Showing 1–33 of 33 results for author: Luo, P

Lost in Aggregation: A Multi-Scale Diagnostic Benchmark for LLM Spatial Navigation

Authors: Yuhan jiang, Peng Luo, Liqiu Meng

Abstract: Large language models (LLMs) are increasingly deployed as planners and assistants in tasks with inherent spatial structure, such as navigation and route planning, yet they remain brittle in sequential spatial reasoning. We ask not merely whether LLMs fail at navigation but where in the spatial-cognition pipeline they get lost. We introduce a multi-scale diagnostic benchmark that decomposes maze na… ▽ More Large language models (LLMs) are increasingly deployed as planners and assistants in tasks with inherent spatial structure, such as navigation and route planning, yet they remain brittle in sequential spatial reasoning. We ask not merely whether LLMs fail at navigation but where in the spatial-cognition pipeline they get lost. We introduce a multi-scale diagnostic benchmark that decomposes maze navigation into three cognitive levels drawn from human spatial cognition: Fine (local passability), Meso (junction topology), and Macro (global goal direction). We evaluate three instruction-tuned chat LLMs (GPT-4o, DeepSeek-V3, Llama-3.3-70B) on 1,050 topology-annotated mazes spanning seven sizes (3x3 to 30x30) and three difficulty tiers. The benchmark is organized as three modules. (i) Input acquisition: among four input formats, structured coordinate text is the most navigable, far surpassing rendered images. (ii) Multi-scale representation: end-to-end one-shot navigation collapses to near zero by 10x10 for every model, yet the same models respond to isolated single-level probes (Fine, Meso, Macro) at 30-75% far beyond that size. A multi-hot first-error analysis localizes failures to Meso junction choices (59%) and Fine perception (39%), with global direction almost never at fault (1%). The barrier is therefore the cross-scale aggregation of individually available competences over a long sequential plan, not any single perceptual deficit. (iii) Hierarchical route planning: delegating per-step execution to a deterministic walker and querying the LLM only at junctions, with an explicit cell-type prompt, lifts GPT-4o success by up to 92 points at mid sizes, but the same scaling wall re-emerges by 30x30. We release the benchmark, mazes, and code as a reusable diagnostic instrument for spatial reasoning in LLMs, available at https://yuhanjiang415.github.io/lost-in-aggregation/. △ Less

Submitted 20 June, 2026; originally announced June 2026.

Reinforcement Learning-Enabled Agent for Transmitter Optimization in Digital-Analog Radio-over-Fiber Fronthaul

Authors: Junhao Zhao, Huayuan Qin, Ouhan Huang, Zhongya Li, Chengxi Wang, Boyu Dong, Liangtao Chen, Xuyu Deng, An Yan, Penghao Luo, Renle Zheng, Yongzhu Hu, Aolong Sun, Yinjun Liu, Sizhe Xing, Nan Chi, Junwen Zhang

Abstract: Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization… ▽ More Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization taps coefficients, which jointly affect quantization noise, nonlinear distortion, and bandwidth-induced inter-symbol interference (ISI). Conventional grid search-based optimization is computationally prohibitive and impractical for optical communication. In this work, we propose a reinforcement-learning (RL)-enabled DA-RoF fronthaul agent architecture, capable of autonomously learning optimal transmitter parameters from end-to-end signal-to-noise ratio (SNR) feedback without a differentiable channel model. Experimental results demonstrate that the trained agent steadily improves SNR through sequential decision making and outperforms baseline, achieving ~2.7-dB SNR improvement for 1- to 4-order DA-RoF transmission, reaching final SNR of 35.8 dB, 42.9 dB, 53.8 dB, and 63.2 dB and supporting 1024-, 4096-, 16384-, 65536-quadrature amplitude modulation (QAM) format, respectively. These results validate that the proposed RL-enabled framework provides online, scalable, and hardware-efficient parameter optimization for DA-RoF fronthaul systems, paving the way toward high-order modulation format and intelligent next-generation radio access networks. △ Less

Submitted 3 June, 2026; originally announced June 2026.

