Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

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

JUNO Sensitivity to Invisible Decay Modes of Neutrons

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $n \rightarrow { inv}$ and $nn \rightarrow { inv}$. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$.Comment: 28 pages, 7 figures, 4 table

Evaluation and measurement methods for the surface radon exhalation rate of buildings

Half of the natural radiation dose to the human body comes from indoor radon and its progeny, inhaling of which plays a key role in the development of lung cancer. Given the relationship between the radon exhalation rate (RnER) and the indoor radon concentration, accurate determination and control of the former directly affect the control and protection of the latter. In this study, a method was developed to estimate the actual RnER of building walls through building material samples. The surface RnER of the wall of any thickness that was constructed of any building material could be calculated by its intrinsic RnER value and radon diffusion length, which could be obtained by measuring the RnER of the pre-treated building material sample models through the activated carbon box-γ spectroscopic method. The experimental results indicated that the deviation between the calculated wall surface RnER of the building and the measured wall surface RnER of the building was &lt;5.1%. Therefore, the intrinsic RnER of building materials could be regarded as an evaluation index for the RnER of buildings. </jats:p

Development of a thoron chamber for calibration of thoron monitors under natural wind speed conditions

Abstract To examine the response of diffusion-type detectors for thoron under wind speeds similar to natural air ventilation, a special design thoron chamber was developed with a dynamic circulating air-flow field forced by fans. Wind speeds of 0–0.52 m s−1 were adjusted by control of the fan rotation rate according to a linear model, with higher wind speeds contributing to more homogenous air flow status. Thoron concentrations, ranging between 3.2 × 103 and 3.7 × 104 Bq m−3, were easily available through different injection conditions and 220Rn gas sources with high and stable emanation coefficient. The stability and homogeneity of thoron concentrations was controlled within 5.0% and the concentrations in the direction of wind speed had minimal differences compared with the other direction. Higher wind speeds also improved the stability and homogeneity of thoron concentrations. The design and construction of the thoron chamber functioned well in controlling thoron concentration. The response of an AlphaGUARD monitor to thoron was examined in the thoron chamber under different wind speeds. The study revealed a monitor response to thoron (rates of thoron infiltration into the detection chamber of the monitor) respectively was from 0.044 to 0.065 under winds speeds from 0.05 to 0.51 m s−1. Reproducible and controlled expourse conditions can be provided for testing thoron monitors.</jats:p

Continuous cryogenic adsorption adjustments of radon in air using carbon-based microporous adsorbents

In underground low-background laboratories (LRBL), minimizing radon concentration is crucial. This study developed a cryogenic Rn adsorption system, which operated for more than 5 h at a flow rate of 60 L/min. The dynamic adsorption coefficients (Kd) of Rn on four carbon-based adsorbents were measured at 293 K, 243 K and 223 K. Combined with the results of N2-adsorption and desorption,XPS, FTIR and SEM test results indicated that the larger the micropore volume within the 0.5–0.7 nm range, the higher the the Kd of Rn adsorption of the adsorbent, and this difference becomes more obvious with the decrease of adsorption temperature. CarbosieveS-III exhibited the highest Kd at 223 K (436 L/g). Adsorption penetration curves of each component of Rn-containing air on activated carbon were measured using an infrared gas analyzer and a RAD7 radon meter. The CO2 concentration gradient was adjusted, and the Kd of all adsorbents (including reduced carbon molecular sieves) were measured. Experimental results revealed that CO2 acts as the dominant competitive adsorbate for radon capture when water vapor interference was eliminated