D. Martin

activity

20202026

most citedMeasurements of neutrino oscillation parameters from the T2K experiment using

3.6 \times 1 0^{21}

protons on target

126 citations · 517 across the 32 of their papers we have counts for

collaborators

Showing 2026Show all

3 papers · 1 filter

hep-ex2026

Search for the $^{16} O (ppp) \to^{13} C π^{+} π^{+} e^{+}$ Decay Mode in Super-Kamiokande Using Machine Learning Techniques

Kamiokande Collaboration, J. Feng, K. Abe +296

We report a new partial lifetime limit of $4.2 \times 1 0^{32}$ years for the trinucleon decay mode $^{16} O (ppp) \to^{13} C π^{+} π^{+} e^{+}$ , obtained from a searc…

hep-ex2026

First double-differential measurement of pionless charged-current muon neutrino interactions using kinematic imbalance observables on carbon and oxygen with the T2K experiment

K. Abe, S. Abe, H. Adhikary +402

We report the first measurement of muon-neutrino charged-current cross section as a function of kinematic imbalance (KI) observables on oxygen with no pions and at least one proton…

hep-ex2026

Signal selection and model-independent extraction of pionless charged-current muon neutrino cross section using double-differential kinematic imbalance observables on carbon and oxygen with the T2K experiment

K. Abe, S. Abe, H. Adhikary +402

We present the first joint measurement of muon neutrino CC $0 π N p$ interactions on carbon and oxygen targets, in two double-differential kinematic imbalance (KI) observable spaces, $…

Search for the 16O(ppp)→13Cπ+π+e+ Decay Mode in Super-Kamiokande Using Machine Learning Techniques

First double-differential measurement of pionless charged-current muon neutrino interactions using kinematic imbalance observables on carbon and oxygen with the T2K experiment

Signal selection and model-independent extraction of pionless charged-current muon neutrino cross section using double-differential kinematic imbalance observables on carbon and oxygen with the T2K experiment

Search for the $^{16} O (ppp) \to^{13} C π^{+} π^{+} e^{+}$ Decay Mode in Super-Kamiokande Using Machine Learning Techniques