D. Cao

Some new progress on the light absorption properties of linear alkyl benzene solvent

Linear alkyl benzene(LAB) will be used as the solvent in a liquid scintillator mixture for the JUNO antineutrino experiment. Its light absorption properties should therefore be understood prior to its effective use in the experiment. Attenuation length measurements at a light wavelength of 430 nm have been performed on samples of LAB prepared for the JUNO experiment. Inorganic impurities in LAB have also been studied for their possibilities of light absorption in our wavelength of interest. In view of a tentative plan by the JUNO collaboration to utilize neutron capture with hydrogen in the detector, we also present in this work a preliminary study on the carbon–hydrogen ratio and the attenuation length of the samples.Linear alkyl benzene(LAB) will be used as the solvent in a liquid scintillator mixture for the JUNO antineutrino experiment. Its light absorption properties should therefore be understood prior to its effective use in the experiment. Attenuation length measurements at a light wavelength of 430 nm have been performed on samples of LAB prepared for the JUNO experiment. Inorganic impurities in LAB have also been studied for their possibilities of light absorption in our wavelength of interest. In view of a tentative plan by the JUNO collaboration to utilize neutron capture with hydrogen in the detector, we also present in this work a preliminary study on the carbon–hydrogen ratio and the attenuation length of the samples.

Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a benchmark case and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multi-exponential relationship with respect to the number of PMTs and hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction.