Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access
  • Access by Xinjiang University

Likelihood reconstruction for radio detectors of neutrinos and cosmic rays

Martin Ravn1, Christian Glaser1,2, Thorsten Glüsenkamp1,3, Ayca Özcelikkale4, and Alan Coleman1

Phys. Rev. D 114, 043025 – Published 11 August, 2026

DOI: https://doi.org/10.1103/m22t-cbgw

Abstract

Ultra-high-energy neutrinos and cosmic rays are excellent probes of astroparticle physics phenomena. For astroparticle physics analyses, robust and accurate reconstruction of signal parameters such as arrival direction and energy is essential. Radio detection is an established detector concept explored by many observatories; however, current reconstruction methods ignore bin-to-bin noise correlations, which limits reconstruction resolution and, so far, has prevented calculations of event-by-event uncertainties. In this work, we present a likelihood description of neutrino or cosmic-ray signals in radio detectors with correlated noise, as present in all neutrino and cosmic-ray radio detectors. We demonstrate, with simulation studies of both neutrinos and cosmic-ray radio signals, that signal parameters such as energy and direction, including event-by-event uncertainties with correct coverage, can be obtained. This method reduces reconstruction uncertainties and biases compared to previous approaches. Additionally, the Likelihood can be used for event selection and enables differentiable end-to-end detector optimization. The reconstruction code is available through the open-source software NuRadioReco.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (100)