Microsecond-resolved electro-optic dual-comb spectroscopy in the 10~12.5 $μ$m fingerprint region for radical kinetics

Authors: Pei-Ling Luo, I-Yun Chen

Abstract: Dual-comb spectroscopy enables broadband, high-resolution measurements with microsecond temporal resolution, but extending this capability to the 10~12.5 $μ$m molecular fingerprint region remains technically challenging, particularly for transient radical kinetics. Here, we demonstrate microsecond-resolved dual-comb spectroscopy in this spectral range using electro-optic combs and difference-frequ… ▽ More Dual-comb spectroscopy enables broadband, high-resolution measurements with microsecond temporal resolution, but extending this capability to the 10~12.5 $μ$m molecular fingerprint region remains technically challenging, particularly for transient radical kinetics. Here, we demonstrate microsecond-resolved dual-comb spectroscopy in this spectral range using electro-optic combs and difference-frequency generation in an orientation-patterned gallium phosphide crystal. Operation near a turning-point quasi-phase-matching condition at approximately 140 $^\circ$C reduces the wavelength sensitivity of the nonlinear conversion, enabling robust tuning of the idler comb over 83 cm$^{-1}$, corresponding to approximately 1.2 $μ$m near 12 $μ$m, by adjusting only the signal-comb center wavelength while keeping the pump wavelength and crystal temperature fixed. As a demonstration, we perform high-resolution, microsecond-resolved spectroscopy of transient chlorine monoxide (ClO) near 12 $μ$m. Time-resolved dual-comb spectra capture the temporal evolution of ClO produced by the Cl + O$_3$ reaction with a temporal resolution of 1.5 $μ$s, enabling quantitative determination of the ClO formation rate coefficient. These results establish this dual-comb platform as a promising tool for quantitative, microsecond-resolved studies of short-lived radicals, particularly atmospherically relevant halogen oxides. △ Less

Submitted 29 April, 2026; originally announced April 2026.

Comments: 11 pages, 5 figures

Weighted Bayesian Conformal Prediction

Authors: Xiayin Lou, Peng Luo

Abstract: Machine learning predictors are rarely deployed on data that match the data they were calibrated on. Conformal prediction and its risk-control generalization promise distribution-free guarantees at deployment, but only under exchangeability or a covariate shift whose likelihood ratio is known exactly -- and the estimated-ratio case, the one that occurs in practice, is explicitly open, out of reach… ▽ More Machine learning predictors are rarely deployed on data that match the data they were calibrated on. Conformal prediction and its risk-control generalization promise distribution-free guarantees at deployment, but only under exchangeability or a covariate shift whose likelihood ratio is known exactly -- and the estimated-ratio case, the one that occurs in practice, is explicitly open, out of reach of the Bayesian construction available under exchangeability. We reach it from a different starting point: pushing a Dirichlet process prior on the calibration distribution through the likelihood ratio yields the exact Bayesian posterior over the deployed risk. That posterior is exact given the weight function, which at deployment is itself estimated, so we further prove finite-sample upper and lower bounds on the risk the selected threshold actually incurs, in terms of the weight-estimation error and the conditional shift. On synthetic and real covariate shifts, in regression and classification, the realized failure rate then stays close to target where shift-blind selection fails, and what deployment receives is a posterior over the risk of every threshold rather than a single number. △ Less

Submitted 8 August, 2026; v1 submitted 7 April, 2026; originally announced April 2026.

Optical modulation of Gate-Induced Electron Trapping via Persistent Photoconductivity in STO/AlOx Heterostructures

