Data release for neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCoredoi:10.7910/DVN/U20MMBHarvard Dataverse2024-09-032IceCube Collaboration, 2024, "Data release for neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore", https://doi.org/10.7910/DVN/U20MMB, Harvard Dataverse, V2Data release for neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCoredoi:10.7910/DVN/U20MMBIceCube Collaboration2024-08-21Harvard DataverseIceCube CollaborationShiqi YuYu, Shiqi2024-08-22PhysicsNeutrino oscillationconvolutional neural networkPublic data release from the IceCube collaboration for the result in arXiv:2405.02163 ("Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore"). This public dataset contains two \DeltaLLH maps of the oscillation parameters assuming normal and inverted ordering separately. The absolute difference between the best-fit points in the two orderings has been removed from this release. The details can be found in the README.<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>arXiv:2405.02163 [hep-ex]arXiv:2405.02163 [hep-ex]README.txttext/plainsaved_io_delta_LLH_map.npythis npy file contains the scanned grid points, the -2ΔLLH map, and the best-fit point assuming inverted ordering of neutrino massesapplication/octet-streamsaved_no_delta_LLH_map.npythis npy file contains the scanned grid points, the -2ΔLLH map, and the best-fit point assuming normal ordering of neutrino massesapplication/octet-streamscript.pytext/x-python-script