Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Updated reference model for lithospheric heat production and geoneutrino flux

We report a critical assessment of the abundance and distribution of the heat producing elements in the lithospheric and provide insights into its heat production, physical properties, and geoneutrino flux. The energy yield from these elements are then inputs for calculating the energy budget of the mantle and bulk Earth. We couple and compare these insights with mature and higher resolution methods for modeling lithospheric heat production. Our updated 3D model for heat and geoneutrino production of the global lithosphere ascribes U, Th, and K concentrations to 1° x 1° x 9 layers as specified by the geophysical model LITHO1.0. The physical state of this lithosphere is constrained by data from seismic and gravity models. Density and seismic wave velocity uncertainties are derived from a comparison of LITHO1.0 with a high-resolution surface wave tomography model of the United States. Uncertainties in layer thickness are determined from a comparison of crustal thickness models, using seismic and gravity measurements. The chemical state of each layer is determined using literature data and, for the middle and lower crust, from seismological-compositional relationships. Geochemical uncertainties are determined from natural variation in datasets. Input uncertainties are propagated through Monte Carlo methods, providing uncertainty on every parameter calculated from the model. The heat production in each layer voxel (1° x 1° x layer thickness) is calculated and summed for bulk crustal and lithospheric heat production. Surface and Moho heat flux results from our model are compared with global models of heat flux. The geoneutrino flux is calculated for current and proposed detectors and compared with previous estimates. Uncertainty correlations and the sensitivity of various modeling parameters, particularly regarding the calculation of abundances in the middle and lower crust, are assessed