Using End-to-End Optimized Summary Statistics to Improve IceCube’s Diffuse Galactic Fits

Icecube Collaboration

doi:10.22323/1.501.1132

Using End-to-End Optimized Summary Statistics to Improve IceCube’s Diffuse Galactic Fits

Icecube Collaboration

Department of Physics

University of Delaware
RWTH Aachen University
Karlsruhe Institute of Technology
Adelaide University
Loyola University Chicago
German Electron Synchrotron
University of Canterbury
University of Wisconsin–Madison
Université libre de Bruxelles
University of Copenhagen
TU Dortmund University
University of Kansas
Marquette University
Harvard University
University of Utah
Michigan State University
South Dakota School of Mines & Technology
University of California at Irvine
Technical University of Munich
University of California at Berkeley
Ohio State University
Ruhr University Bochum
Uppsala University
University of Rochester
University of Maryland, College Park
University of Padua
University of Alabama
Johannes Gutenberg University Mainz
Georgia Institute of Technology
Queen's University Kingston

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

Abstract

Characterizing the astrophysical neutrino flux with the IceCube Neutrino Observatory traditionally relies on a binned forward folding likelihood approach. Insufficient Monte Carlo (MC) statistics in each bin limits the granularity and dimensionality of the binning scheme. A neural network can be employed to optimize a summary statistic that serves as the input for data analysis, yielding the best possible outcomes. This end-to-end optimized summary statistic allows for the inclusion of more observables while maintaining adequate MC statistics per bin. This work will detail the application of end-to-end optimized summary statistics in analyzing and characterizing the galactic neutrino flux, achieving improved resolution in the likelihood contours for selected signal parameters and models.

Original language	English
Article number	1132
Journal	Proceedings of Science
Volume	501
DOIs	https://doi.org/10.22323/1.501.1132
State	Published - 30 Dec 2025
Event	39th International Cosmic Ray Conference, ICRC 2025 - Geneva, Switzerland Duration: 15 Jul 2025 → 24 Jul 2025

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10.22323/1.501.1132

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@article{f513e1810f074561b8683f0902493f81,

title = "Using End-to-End Optimized Summary Statistics to Improve IceCube{\textquoteright}s Diffuse Galactic Fits",

abstract = "Characterizing the astrophysical neutrino flux with the IceCube Neutrino Observatory traditionally relies on a binned forward folding likelihood approach. Insufficient Monte Carlo (MC) statistics in each bin limits the granularity and dimensionality of the binning scheme. A neural network can be employed to optimize a summary statistic that serves as the input for data analysis, yielding the best possible outcomes. This end-to-end optimized summary statistic allows for the inclusion of more observables while maintaining adequate MC statistics per bin. This work will detail the application of end-to-end optimized summary statistics in analyzing and characterizing the galactic neutrino flux, achieving improved resolution in the likelihood contours for selected signal parameters and models.",

author = "\{Icecube Collaboration\} and Schroeder, \{Frank G.\} and Federico Bontempo and R. Abbasi and M. Ackermann and J. Adams and Agarwalla, \{S. K.\} and Aguilar, \{J. A.\} and M. Ahlers and Alameddine, \{J. M.\} and S. Ali and Amin, \{N. M.\} and K. Andeen and C. Arg{\"u}elles and Y. Ashida and S. Athanasiadou and Axani, \{S. N.\} and R. Babu and X. Bai and J. Baines-Holmes and A. Balagopal and Barwick, \{S. W.\} and S. Bash and V. Basu and R. Bay and Beatty, \{J. J.\} and Tjus, \{J. Becker\} and P. Behrens and J. Beise and C. Bellenghi and B. Benkel and S. BenZvi and D. Berley and E. Bernardini and Besson, \{D. Z.\} and E. Blaufuss and L. Bloom and S. Blot and I. Bodo and F. Bontempo and Motzkin, \{J. Y.Book\} and Meneguolo, \{C. Boscolo\} and S. B{\"o}ser and O. Botner and J. B{\"o}ttcher and J. Braun and B. Brinson and Z. Brisson-Tsavoussis and Burley, \{R. T.\} and D. Butterfield and Rho, \{C. D.\}",