Authors: Peiwen Luo, Huizhong Zeng, Bin Peng, Wanli Zhang, Wenxu Zhang

Abstract: The dynamic interplay between light and electric field control of charge states lies at the heart of developing multifunctional optoelectronic devices. While persistent photoconductivity (PPC) and gate-voltage (VG)-induced electron trapping are well-known phenomena in oxide heterostructures, their mutual coupling remains poorly explored. Here, we report that the non-equilibrium state established b… ▽ More The dynamic interplay between light and electric field control of charge states lies at the heart of developing multifunctional optoelectronic devices. While persistent photoconductivity (PPC) and gate-voltage (VG)-induced electron trapping are well-known phenomena in oxide heterostructures, their mutual coupling remains poorly explored. Here, we report that the non-equilibrium state established by PPC can effectively modulate the efficacy of VG-induced electron trapping in a STO/Al heterostructure. The PPC, characterized by a slow relaxation (8.5 hours at 4 K) after sub-illumination, originates from the re-trapping of photoexcited carriers into deep-level states. In contrast, VG-induced trapping, governed by shallow states, exhibits much faster dynamics ({100 - 400 s). Crucially, we discover that the strength of VG-induced trapping is not constant but is dynamically modulated by the PPC relaxation process. The trapping amplitude is strongly amplified after illumination and recovers only after the deep-level states are substantially refilled, precisely following the PPC relaxation time constant. Furthermore, the electron trapping effect diminishes with increasing temperature and vanishes near the ferroelastic phase transition of STO (110 K), confirming that ferroelastic twin walls and associated oxygen vacancy clusters are the physical origin of the traps. Our findings reveal a novel optical gating mechanism for electron trapping, paving the way for designing non-volatile, optically programmable electronic devices. △ Less

Submitted 16 December, 2025; v1 submitted 1 December, 2025; originally announced December 2025.

Initial performance results of the JUNO detector

Authors: Angel Abusleme, Thomas Adam, Kai Adamowicz, David Adey, Shakeel Ahmad, Rizwan Ahmed, Timo Ahola, Sebastiano Aiello, Fengpeng An, Guangpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, Didier Auguste, Margherita Buizza Avanzini, Andrej Babic, Jingzhi Bai, Weidong Bai, Nikita Balashov, Roberto Barbera, Andrea Barresi , et al. (1114 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper present… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper presents the performance results of the detector, extensively studied during the commissioning of the water phase, the subsequent liquid scintillator filling phase, and the first physics runs. The liquid scintillator achieved an attenuation length of 20.6 m at 430 nm, while the high coverage PMT system and scintillator together yielded about 1785 photoelectrons per MeV of energy deposit at the detector centre, measured using the 2.223 MeV $γ$ from neutron captures on hydrogen with an Am-C calibration source. The reconstructed energy resolution is 3.4% for two 0.511 MeV $γ$ at the detector centre and 2.9% for the 0.93 MeV quenched Po-214 alpha decays from natural radioactive sources. The energy nonlinearity is calibrated to better than 1%. Intrinsic contaminations of U-238 and Th-232 in the liquid scintillator are below 10$^{-16}$ g/g, assuming secular equilibrium. The water Cherenkov detector achieves a muon detection efficiency better than 99.9% for muons traversing the liquid scintillator volume. During the initial science runs, the data acquisition duty cycle exceeded 97.8%, demonstrating the excellent stability and readiness of JUNO for high-precision neutrino physics. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Comments: 38 pages, 23 figures

GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models

Authors: Peng Luo, Xiayin Lou, Yu Zheng, Zhuo Zheng, Stefano Ermon

Abstract: Geospatial modeling provides critical solutions for pressing global challenges such as sustainability and climate change. Existing large language model (LLM)-based algorithm discovery frameworks, such as AlphaEvolve, excel at evolving generic code but lack the domain knowledge and multi-step reasoning required for complex geospatial problems. We introduce GeoEvolve, a multi-agent LLM framework tha… ▽ More Geospatial modeling provides critical solutions for pressing global challenges such as sustainability and climate change. Existing large language model (LLM)-based algorithm discovery frameworks, such as AlphaEvolve, excel at evolving generic code but lack the domain knowledge and multi-step reasoning required for complex geospatial problems. We introduce GeoEvolve, a multi-agent LLM framework that couples evolutionary search with geospatial domain knowledge to automatically design and refine geospatial algorithms. GeoEvolve operates in two nested loops: an inner loop leverages a code evolver to generate and mutate candidate solutions, while an outer agentic controller evaluates global elites and queries a GeoKnowRAG module -- a structured geospatial knowledge base that injects theoretical priors from geography. This knowledge-guided evolution steers the search toward theoretically meaningful and computationally efficient algorithms. We evaluate GeoEvolve on two fundamental and classical tasks: spatial interpolation (kriging) and spatial uncertainty quantification (geospatial conformal prediction). Across these benchmarks, GeoEvolve automatically improves and discovers new algorithms, incorporating geospatial theory on top of classical models. It reduces spatial interpolation error (RMSE) by 13-21% and enhances uncertainty estimation performance by 17\%. Ablation studies confirm that domain-guided retrieval is essential for stable, high-quality evolution. These results demonstrate that GeoEvolve provides a scalable path toward automated, knowledge-driven geospatial modeling, opening new opportunities for trustworthy and efficient AI-for-Science discovery. △ Less

