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【摘要】

The IceCube Neutrino Observatory enables precise measurements of high-energy atmospheric neutrinos with unprecedented statistics. These neutrinos are produced from cosmic ray interactions in the atmosphere, primarily through the decay of pions and kaons. Seasonal temperature variations in the stratosphere influence the rate of these neutrinos, with effects expected to be more pronounced at higher energies. In this talk, I will present recent findings on these seasonal variations, based on over a decade of IceCube data. Using a novel machine-learning-based spectrum unfolding algorithm, these seasonal effects on the atmospheric neutrino flux could be quantified for the first time.