Javascript must be enabled to continue!

Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore

The DeepCore subdetector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV. Data taken between 2012 and 2021 (3387 days) are utilized for an atmospheric νμ disappearance analysis that studied 150 257 neutrino-candidate events with reconstructed energies between 5 and 100 GeV. An advanced reconstruction based on a convolutional neural network is applied, providing increased signal efficiency and background suppression, resulting in a measurement with both significantly increased statistics compared to previous DeepCore oscillation results and high neutrino purity. For the normal neutrino mass ordering, the atmospheric neutrino oscillation parameters and their 1σ errors are measured to be Δm322=2.40−0.04+0.05×10−3 eV2 and sin2θ23=0.54−0.03+0.04. The results are the most precise to date using atmospheric neutrinos, and are compatible with measurements from other neutrino detectors including long-baseline accelerator experiments.

American Physical Society (APS)

R. Abbasi M. Ackermann J. Adams S. K. Agarwalla J. A. Aguilar M. Ahlers J. M. Alameddine N. M. Amin K. Andeen G. Anton C. Argüelles Y. Ashida S. Athanasiadou L. Ausborm S. N. Axani X. Bai A. Balagopal V. M. Baricevic S. W. Barwick S. Bash V. Basu R. Bay J. J. Beatty J. Becker Tjus J. Beise C. Bellenghi C. Benning S. BenZvi D. Berley E. Bernardini D. Z. Besson E. Blaufuss L. Bloom S. Blot F. Bontempo J. Y. Book Motzkin C. Boscolo Meneguolo S. Böser O. Botner J. Böttcher E. Bourbeau J. Braun B. Brinson J. Brostean-Kaiser L. Brusa R. T. Burley D. Butterfield M. A. Campana I. Caracas K. Carloni J. Carpio S. Chattopadhyay N. Chau Z. Chen D. Chirkin S. Choi B. A. Clark A. Coleman G. H. Collin A. Connolly J. M. Conrad P. Coppin R. Corley P. Correa D. F. Cowen P. Dave C. De Clercq J. J. DeLaunay D. Delgado S. Deng A. Desai P. Desiati K. D. de Vries G. de Wasseige T. DeYoung A. Diaz J. C. Díaz-Vélez P. Dierichs M. Dittmer A. Domi L. Draper H. Dujmovic K. Dutta M. A. DuVernois T. Ehrhardt L. Eidenschink A. Eimer P. Eller E. Ellinger S. El Mentawi D. Elsässer R. Engel H. Erpenbeck J. Evans P. A. Evenson K. L. Fan K. Fang K. Farrag A. R. Fazely A. Fedynitch N. Feigl S. Fiedlschuster C. Finley L. Fischer D. Fox A. Franckowiak S. Fukami P. Fürst J. Gallagher E. Ganster A. Garcia M. Garcia G. Garg E. Genton L. Gerhardt A. Ghadimi C. Girard-Carillo C. Glaser T. Glüsenkamp J. G. Gonzalez S. Goswami A. Granados D. Grant S. J. Gray O. Gries S. Griffin S. Griswold K. M. Groth C. Günther P. Gutjahr C. Ha C. Haack A. Hallgren L. Halve F. Halzen H. Hamdaoui M. Ha Minh M. Handt K. Hanson J. Hardin A. A. Harnisch P. Hatch A. Haungs J. Häußler K. Helbing J. Hellrung J. Hermannsgabner L. Heuermann N. Heyer S. Hickford A. Hidvegi J. Hignight C. Hill G. C. Hill K. D. Hoffman S. Hori K. Hoshina M. Hostert W. Hou T. Huber K. Hultqvist M. Hünnefeld R. Hussain K. Hymon A. Ishihara W. Iwakiri M. Jacquart O. Janik M. Jansson G. S. Japaridze M. Jeong M. Jin B. J. P. Jones N. Kamp D. Kang W. Kang X. Kang A. Kappes D. Kappesser L. Kardum T. Karg M. Karl A. Karle A. Katil U. Katz M. Kauer J. L. Kelley M. Khanal A. Khatee Zathul A. Kheirandish J. Kiryluk S. R. Klein A. Kochocki R. Koirala H. Kolanoski T. Kontrimas L. Köpke C. Kopper D. J. Koskinen P. Koundal M. Kovacevich M. Kowalski T. Kozynets J. Krishnamoorthi K. Kruiswijk E. Krupczak A. Kumar E. Kun N. Kurahashi N. Lad C. Lagunas Gualda M. Lamoureux M. J. Larson S. Latseva F. Lauber J. P. Lazar J. W. Lee K. Leonard DeHolton A. Leszczyńska J. Liao M. Lincetto Y. T. Liu M. Liubarska E. Lohfink C. Love C. J. Lozano Mariscal L. Lu F. Lucarelli W. Luszczak Y. Lyu W. Y. Ma J. Madsen E. Magnus K. B. M. Mahn Y. Makino E. Manao S. Mancina W. Marie Sainte I. C. Mariş S. Marka Z. Marka M. Marsee I. Martinez-Soler R. Maruyama F. Mayhew F. McNally J. V. Mead K. Meagher S. Mechbal A. Medina M. Meier Y. Merckx L. Merten J. Micallef J. Mitchell T. Montaruli R. W. Moore Y. Morii R. Morse M. Moulai T. Mukherjee R. Naab R. Nagai M. Nakos U. Naumann J. Necker A. Negi L. Neste M. Neumann H. Niederhausen M. U. Nisa K. Noda A. Noell A. Novikov A. Obertacke Pollmann V. O’Dell B. Oeyen A. Olivas R. Orsoe J. Osborn E. O’Sullivan H. Pandya N. Park G. K. Parker E. N. Paudel L. Paul C. Pérez de los Heros T. Pernice J. Peterson S. Philippen A. Pizzuto M. Plum A. Pontén Y. Popovych M. Prado Rodriguez B. Pries R. Procter-Murphy G. T. Przybylski C. Raab J. Rack-Helleis M. Ravn K. Rawlins Z. Rechav A. Rehman P. Reichherzer E. Resconi S. Reusch W. Rhode B. Riedel A. Rifaie E. J. Roberts S. Robertson S. Rodan G. Roellinghoff M. Rongen A. Rosted C. Rott T. Ruhe L. Ruohan D. Ryckbosch I. Safa J. Saffer D. Salazar-Gallegos P. Sampathkumar A. Sandrock M. Santander S. Sarkar S. Sarkar J. Savelberg P. Savina P. Schaile M. Schaufel H. Schieler S. Schindler B. Schlüter F. Schlüter N. Schmeisser T. Schmidt J. Schneider F. G. Schröder L. Schumacher S. Sclafani D. Seckel M. Seikh M. Seo S. Seunarine P. Sevle Myhr R. Shah S. Shefali N. Shimizu M. Silva B. Skrzypek B. Smithers R. Snihur J. Soedingrekso A. Søgaard D. Soldin P. Soldin G. Sommani C. Spannfellner G. M. Spiczak C. Spiering M. Stamatikos T. Stanev T. Stezelberger T. Stürwald T. Stuttard G. W. Sullivan I. Taboada S. Ter-Antonyan A. Terliuk M. Thiesmeyer W. G. Thompson J. Thwaites S. Tilav K. Tollefson C. Tönnis S. Toscano D. Tosi A. Trettin R. Turcotte J. P. Twagirayezu M. A. Unland Elorrieta A. K. Upadhyay K. Upshaw A. Vaidyanathan N. Valtonen-Mattila J. Vandenbroucke N. van Eijndhoven D. Vannerom J. van Santen J. Vara J. Veitch-Michaelis M. Venugopal M. Vereecken S. Verpoest D. Veske A. Vijai C. Walck A. Wang C. Weaver P. Weigel A. Weindl J. Weldert A. Y. Wen C. Wendt J. Werthebach M. Weyrauch N. Whitehorn C. H. Wiebusch D. R. Williams J. Willison L. Witthaus A. Wolf M. Wolf G. Wrede X. W. Xu J. P. Yanez E. Yildizci S. Yoshida R. Young S. Yu T. Yuan Z. Zhang P. Zhelnin P. Zilberman M. Zimmerman