Submitted 25 September, 2025; originally announced September 2025.

Feature-free regression kriging

Authors: Peng Luo, Yilong Wu, Yongze Song

Abstract: Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models -- such as Ordinary Kriging (OK) -- assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanat… ▽ More Spatial interpolation is a crucial task in geography. As perhaps the most widely used interpolation methods, geostatistical models -- such as Ordinary Kriging (OK) -- assume spatial stationarity, which makes it difficult to capture the nonstationary characteristics of geographic variables. A common solution is trend surface modeling (e.g., Regression Kriging, RK), which relies on external explanatory variables to model the trend and then applies geostatistical interpolation to the residuals. However, this approach requires high-quality and readily available explanatory variables, which are often lacking in many spatial interpolation scenarios -- such as estimating heavy metal concentrations underground. This study proposes a Feature-Free Regression Kriging (FFRK) method, which automatically extracts geospatial features -- including local dependence, local heterogeneity, and geosimilarity -- to construct a regression-based trend surface without requiring external explanatory variables. We conducted experiments on the spatial distribution prediction of three heavy metals in a mining area in Australia. In comparison with 17 classical interpolation methods, the results indicate that FFRK, which does not incorporate any explanatory variables and relies solely on extracted geospatial features, consistently outperforms both conventional Kriging techniques and machine learning models that depend on explanatory variables. This approach effectively addresses spatial nonstationarity while reducing the cost of acquiring explanatory variables, improving both prediction accuracy and generalization ability. This finding suggests that an accurate characterization of geospatial features based on domain knowledge can significantly enhance spatial prediction performance -- potentially yielding greater improvements than merely adopting more advanced statistical models. △ Less

Submitted 9 July, 2025; originally announced July 2025.

Reframing Spatial Dependence as Geographic Feature Attribution

Authors: Chuan Chen, Peng Luo

Abstract: Spatial dependence, referring to the correlation between variable values observed at different geographic locations, is one of the most fundamental characteristics of spatial data. The presence of spatial dependence violates the classical statistical assumption of independent and identically distributed observations and implies a high degree of information redundancy within spatial datasets. Howev… ▽ More Spatial dependence, referring to the correlation between variable values observed at different geographic locations, is one of the most fundamental characteristics of spatial data. The presence of spatial dependence violates the classical statistical assumption of independent and identically distributed observations and implies a high degree of information redundancy within spatial datasets. However, this redundancy can also be interpreted as structured information, which has been widely leveraged in spatial modeling, prediction, and explanation tasks. With the rise of geospatial big data and the rapid advancement of deep learning and large models, effectively modeling and characterizing spatial dependence has become essential for enhancing the performance of spatial analysis and uncovering latent spatial processes. From a data-driven perspective, this study proposes a novel interpretation: spatial dependence can be understood as the contribution of geographic location -- specifically, latitude and longitude -- to the observed variation in target variables. To validate this hypothesis, we conduct simulation experiments in which data are generated based on known spatial processes. We train machine learning models to predict variable values using only coordinate information. Subsequently, XAI techniques are employed to quantify the contribution of spatial features. The resulting importance scores are then compared with local indicators of spatial association (LISA). Across a range of spatial process settings, we observe consistently high correlations (greater than 0.94) between coordinate-based contributions and LISA values. These findings offer a new data-driven perspective on spatial dependence, bridging traditional spatial statistical approaches with modern machine learning techniques. △ Less

Submitted 20 June, 2025; originally announced June 2025.

