arXiv subjects

Kanako Yamaguchi

Publications and source records attributed to Kanako Yamaguchi.

2 recordsLinked to original sources

arXiv2026

First measurement of $ϕ$ meson production in 30 GeV proton-nucleus reactions via di-electron decay at J-PARC

We present the first measurement of the production of the $ϕ$ meson in 30 GeV proton-nucleus interactions on carbon and copper targets via the di-electron decay channel. The measurement was conducted at the high-momentum beamline of the J-PARC Hadron Experimental Facility, which was commissioned in 2020. The $e^+e^-$ pairs were detected using the E16 spectrometer, during a commissioning run of the J-PARC E16 experiment. The $ϕ$ mesons are successfully reconstructed on all experimental targets. The obtained yields are converted to the total production cross section, assuming a kinematical distribution of the event generator JAM. The total cross sections derived are 2.0 $\pm$ 0.9 (stat.) $\pm$ 1.0 (syst.) mb on the carbon target and 10.3 $\pm$ 4.4 (stat.) $\pm$ 4.4 (syst.) mb on the copper target. The mass-number dependence of the cross section is discussed using the parameter $α$, defined as $σ\propto A^α$, resulting in $α= $ 0.99 $\pm$ 0.38 (stat.) $\pm$ 0.34 (syst.). The extrapolation to $A=1$, which means that the cross section of proton-proton reactions, is in good agreement with the existing measurements at comparable energies.

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arXiv2024

Differentiable High-Order Markov Models for Spectrum Prediction

The advent of deep learning and recurrent neural networks revolutionized the field of time-series processing. Therefore, recent research on spectrum prediction has focused on the use of these tools. However, spectrum prediction, which involves forecasting wireless spectrum availability, is an older field where many "classical" tools were considered around the 2010s, such as Markov models. This work revisits high-order Markov models for spectrum prediction in dynamic wireless environments. We introduce a framework to address mismatches between sensing length and model order as well as state-space complexity arising with large order. Furthermore, we extend this Markov framework by enabling fine-tuning of the probability transition matrix through gradient-based supervised learning, offering a hybrid approach that bridges probabilistic modeling and modern machine learning. Simulations on real-world Wi-Fi traffic demonstrate the competitive performance of high-order Markov models compared to deep learning methods, particularly in scenarios with constrained datasets containing outliers.

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