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

A Subblock Partition Of Multi-Layer Pattern Based Image Classification Approach

Since traditional partition approach may construct very different image representation because of the changed locations of objects in the same image, a subblock partition of multi-layer pattern method for image representation is proposed. The saliency windows straddled by superpixels are utilized to partition the image into multi-layer pattern subblocks. Then all the subblocks are combined to a three order tensor. Comparing to the results of image classification item of Pascal Voc 2007 Challenge，it indicates that the proposed representation method is robust to the varied object locations and achieves better performance than other approaches. CCS Concepts Computing methodologies➝Computer vision • Computing methodologies➝ Machine learnin

Low-Rank Optimization Dictionary Training for Image Classification

Bag-of-words model has been extremely popular in image categorization. The method of constructing the dictionary is important. In this paper a category constrained low-rank optimization dictionary training approach is proposed for the dictionary construction. Through the low-rank optimization, the rank of the coefficient matrix constructed by same category images is minimized. Experimental results show that the proposed method can obtain better performance on two standard image databases (Caltech-101 and Caltech-256) than not employing the category constrained low-rank optimization

Structure Design and Implementation of the Passive μ-DMFC

A dual-chamber anode structure is proposed in order to solve two performance problems of the conventional passive micro direct methanol fuel cell (μ-DMFC). One of the problems is the unstable performance during long time discharge. The second problem is the short operating time. In this structure, low concentration chamber is filled with methanol solution with appropriate concentration for the μ-DMFC. Pure methanol in high concentration chamber diffuses to the low concentration chamber to keep the concentration of methanol solution suitable for long-term discharge of μ-DMFC. In this study, a Nafion-Polytetrafluoroethylene (PTFE) composite membrane is inserted between the two chambers to conduct pure methanol. The experimental results during long-term discharge show that the stable operating time of passive μ-DMFC increases by nearly 2.3 times compared to a conventional one with the same volume. These results could be applied to real products

Deep learning instance segmentation framework for burnt area instances characterization

The resemblance of burnt areas with other bright features undermines the certainty of wildfire detection. Bare surfaces and water reflection mislead and directly affect the detection rate. As of now, burnt area characterization and detection of resembling bright features are confined to conventional approaches (change detection, machine learning techniques, semantic segmentation). Consequently, the presented research article established an innovative deep learning instance segmentation model ahead of semantic segmentation. Transfer learning is employed on the ResNet-50/101 as the backbone. For burnt area detection and segmentation, the best performance with deep learning reported in the literature was 98%. The proposed technique was trained using variant regions (datasets) and evaluated precision based on IOU threshold, F1-Score, kappa, recall, missed & detection rate, with an overall accuracy of 98.5%. The research work provides the accurate groundwork for the hybrid qualitative and comparative quantitative analysis among classifiers (U-Net Classifier), capsule-based segmentation models (SegCaps, BA_EnCaps), semantic segmentation models (PSPNET, DeepLabV3) keeping the backbone (ResNet-50) and hyperparameters configuration identical. The suggested model indicated that the instance segmentation deep learning approach outperforms primitive techniques by presenting a greater detection rate and segmentation accuracy. The research inferred that compared to primitive approaches, integration of bright and resemble feature detection support burnt area characterization that localizes and characterizes each smallest fragmented overlapped burnt area instance (feature part)

An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform

The Empirical Wavelet Transform (EWT), which has a reliable mathematical derivation process and can adaptively decompose signals, has been widely used in mechanical applications, EEG, seismic detection and other fields. However, the EWT still faces the problem of how to optimally divide the Fourier spectrum during the application process. When there is noise interference in the analyzed signal, the parameterless scale-space histogram method will divide the spectrum into a variety of narrow bands, which will weaken or even fail to extract the fault modulation information. To accurately determine the optimal resonant demodulation frequency band, this paper proposes a method for applying Adaptive Average Spectral Negentropy (AASN) to EWT analysis (AEWT): Firstly, the spectrum is segmented by the parameterless clustering scale-space histogram method to obtain the corresponding empirical mode. Then, by comprehensively considering the Average Spectral Negentropy (ASN) index and correlation coefficient index on each mode, the correlation coefficient is used to adjust the ASN value of each mode, and the IMF with the highest value is used as the center frequency band of the fault information. Finally, a new resonant frequency band is reconstructed for the envelope demodulation analysis. The experimental results of different background noise intensities show that the proposed method can effectively detect the repetitive transients in the signal

Study on the Influence of Different Factors on Pneumatic Conveying in Horizontal Pipe

Aiming at the problems of high energy consumption and particle breakage in the pneumatic conveying process of large-scale breeding enterprises, in this paper, based on the theoretical calculated value of particle suspension velocity, a computational fluid model and a discrete element model are established based on computational fluid dynamics (CFD) and discrete element method (DEM). Then, through the numerical simulation of gas-solid two-phase flow, the influence of four factors of conveying wind speed, particle mass flow rate, pipe diameter, and particle size on the velocity distribution of particles in a horizontal pipe, dynamic pressure change in the pipe, pressure drop in the pipe, and solid mass concentration are studied. The results show that the k-ε turbulence model can better simulate the movement of gas-solid two-phase flow, and through the analysis of the simulation, the influence of four different factors on the conveying characteristics is obtained, which provides a scientific basis for the construction of the conveying line

The Hydraulic and Boundary Characteristics of a Dike Breach Based on Cluster Analysis

It is important to determine the hydraulic boundary eigenvalues of typical embankment breaches before carrying out research on their occurrence mechanisms and assessing their repair technology. However, it is difficult to obtain the hydraulic boundary conditions of the typical levee breaches accurately with minor or incomplete measured data due to the complexity and instability of the levee breach. Based on more than 100 groups of domestic and foreign test data of embankment/earth dam failures, the correlation between the hydraulic boundary eigenvalues of a breach was established based on the cluster analysis approach. Additionally, the missing values were imputed after correlating and fitting. Meanwhile, the hydraulic boundary parameters and the related equations of a generalized typical breach were obtained through the statistical analysis of the probability density of the dimensionless eigenvalues of the breach. The analysis showed that the width of the breach mainly ranges in 20~100 m, while the water head of the breach is 4~12 m, and the velocity of the breach is 2~8 m/s. The distribution probabilities of all them are about 64~71%. The probability density of the width-to-depth ratio and the Froude number of the breach are both subject to normal distribution characteristics. The distribution frequency of the width-to-depth ratio of 3~8 is approximately 55%, and the Froude number of 0.4~0.8 is approximately 60%. These methods and findings might provide valuable support for the statistical research of the boundary and hydraulic characteristics of the breach, and the closure technology of breach