Physical Review Letters

2025

Title: Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore

Description:

The DeepCore subdetector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV.

Data taken between 2012 and 2021 (3387 days) are utilized for an atmospheric νμ disappearance analysis that studied 150 257 neutrino-candidate events with reconstructed energies between 5 and 100 GeV.

An advanced reconstruction based on a convolutional neural network is applied, providing increased signal efficiency and background suppression, resulting in a measurement with both significantly increased statistics compared to previous DeepCore oscillation results and high neutrino purity.

For the normal neutrino mass ordering, the atmospheric neutrino oscillation parameters and their 1σ errors are measured to be Δm322=2.

40−0.

04+0.

05×10−3 eV2 and sin2θ23=0.

54−0.

03+0.

04.

The results are the most precise to date using atmospheric neutrinos, and are compatible with measurements from other neutrino detectors including long-baseline accelerator experiments.

Back

Abstract In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and m...

Testing the Neutrino Mass Ordering with Four Years of IceCube/DeepCore Data

Abstract The measurement of the Neutrino Mass Ordering (NMO), i.e. the ordering of the neutrino mass eigenstates, is one of the major goals of many future neutrino e...

OBSERVATION AND CHARACTERIZATION OF A COSMIC MUON NEUTRINO FLUX FROM THE NORTHERN HEMISPHERE USING SIX YEARS OF ICECUBE DATA

ABSTRACT The IceCube Collaboration has previously discovered a high-energy astrophysical neutrino flux using neutrino events with interaction...

THE SEARCH FOR TRANSIENT ASTROPHYSICAL NEUTRINO EMISSION WITH ICECUBE-DEEPCORE

ABSTRACT We present the results of a search for astrophysical sources of brief transient neutrino emission using IceCube and DeepCore data acquired between 2012 May ...

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

Abstract IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45...

Search for a light sterile neutrino with 7.5 years of IceCube DeepCore data

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5...

First all-flavor search for transient neutrino emission using 3-years of IceCube DeepCore data

Abstract Since the discovery of a flux of high-energy astrophysical neutrinos, searches for their origins have focused primarily at TeV-PeV energies. Compared to sub...

Fast low energy reconstruction using Convolutional Neural Networks

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detect...

Email:
Password:

Email:

Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore

Related Results