  1. T. Huege, Radio detection of cosmic ray air showers in the digital era, Phys. Rep. 620, 1 (2016).
  2. F. G. Schröder, Radio detection of cosmic-ray air showers and high-energy neutrinos, Prog. Part. Nucl. Phys. 93, 1 (2017).
  3. A. Aab et al. (Pierre Auger Collaboration), Energy estimation of cosmic rays with the engineering radio array of the Pierre Auger Observatory, Phys. Rev. D 93, 122005 (2016).
  4. A. Aab et al. (Pierre Auger Collaboration), Measurement of the radiation energy in the radio signal of extensive air showers as a universal estimator of cosmic-ray energy, Phys. Rev. Lett. 116, 241101 (2016).
  5. C. Glaser, M. Erdmann, J. R. Hörandel, T. Huege, and J. Schulz, Simulation of radiation energy release in air showers, J. Cosmol. Astropart. Phys. 09 (2016) 024.
  6. M. Gottowik, C. Glaser, T. Huege, and J. Rautenberg, Determination of the absolute energy scale of extensive air showers via radio emission: Systematic uncertainty of underlying first-principle calculations, Astropart. Phys. 103, 87 (2018).
  7. K. Mulrey et al., On the cosmic-ray energy scale of the LOFAR radio telescope, J. Cosmol. Astropart. Phys. 11 (2020) 017.
  8. R. de Almeida et al. (Pierre Auger Collaboration), Long-term calibration and stability of the Auger Engineering Radio array using the diffuse galactic radio emission, Proc. Sci. ICRC2023 (2023) 279.
  9. S. Buitink et al., Method for high precision reconstruction of air shower Xmax using two-dimensional radio intensity profiles, Phys. Rev. D 90, 082003 (2014).
  10. S. Buitink et al., A large light-mass component of cosmic rays at 10^1710^17.5  eV from radio observations, Nature (London) 531, 70 (2016).
  11. A. Abdul Halim et al. (Pierre Auger Collaboration), Demonstrating agreement between radio and fluorescence measurements of the depth of maximum of extensive air showers at the Pierre Auger Observatory, Phys. Rev. Lett. 132, 021001 (2024).
  12. A. Abdul Halim et al. (Pierre Auger Collaboration), Radio measurements of the depth of air-shower maximum at the Pierre Auger Observatory, Phys. Rev. D 109, 022002 (2024).
  13. A. Aab et al. (Pierre Auger Collaboration), The Pierre Auger Observatory upgrade—preliminary design report, arXiv:1604.03637.
  14. A. Abdul Halim et al. (Pierre Auger Collaboration), Status and expected performance of the AugerPrime radio detector, Proc. Sci. ICRC2023 (2023) 344.
  15. A. Haungs (IceCube Collaboration), A scintillator and radio enhancement of the IceCube surface detector array, EPJ Web Conf. 210, 06009 (2019).
  16. IceCube-Gen2 Collaboration, IceCube-Gen2 Technical Design Report, https://icecube-gen2.wisc.edu/science/publications/TDR.
  17. S. Buitink et al., High-resolution air shower observations with the square kilometer array, Proc. Sci. ICRC2023 (2023) 503.
  18. S. W. Barwick and C. Glaser, Chapter 6: Radio detection of high energy neutrinos in ice, arXiv:2208.04971.
  19. J. Alvarez-Muniz, R. A. Batista, A. B. V., J. Bolmont, M. Bustamante et al. (GRAND Collaboration), The Giant Radio array for neutrino detection (GRAND): Science and design, Sci. China Phys. Mech. Astron. 63, 219501 (2020).
  20. S. Wissel, A. Romero-Wolf, H. Schoorlemmer, W. R. C. Jr., J. Alvarez-Muñiz, E. Zas et al., Prospects for high-elevation radio detection of >100PeV tau neutrinos, J. Cosmol. Astropart. Phys. 11 (2020) 065.
  21. A. Anker et al. (TAROGE, ARIANNA Collaborations), TAROGE-M: Radio antenna array on antarctic high mountain for detecting near-horizontal ultra-high energy air showers, J. Cosmol. Astropart. Phys. 11 (2022) 022.
  22. P. W. Gorham et al. (ANITA Collaboration), The antarctic impulsive transient antenna ultra-high energy neutrino detector design, performance, and sensitivity for 2006-2007 balloon flight, Astropart. Phys. 32, 10 (2009).
  23. Q. Abarr et al. (PUEO Collaboration), The Payload for Ultrahigh Energy Observations (PUEO): A white paper, J. Instrum. 16, P08035 (2021).
  24. I. Kravchenko et al. (RICE Collaboration), Performance and simulation of the RICE detector, Astropart. Phys. 19, 15 (2003).
  25. P. Allison et al. (ARA Collaboration), Design and initial performance of the Askaryan radio array prototype EeV neutrino detector at the south pole, Astropart. Phys. 35, 457 (2012).
