From TeV to EeV: Characterization of the Cosmic Neutrino Flux with Combined Samples in IceCube

ORAL

Abstract

The IceCube Neutrino Observatory has detected astrophysical neutrinos from a few TeV to tens of PeV, and recent analyses have revealed deviations from a single power law at low energies, though statistical limitations have so far prevented resolving features at the PeV scale. A precise characterization of the neutrino spectrum across this range is essential to probe its connection to high-energy gamma rays in the low-energy regime and to ultra-high-energy cosmic rays (UHECRs) in the high-energy regime. To enhance sensitivity and leverage complementary information, we combine throughgoing tracks from the Northern Sky with high-energy starting tracks and both contained and uncontained cascades from the full sky. This analysis employs advanced atmospheric background modeling, refined ice systematics, and improved energy reconstruction through a hybrid of machine-learning and likelihood-based techniques.

*U.S. National Science Foundation-Office of Polar Programs, U.S. National Science Foundation-Physics Division, U.S. National Science Foundation-EPSCoR, U.S. National Science Foundation-Office of Advanced Cyberinfrastructure, Wisconsin Alumni Research Foundation, Center for High Throughput Computing (CHTC) at the University of Wisconsin–Madison, Open Science Grid (OSG), Partnership to Advance Throughput Computing (PATh), Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS), Frontera and Ranch computing project at the Texas Advanced Computing Center, U.S. Department of Energy-National Energy Research Scientific Computing Center, Particle astrophysics research computing center at the University of Maryland, Institute for Cyber-Enabled Research at Michigan State University, Astroparticle physics computational facility at Marquette University, NVIDIA Corporation, and Google Cloud Platform

Presenters

  • Emre B Yildizci

    • University of Wisconsin - Madison

Authors

  • Emre B Yildizci

    • University of Wisconsin - Madison
  • Zoe Rechav

    • University of Wisconsin - Madison
  • Lu Lu

    • University of Wisconsin - Madison