note = "Publisher Copyright: {\textcopyright} Copyright owned by the author(s); 39th International Cosmic Ray Conference, ICRC 2025 ; Conference date: 15-07-2025 Through 24-07-2025",

year = "2025",

month = dec,

day = "30",

doi = "10.22323/1.501.1132",

language = "English",

volume = "501",

journal = "Proceedings of Science",

issn = "1824-8039",

publisher = "Sissa Medialab Srl",

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TY - JOUR

T1 - Using End-to-End Optimized Summary Statistics to Improve IceCube’s Diffuse Galactic Fits

AU - Icecube Collaboration

AU - Schroeder, Frank G.

AU - Bontempo, Federico

AU - Abbasi, R.

AU - Ackermann, M.

AU - Adams, J.

AU - Agarwalla, S. K.

AU - Aguilar, J. A.

AU - Ahlers, M.

AU - Alameddine, J. M.

AU - Ali, S.

AU - Amin, N. M.

AU - Andeen, K.

AU - Argüelles, C.

AU - Ashida, Y.

AU - Athanasiadou, S.

AU - Axani, S. N.

AU - Babu, R.

AU - Bai, X.

AU - Baines-Holmes, J.

AU - Balagopal, A.

AU - Barwick, S. W.

AU - Bash, S.

AU - Basu, V.

AU - Bay, R.

AU - Beatty, J. J.

AU - Tjus, J. Becker

AU - Behrens, P.

AU - Beise, J.

AU - Bellenghi, C.

AU - Benkel, B.

AU - BenZvi, S.

AU - Berley, D.

AU - Bernardini, E.

AU - Besson, D. Z.

AU - Blaufuss, E.

AU - Bloom, L.

AU - Blot, S.

AU - Bodo, I.

AU - Bontempo, F.

AU - Motzkin, J. Y.Book

AU - Meneguolo, C. Boscolo

AU - Böser, S.

AU - Botner, O.

AU - Böttcher, J.

AU - Braun, J.

AU - Brinson, B.

AU - Brisson-Tsavoussis, Z.

AU - Burley, R. T.

AU - Butterfield, D.

AU - Rho, C. D.

PY - 2025/12/30

Y1 - 2025/12/30

N2 - Characterizing the astrophysical neutrino flux with the IceCube Neutrino Observatory traditionally relies on a binned forward folding likelihood approach. Insufficient Monte Carlo (MC) statistics in each bin limits the granularity and dimensionality of the binning scheme. A neural network can be employed to optimize a summary statistic that serves as the input for data analysis, yielding the best possible outcomes. This end-to-end optimized summary statistic allows for the inclusion of more observables while maintaining adequate MC statistics per bin. This work will detail the application of end-to-end optimized summary statistics in analyzing and characterizing the galactic neutrino flux, achieving improved resolution in the likelihood contours for selected signal parameters and models.

AB - Characterizing the astrophysical neutrino flux with the IceCube Neutrino Observatory traditionally relies on a binned forward folding likelihood approach. Insufficient Monte Carlo (MC) statistics in each bin limits the granularity and dimensionality of the binning scheme. A neural network can be employed to optimize a summary statistic that serves as the input for data analysis, yielding the best possible outcomes. This end-to-end optimized summary statistic allows for the inclusion of more observables while maintaining adequate MC statistics per bin. This work will detail the application of end-to-end optimized summary statistics in analyzing and characterizing the galactic neutrino flux, achieving improved resolution in the likelihood contours for selected signal parameters and models.

UR - https://www.scopus.com/pages/publications/105029029249

U2 - 10.22323/1.501.1132

DO - 10.22323/1.501.1132

M3 - Conference article

AN - SCOPUS:105029029249

SN - 1824-8039

VL - 501

JO - Proceedings of Science

JF - Proceedings of Science

M1 - 1132

T2 - 39th International Cosmic Ray Conference, ICRC 2025

Y2 - 15 July 2025 through 24 July 2025

ER -

Using End-to-End Optimized Summary Statistics to Improve IceCube’s Diffuse Galactic Fits

Abstract

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