  26. A. Anker et al. (ARIANNA Collaboration), Targeting ultra-high energy neutrinos with the ARIANNA experiment, Adv. Space Res. 64, 2595 (2019).
  27. J. A. Aguilar et al. (RNO-G Collaboration), Design and sensitivity of the radio neutrino observatory in Greenland (RNO-G), J. Instrum. 16, P03025 (2021).
  28. I. Kravchenko et al., Rice limits on the diffuse ultrahigh energy neutrino flux, Phys. Rev. D 73, 082002 (2006).
  29. P. Allison, S. Archambault, R. Bard, J. J. Beatty, M. Beheler-Amass, D. Z. Besson et al. (ARA Collaboration), Design and performance of an interferometric trigger array for radio detection of high-energy neutrinos, Nucl. Instrum. Methods Phys. Res., Sect. A 930, 112 (2019).
  30. P. Allison et al. (ARA Collaboration), Constraints on the diffuse flux of ultrahigh energy neutrinos from four years of askaryan radio array data in two stations, Phys. Rev. D 102, 043021 (2020).
  31. P. Allison et al. (ARA Collaboration), Low-threshold ultrahigh-energy neutrino search with the Askaryan Radio Array, Phys. Rev. D 105, 122006 (2022).
  32. A. Anker et al. (ARIANNA Collaboration), A search for cosmogenic neutrinos with the ARIANNA test bed using 4.5 years of data, J. Cosmol. Astropart. Phys. 03 (2020) 053.
  33. A. Anker et al. (ARIANNA Collaboration), Neutrino vertex reconstruction with in-ice radio detectors using surface reflections and implications for the neutrino energy resolution, J. Cosmol. Astropart. Phys. 11 (2019) 030.
  34. A. Anker et al. (ARIANNA Collaboration), Probing the angular and polarization reconstruction of the ARIANNA detector at the south pole, J. Instrum. 15, P09039 (2020).
  35. S. Barwick et al. (ARIANNA Collaboration), Capabilities of ARIANNA: Neutrino pointing resolution and implications for future ultra-high energy neutrino astronomy, Proc. Sci. ICRC2021 (2021) 1151.
  36. A. Anker et al. (Arianna Collaboration), Measuring the polarization reconstruction resolution of the ARIANNA neutrino detector with cosmic rays, J. Cosmol. Astropart. Phys. 04 (2022) 022.
  37. A. Anker et al. (Arianna Collaboration), Improving sensitivity of the ARIANNA detector by rejecting thermal noise with deep learning, J. Instrum. 17, P03007 (2022).
  38. A. Anker et al. (Arianna Collaboration), Developing new analysis tools for near surface radio-based neutrino detectors, J. Cosmol. Astropart. Phys. 10 (2023) 060.
  39. S. Hallmann, B. Clark, C. Glaser, D. Smith et al. (IceCube-Gen2 Collaboration), Sensitivity studies for the IceCube-Gen2 radio array, Proc. Sci. ICRC2021 (2021) 1183 [arXiv:2107.08910].
  40. A. Nelles et al., The radio emission pattern of air showers as measured with LOFAR—a tool for the reconstruction of the energy and the shower maximum, J. Cosmol. Astropart. Phys. 05 (2015) 018.
  41. C. Glaser, S. de Jong, M. Erdmann, and J. R. Hörandel, An analytic description of the radio emission of air showers based on its emission mechanisms, Astropart. Phys. 104, 64 (2019).
  42. A. Coleman, Model of the lateral distribution of the radio emission from air showers in the 70–350 MHz frequency band, Proc. Sci. ARENA2022 (2023) 038.
  43. A. Corstanje et al., Prospects for measuring the longitudinal particle distribution of cosmic-ray air showers with SKA, Proc. Sci. ARENA2022 (2023) 024 [arXiv:2303.09249].
  44. C. Glaser, Absolute energy calibration of the Pierre Auger Observatory using radio emission of extensive air showers, PhD thesis, RWTH Aachen University, 2017, 10.18154/RWTH-2017-02960.
  45. C. Glaser (ARIANNA Collaboration), ARIANNA: Measurement of cosmic rays with a radio neutrino detector in antarctica, EPJ Web Conf. 216, 02008 (2019).
  46. C. Glaser, A. Nelles, I. Plaisier, C. Welling, S. W. Barwick, D. García-Fernández, G. Gaswint, R. Lahmann, and C. Persichilli, NuRadioReco: A reconstruction framework for radio neutrino detectors, Eur. Phys. J. C 79, 464 (2019).
  47. C. Welling, C. Glaser, and A. Nelles, Reconstructing the cosmic-ray energy from the radio signal measured in one single station, J. Cosmol. Astropart. Phys. 10 (2019) 075.
  48. C. Glaser and S. W. Barwick, An improved trigger for Askaryan radio detectors, J. Instrum. 16, T05001 (2021).
  49. C. Glaser, A. Coleman, and T. Glusenkamp, NuRadioOpt: Optimization of radio detectors of ultra-high energy neutrinos through deep learning and differential programming, Proc. Sci. ICRC2023 (2023) 1114.
