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

System Cognition and Analytic Technology of Cultivated Land Quality from a Data Perspective

As cultivated land quality has been paid more and more scientific attention, its connotation generalization and cognitive bias are widespread, bringing many challenges to the investigation and evaluation of regional cultivated land quality and its data analysis and mining. Establishing a systematic and interdisciplinary cognitive approach to cultivated land quality is urgent and necessary. Therefore, we explored and developed a conceptual framework of the model for the cultivated land quality analysis from the data perspective, including cultivated land quality ontology, mapping, correlation, and decision models. We identified the primary content of cultivated land quality perceptions and four cognitive mechanisms. We built vital technologies, such as the collaborative perception of the quality of cultivated land, intelligent treatment, diagnostic evaluation, and simulation prediction. Applying this analysis framework, we sorted out the frequency of indicators that characterize the function of cultivated land according to the literature in recent years and have built the cognitive system of cultivated land quality in the black soil region of Northeast China. The system’s central component was production capacity and it had three components: a foundation, a guarantee, and an effect. The black soil region cultivated land quality evaluation system has seven purposes involving 20–31 key indicators: production supply, threat control, farmland infrastructure regulation, cultivated land ecological maintenance, economics, social culture, and environmental protection. In various application contexts, the system had many critical supporting technologies. The results demonstrate that the framework has strong adaptability, efficiency, and scalability, which might offer a theoretical direction for further studies on the evaluation of the quality of cultivated land in the area. The analysis framework established in this study is helpful to deepen the understanding of cultivated land quality systems from the perspective of big data. Taking the big data of cultivated land quality as the driving force, combined with the technical methods of cultivated land quality analysis, the evaluation results of cultivated land quality under different scenarios and different objectives are optimized. In addition, the framework can serve the practice of farmland management and engineering improvement, adapt to the management needs of different objects and different scales, and achieve the combination of theory and practice.</jats:p

System Cognition and Analytic Technology of Cultivated Land Quality from a Data Perspective

As cultivated land quality has been paid more and more scientific attention, its connotation generalization and cognitive bias are widespread, bringing many challenges to the investigation and evaluation of regional cultivated land quality and its data analysis and mining. Establishing a systematic and interdisciplinary cognitive approach to cultivated land quality is urgent and necessary. Therefore, we explored and developed a conceptual framework of the model for the cultivated land quality analysis from the data perspective, including cultivated land quality ontology, mapping, correlation, and decision models. We identified the primary content of cultivated land quality perceptions and four cognitive mechanisms. We built vital technologies, such as the collaborative perception of the quality of cultivated land, intelligent treatment, diagnostic evaluation, and simulation prediction. Applying this analysis framework, we sorted out the frequency of indicators that characterize the function of cultivated land according to the literature in recent years and have built the cognitive system of cultivated land quality in the black soil region of Northeast China. The system’s central component was production capacity and it had three components: a foundation, a guarantee, and an effect. The black soil region cultivated land quality evaluation system has seven purposes involving 20–31 key indicators: production supply, threat control, farmland infrastructure regulation, cultivated land ecological maintenance, economics, social culture, and environmental protection. In various application contexts, the system had many critical supporting technologies. The results demonstrate that the framework has strong adaptability, efficiency, and scalability, which might offer a theoretical direction for further studies on the evaluation of the quality of cultivated land in the area. The analysis framework established in this study is helpful to deepen the understanding of cultivated land quality systems from the perspective of big data. Taking the big data of cultivated land quality as the driving force, combined with the technical methods of cultivated land quality analysis, the evaluation results of cultivated land quality under different scenarios and different objectives are optimized. In addition, the framework can serve the practice of farmland management and engineering improvement, adapt to the management needs of different objects and different scales, and achieve the combination of theory and practice

Temporal and Spatial Characteristics Endogenous Hormone Regulation During Male Cone Development of Dacrydium pectinatum

Abstract Background: Dacrydiumpectinatum de Laubenfels is a perennial gymnosperm dominant in tropical montane rain forests. Due to severe damages by excessive deforestation, typhoons, and other external forces, the population of the species has been significantly reduced. Furthermore, natural regeneration is poor. In order to better understand the reproductive process in D. pectinatum, we examined the morphological and anatomical changes during the development of male cone and analyzed the endogenous hormone dynamics.Results: Our study indicates that D. pectinatum male buds become distinguishable in April in tropical montane rain forests, while microspore sac forms in September and pollen mother cell forms and divides in December. Pollen grains mature and disperse in the following February. A mature male cone averages 8.5 mm in length. Level of GA, IAA, ABA and JA and their ratios fluctuated during late August to late November when sporogenous tissues were actively differentiated.Conclusions: The differentiation of sporogenous tissues is accompanied by variations in levels of endogenous hormones (GA, ABA, IAA, and JA) and their balances. The new insights about the cone development in D. pectinatum lay the foundation for future cone induction with hormones and study of factors contributing to the species’ low rate of seed germination.</jats:p

Triphenylphosphine‑<i>m</i>‑sulfonate/Carbon Tetrabromide as an Efficient and Easily Recoverable Catalyst System for Friedel–Crafts Alkylation of Indoles with Carbonyl Compounds or Acetals

A solid complex, conveniently prepared from commercially available sodium triphenylphosphine-<i>m</i>-sulfonate (TPPMS) and carbon tetrabromide, can be used as an easily recoverable and reusable catalyst system for Friedel–Crafts alkylation of indoles with carbonyl compounds or acetals to produce bis­(indolyl)­alkane products (BIAs)

Green Tandem [5C + 1C] Cycloaromatization of α‑Alkenoyl Ketene Dithioacetals and Nitroethane in Water: Eco-Friendly Synthesis of <i>Ortho</i>-Acylphenols

For the first time, an eco-friendly and sustainable tandem [5C + 1C] cycloaromatization of α-alkenoyl ketene dithioacetals and nitroethane in water for the efficient synthesis of ortho-acylphenols was reported. In refluxing water, a range of α-alkenoyl ketene dithioacetals and nitroethane smoothly underwent tandem Michael addition/cyclization/aromatization reactions in the presence of 2.0 equivalents of DBU to provide various ortho-acylphenols in excellent yields. The green approach to ortho-acylphenols not only avoided the use of harmful organic solvents, which could result in serious environmental and safety issues, but also exhibited fascinating features such as good substrate scope, excellent yields, simple purification for desired products, ease of scale-up, and reusable aqueous medium