Discovery potential for supernova relic neutrinos with slow liquid scintillator detectors

Detection of supernova relic neutrinos could provide key support for our current understanding of stellar and cosmological evolution, and precise measurements of these neutrinos could yield novel insights into the universe. In this paper, we studied the detection potential of supernova relic neutrinos using linear alkyl benzene (LAB) as a slow liquid scintillator. The linear alkyl benzene features good separation of Cherenkov and scintillation lights, thereby providing a new route for particle identification. We further addressed key issues in current experiments, including (1) the charged current background of atmospheric neutrinos in water Cherenkov detectors and (2) the neutral current background of atmospheric neutrinos in typical liquid scintillator detectors. A kiloton-scale LAB detector at Jinping with $\mathcal{O}$(10) years of data could discover supernova relic neutrinos with a sensitivity comparable to that of large-volume water Cherenkov detectors, typical liquid scintillator detectors, and liquid argon detectors.Comment: 9 pages, 6 figure

Design, characterization, and sensitivity of the supernova trigger system at Daya Bay

Providing an early warning of galactic supernova explosions from neutrino signals is important in studying supernova dynamics and neutrino physics. A dedicated supernova trigger system has been designed and installed in the data acquisition system at Daya Bay and integrated into the worldwide Supernova Early Warning System (SNEWS). Daya Bay's unique feature of eight identically-designed detectors deployed in three separate experimental halls makes the trigger system naturally robust against cosmogenic backgrounds, enabling a prompt analysis of online triggers and a tight control of the false-alert rate. The trigger system is estimated to be fully sensitive to 1987A-type supernova bursts throughout most of the Milky Way. The significant gain in sensitivity of the eight-detector configuration over a mass-equivalent single detector is also estimated. The experience of this online trigger system is applicable to future projects with spatially distributed detectors.Comment: 8 pages, 6 figures, to be submitted to Astroparticle Physic

Understanding Usage Intention of Social Media’s Innovative Functions: Based on Expanded Innovation Diffusion Theory

Drawing upon expanded innovation diffusion theory (IDT), this study investigates the social media users’ usage intention toward its innovative functions. 532 data were collected from the Chinese leading social media—WeChat. The results show that the relative advantage, ease of use, trialability, observability, subjective norm and image have positive effect on users’ usage intention. Whereas compatibility has no significant impact. Based upon these findings, we discussed the theoretical contributions and practical implication of this study

Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow

Optical flow estimation has made great progress, but usually suffers from degradation under adverse weather. Although semi/full-supervised methods have made good attempts, the domain shift between the synthetic and real adverse weather images would deteriorate their performance. To alleviate this issue, our start point is to unsupervisedly transfer the knowledge from source clean domain to target degraded domain. Our key insight is that adverse weather does not change the intrinsic optical flow of the scene, but causes a significant difference for the warp error between clean and degraded images. In this work, we propose the first unsupervised framework for adverse weather optical flow via hierarchical motion-boundary adaptation. Specifically, we first employ image translation to construct the transformation relationship between clean and degraded domains. In motion adaptation, we utilize the flow consistency knowledge to align the cross-domain optical flows into a motion-invariance common space, where the optical flow from clean weather is used as the guidance-knowledge to obtain a preliminary optical flow for adverse weather. Furthermore, we leverage the warp error inconsistency which measures the motion misalignment of the boundary between the clean and degraded domains, and propose a joint intra- and inter-scene boundary contrastive adaptation to refine the motion boundary. The hierarchical motion and boundary adaptation jointly promotes optical flow in a unified framework. Extensive quantitative and qualitative experiments have been performed to verify the superiority of the proposed method

Rare Helium-Bearing Compound FeO2He Stabilized at Deep-Earth Conditions

There is compelling geochemical evidence for primordial helium trapped in Earth’s lower mantle, but the origin and nature of the helium source remain elusive due to scarce knowledge on viable helium-bearing compounds that are extremely rare. Here we explore materials physics underlying this prominent challenge. Our structure searches in conjunction with first-principles energetic and thermodynamic calculations uncover a remarkable helium-bearing compound FeO2He at high pressure-temperature conditions relevant to the core-mantle boundary. Calculated sound velocities consistent with seismic data validate FeO2He as a feasible constituent in ultralow velocity zones at the lowermost mantle. These mutually corroborating findings establish the first and hitherto only helium-bearing compound viable at pertinent geophysical conditions, thus providing vital physics mechanisms and materials insights for elucidating the enigmatic helium reservoir in deep Earth

On Tightness of the Tsaknakis-Spirakis Algorithm for Approximate Nash Equilibrium

Finding the minimum approximate ratio for Nash equilibrium of bi-matrix games has derived a series of studies, started with 3/4, followed by 1/2, 0.38 and 0.36, finally the best approximate ratio of 0.3393 by Tsaknakis and Spirakis (TS algorithm for short). Efforts to improve the results remain not successful in the past 14 years. This work makes the first progress to show that the bound of 0.3393 is indeed tight for the TS algorithm. Next, we characterize all possible tight game instances for the TS algorithm. It allows us to conduct extensive experiments to study the nature of the TS algorithm and to compare it with other algorithms. We find that this lower bound is not smoothed for the TS algorithm in that any perturbation on the initial point may deviate away from this tight bound approximate solution. Other approximate algorithms such as Fictitious Play and Regret Matching also find better approximate solutions. However, the new distributed algorithm for approximate Nash equilibrium by Czumaj et al. performs consistently at the same bound of 0.3393. This proves our lower bound instances generated against the TS algorithm can serve as a benchmark in design and analysis of approximate Nash equilibrium algorithms