  50. C. Glaser et al., NuRadioMC: Simulating the radio emission of neutrinos from interaction to detector, Eur. Phys. J. C 80, 77 (2020).
  51. G. G. Gaswint, Quantifying the neutrino energy and pointing resolution of the ARIANNA detector, PhD thesis, UC, Irvine, 2021.
  52. I. Plaisier, S. Bouma, and A. Nelles, Reconstructing the arrival direction of neutrinos in deep in-ice radio detectors, Eur. Phys. J. C 83, 443 (2023).
  53. S. Bouma et al. (IceCube-Gen2 Collaboration), Direction reconstruction performance for IceCube-Gen2 radio, Proc. Sci. ICRC2023 (2023) 1045.
  54. L. D. Landau and I. Pomeranchuk, Limits of applicability of the theory of bremsstrahlung electrons and pair production at high-energies, Dokl. Akad. Nauk Ser. Fiz. 92, 535 (1953).
  55. L. D. Landau and I. Pomeranchuk, Electron-Cascade processes at ultra-high energies, Dokl. Akad. Nauk SSSR 92, 589 (1965).
  56. A. B. Migdal, Bremsstrahlung and pair production in condensed media at high-energies, Phys. Rev. 103, 1811 (1956).
  57. R. Abbasi et al. (IceCube Collaboration), Observation of high-energy neutrinos from the galactic plane, Science 380, adc9818 (2023).
  58. C. Glaser, S. McAleer, S. Stjärnholm, P. Baldi, and S. W. Barwick, Deep-learning-based reconstruction of the neutrino direction and energy for in-ice radio detectors, Astropart. Phys. 145, 102781 (2023).
  59. T. Glüsenkamp, Unifying supervised learning and VAEs: Coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructions, Eur. Phys. J. C 84, 163 (2024).
  60. T. Glüsenkamp et al. (IceCube Collaboration), Conditional normalizing flows for IceCube event reconstruction, Proc. Sci. ICRC2023 (2023) 1003 [arXiv:2309.16380].
  61. N. Heyer et al. (IceCube-Gen2 Collaboration), Deep learning based event reconstruction for the IceCube-Gen2 radio detector, Proc. Sci. ICRC2023 (2023) 1102 [arXiv:2308.00164].
  62. I. R. C. Committee, Recommendations and Reports of the CCIR, 1982: Spectrum utilization and monatary. Recommendations and Reports of the CCIR, 1982. International Telecommunication Union, 1982.
  63. K. Mulrey et al., Calibration of the LOFAR low-band antennas using the galaxy and a model of the signal chain, Astropart. Phys. 111, 1 (2019).
  64. E. S. Hong, Searching for ultra-high energy neutrinos with data from a prototype station of the askaryan radio array, PhD thesis, The Ohio State University, 2014.
  65. T. Meures, Development of a sub-glacial array of radio antennas for the detection of the flux of GZK neutrinos, PhD thesis, U. Brussels, Brussels U., IIHE, 2014, 10.1007/978-3-319-18756-3.
  66. L. Cremonesi et al. (ANITA Collaboration), The simulation of the sensitivity of the Antarctic Impulsive Transient Antenna (ANITA) to Askaryan radiation from cosmogenic neutrinos interacting in the Antarctic Ice, J. Instrum. 14, P08011 (2019).
  67. S. Ali et al. (ARA Collaboration), Progress towards an array-wide diffuse UHE neutrino search with the Askaryan radio array, arXiv:2409.03854.
  68. S. Agarwal, J. A. Aguilar, N. Alden, S. Ali, P. Allison, M. Betts et al., Calibration of the radio neutrino observatory in Greenland using thermal noise, Proc. Sci. ICRC2025 (2025) 1003.
  69. S. S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses, Ann. Math. Stat. 9, 60 (1938).
  70. C. R. Rao, Linear Statistical Inference and its Applications (Wiley, New York, 1973).
  71. M. Ravn, C. Glaser, T. Glüsenkamp, and A. Coleman, Likelihood reconstruction of radio signals of neutrinos and cosmic rays, in 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (2024), 9, arXiv:2409.11888.
  72. G. A. Askar’yan, Excess negative charge of an electron-photon shower and its coherent radio emission, Zh. Eksp. Teor. Fiz. 41, 616 (1961).
  73. G. A. Askar’yan, Coherent radio emission from cosmic showers in air and in dense media, Sov. J. Exp. Theor. Phys. 21, 658 (1965).
  74. J. Alvarez-Muniz, E. Marques, R. A. Vazquez, and E. Zas, Coherent radio pulses from showers in different media: A unified parameterization, Phys. Rev. D 74, 023007 (2006).
  75. J. Alvarez-Muniz, C. W. James, R. J. Protheroe, and E. Zas, Thinned simulations of extremely energetic showers in dense media for radio applications, Astropart. Phys. 32, 100 (2009).
