tjus, J.

AU - Bellenghi, C.

AU - BenZvi, S.

AU - Berley, D.

AU - Bernardini, E.

AU - Besson, D. Z.

AU - Binder, G.

AU - Bindig, D.

AU - Blaufuss, E.

AU - Blot, S.

AU - Boddenberg, M.

AU - Bontempo, F.

AU - Borowka, J.

AU - Böser, S.

AU - Botner, O.

AU - Böttcher, J.

AU - Bourbeau, E.

AU - Bradascio, F.

AU - Braun, J.

AU - Bron, S.

AU - Brostean-Kaiser, J.

AU - Kiryluk, J.

PY - 2022/3/18

Y1 - 2022/3/18

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85145590100

M3 - Conference article

AN - SCOPUS:85145590100

SN - 1824-8039

VL - 395

JO - Proceedings of Science

JF - Proceedings of Science

M1 - 1073

T2 - 37th International Cosmic Ray Conference, ICRC 2021

Y2 - 12 July 2021 through 23 July 2021

ER -

The SkyLLH framework for IceCube point-source search

Abstract

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