Measurement of the proton light response of various LAB based scintillators and its implication for supernova neutrino detection via neutrino-proton scattering

The proton light output function in electron-equivalent energy of various scintillators based on linear alkylbenzene (LAB) has been measured in the energy range from 1 MeV to 17.15 MeV for the first time. The measurement was performed at the Physikalisch-Technische Bundesanstalt (PTB) using a neutron beam with continuous energy distribution. The proton light output data is extracted from proton recoil spectra originating from neutron-proton scattering in the scintillator. The functional behavior of the proton light output is described succesfully by Birks' law with a Birks constant kB between (0.0094 +/- 0.0002) cm/MeV and (0.0098 +/- 0.0003) cm/MeV for the different LAB solutions. The constant C, parameterizing the quadratic term in the generalized Birks law, is consistent with zero for all investigated scintillators with an upper limit (95% CL) of about 10^{-7} cm^2/MeV^2. The resulting quenching factors are especially important for future planned supernova neutrino detection based on the elastic scattering of neutrinos on protons. The impact of proton quenching on the supernova event yield from neutrino-proton scattering is discussed.Comment: 12 pages, 17 figures, 4 tables, updated version for publication in Eur.Phys.J.

The Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks

We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from electron density. The output of the network is used as a non-local correction to the conventional local and semi-local kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. Numerical noise inherited from the non-linearity of the neural network is identified as the major challenge for the model. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models