  76. J. Alvarez-Muniz, W. R. Carvalho, Jr., M. Tueros, and E. Zas, Coherent Cherenkov radio pulses from hadronic showers up to EeV energies, Astropart. Phys. 35, 287 (2012).
  77. A. Coleman, O. Ericsson, C. Glaser, and M. Bustamante, Flavor composition of ultrahigh-energy cosmic neutrinos: Measurement forecasts for in-ice radio-based EeV neutrino telescopes, Phys. Rev. D 110, 023044 (2024).
  78. S. W. Barwick et al. (ARIANNA Collaboration), Observation of classically ‘forbidden’ electromagnetic wave propagation and implications for neutrino detection, J. Cosmol. Astropart. Phys. 07 (2018) 055.
  79. P. Allison et al. (ARA Collaboration), Design and performance of an interferometric trigger array for radio detection of high-energy neutrinos, Nucl. Instrum. Methods Phys. Res., Sect. A 930, 112 (2019).
  80. H. Dembinski, P. Ongmongkolkul et al., scikit-hep/iminuit.
  81. F. James and M. Roos, Minuit: A system for function minimization and analysis of the parameter errors and correlations, Comput. Phys. Commun. 10, 343 (1975).
  82. J. A. Aguilar et al., Reconstructing the neutrino energy for in-ice radio detectors: A study for the Radio Neutrino Observatory Greenland (RNO-G), Eur. Phys. J. C 82, 147 (2022).
  83. G. Cowan, K. Cranmer, E. Gross, and O. Vitells, Asymptotic formulae for likelihood-based tests of new physics, Eur. Phys. J. C 71, 1554 (2011).
  84. H. Cramér, Mathematical Methods of Statistics (PMS-9) (Princeton University Press, Princeton, NJ, 1999).
  85. C. Radhakrishna Rao, Information and the accuracy attainable in the estimation of statistical parameters, Bull. Calcutta Math. Soc. 37, 81 (1945).
  86. C. Glaser, A. Coleman, and T. Glusenkamp, NuRadioOpt: Optimization of radio detectors of ultra-high energy neutrinos through deep learning and differential programming, Proc. Sci. ICRC2023 (2023) 1114.
  87. C. Glaser, M. L. Ravn, T. Glüsenkamp, A. Özcelikkale, and A. Coleman, Likelihood-reconstruction for radio detectors of cosmic rays and neutrinos, Proc. Sci. ICRC2025 (2025) 271.
  88. T. Huege, M. Ludwig, and C. W. James, Simulating radio emission from air showers with CoREAS, AIP Conf. Proc. 1535, 128 (2013).
  89. T. Huege (Pierre Auger Collaboration), The radio detector of the Pierre Auger Observatory—status and expected performance, EPJ Web Conf. 283, 06002 (2023).
  90. S. Martinelli, T. Huege, D. Ravignani, and H. Schoorlemmer, Quantifying energy fluence and its uncertainty for radio emission from particle cascades in the presence of noise, Astropart. Phys. 168, 103091 (2025).
  91. S. Agarwal et al., Validating the performance of the radio neutrino observatory in greenland using cosmic-ray air showers, arXiv:2512.17664.
  92. J. Neyman and E. S. Pearson, Ix. on the problem of the most efficient tests of statistical hypotheses, Phil. Trans. R. Soc. A 231, 289 (1933).
  93. J. Henrichs, A. Nelles, J. A. A. Aguilar, P. Allison, D. Z. Besson, A. Bishop et al., Searching for cosmic-ray air showers with RNO-G, Proc. Sci. ICRC2023 (2023) 259.
  94. A. Nelles, Cosmic ray detection with RNO-G, Proc. Sci. ARENA2024 (2024) 006.
  95. B. Allen, W. G. Anderson, P. R. Brady, D. A. Brown, and J. D. E. Creighton, FINDCHIRP: An algorithm for detection of gravitational waves from inspiraling compact binaries, Phys. Rev. D 85, 122006 (2012).
  96. C. M. Biwer, C. D. Capano, S. De, M. Cabero, D. A. Brown, A. H. Nitz, and V. Raymond, pycbc inference: A python-based parameter estimation toolkit for compact binary coalescence signals, Publ. Astron. Soc. Pac. 131, 024503 (2019).
  97. K. Cannon, S. Caudill, C. Chan, B. Cousins, J. D. E. Creighton, B. Ewing et al., gstlal: A software framework for gravitational wave discovery, SoftwareX 14, 100680 (2021).
  98. F. Aubin et al., The MBTA pipeline for detecting compact binary coalescences in the third LIGO–Virgo observing run, Classical Quantum Gravity 38, 095004 (2021).
  99. Q. Chu et al., SPIIR online coherent pipeline to search for gravitational waves from compact binary coalescences, Phys. Rev. D 105, 024023 (2022).
  100. I. n. Zubeldia, A. Rotti, J. Chluba, and R. Battye, Understanding matched filters for precision cosmology, Mon. Not. R. Astron. Soc. 507, 4852 (2021).

Outline

Information

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation