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000478566 0247_ $$2arXiv$$aarXiv:2102.00258
000478566 0247_ $$2datacite_doi$$a10.3204/PUBDB-2022-02630
000478566 037__ $$aPUBDB-2022-02630
000478566 041__ $$aEnglish
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000478566 088__ $$2arXiv$$aarXiv:2102.00258
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000478566 1001_ $$0P:(DE-H253)PIP1085464$$aWelling, Christoph$$b0$$eCorresponding author
000478566 245__ $$aReconstructing non-repeating radio pulses with Information Field Theory
000478566 260__ $$c2021
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000478566 520__ $$aParticle showers in dielectric media produce radio signals which are used for the detection of both ultra-high energy cosmic rays and neutrinos with energies above a few PeV. The amplitude, polarization, and spectrum of these short, broadband radio pulses allow us to draw conclusions about the primary particles that caused them, as well as the mechanics of shower development and radio emission. However, confidently reconstructing the radio signals can pose a challenge, as they are often obscured by background noise. Information Field Theory offers a robust approach to this challenge by using Bayesian inference to calculate the most likely radio signal, given the recorded data. In this paper, we describe the application of Information Field Theory to radio signals from particle showers in both air and ice and demonstrate how accurately pulse parameters can be obtained from noisy data.
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000478566 650_7 $$2INSPIRE$$acosmic radiation: UHE
000478566 650_7 $$2INSPIRE$$abackground: noise
000478566 650_7 $$2INSPIRE$$aradio wave
000478566 650_7 $$2INSPIRE$$ashowers
000478566 650_7 $$2INSPIRE$$apolarization
000478566 650_7 $$2INSPIRE$$adielectric
000478566 650_7 $$2INSPIRE$$amechanics
000478566 650_7 $$2INSPIRE$$aBayesian
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000478566 650_7 $$2INSPIRE$$aair
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000478566 7001_ $$0P.Frank.2$$aFrank, Philipp$$b1
000478566 7001_ $$0T.A.Ensslin.2$$aEnßlin, Torsten A.$$b2
000478566 7001_ $$0P:(DE-H253)PIP1083021$$aNelles, Anna$$b3
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