Modeling shared micromobility as a label propagation process for detecting the overlapping communities

Authors: Peng Luo, Chengyu Song, Hao Li, Di Zhu, Fabio Duarte

Abstract: Shared micro-mobility such as e-scooters has gained significant popularity in many cities. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating t… ▽ More Shared micro-mobility such as e-scooters has gained significant popularity in many cities. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns. We applied this model to detect overlapping communities within the e-scooter system in Washington, D.C. The results demonstrate that our algorithm outperforms existing model of overlapping community detection in both efficiency and modularity. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns. △ Less

Submitted 26 January, 2025; originally announced January 2025.

Toward Ethical Spatial Analysis: Addressing Endogenous Bias Through Visual Analytics

Authors: Chuan Chen, Peng Luo, Bo Zhao, Yu Feng, Liqiu Meng

Abstract: Spatial analysis can generate both exogenous and endogenous biases, which will lead to ethics issues. Exogenous biases arise from external factors or environments and are unrelated to internal operating mechanisms, while endogenous biases stem from internal processes or technologies. Although much attention has been given to exogenous biases, endogenous biases in spatial analysis have been largely… ▽ More Spatial analysis can generate both exogenous and endogenous biases, which will lead to ethics issues. Exogenous biases arise from external factors or environments and are unrelated to internal operating mechanisms, while endogenous biases stem from internal processes or technologies. Although much attention has been given to exogenous biases, endogenous biases in spatial analysis have been largely overlooked, and a comprehensive methodology for addressing them is yet to be developed. To tackle this challenge, we propose that visual analytics can play a key role in understanding geographic data and improving the interpretation of analytical results. In this study, we conducted a preliminary investigation using various visualization techniques to explore endogenous biases. Our findings demonstrate the potentials of visual analytics to uncover hidden biases and identify associated issues. Additionally, we synthesized these visualization strategies into a framework that approximates a method for detecting endogenous biases. Through this work, we advocate for the integration of visualization at three critical stages of spatial analysis in order to minimize errors, address ethical concerns, and reduce misinterpretations associated with endogenous biases. △ Less

Submitted 10 December, 2024; originally announced December 2024.

GeoConformal prediction: a model-agnostic framework of measuring the uncertainty of spatial prediction

Authors: Xiayin Lou, Peng Luo, Liqiu Meng

Abstract: Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally important to obtain predictions with uncertainty measures to enhance model credibility and support responsible spatial pred… ▽ More Spatial prediction is a fundamental task in geography. In recent years, with advances in geospatial artificial intelligence (GeoAI), numerous models have been developed to improve the accuracy of geographic variable predictions. Beyond achieving higher accuracy, it is equally important to obtain predictions with uncertainty measures to enhance model credibility and support responsible spatial prediction. Although geostatistic methods like Kriging offer some level of uncertainty assessment, such as Kriging variance, these measurements are not always accurate and lack general applicability to other spatial models. To address this issue, we propose a model-agnostic uncertainty assessment method called GeoConformal Prediction, which incorporates geographical weighting into conformal prediction. We applied it to two classic spatial prediction cases, spatial regression and spatial interpolation, to evaluate its reliability. First, in the spatial regression case, we used XGBoost to predict housing prices, followed by GeoConformal to calculate uncertainty. Our results show that GeoConformal achieved a coverage rate of 93.67%, while Bootstrap methods only reached a maximum coverage of 81.00% after 2000 runs. Next, we applied GeoConformal to spatial interpolation models. We found that the uncertainty obtained from GeoConformal aligned closely with the variance in Kriging. Finally, using GeoConformal, we analyzed the sources of uncertainty in spatial prediction. We found that explicitly including local features in AI models can significantly reduce prediction uncertainty, especially in areas with strong local dependence. Our findings suggest that GeoConformal holds potential not only for geographic knowledge discovery but also for guiding the design of future GeoAI models, paving the way for more reliable and interpretable spatial prediction frameworks. △ Less

Submitted 25 April, 2025; v1 submitted 5 December, 2024; originally announced December 2024.

Dependence of Electrostatic Patch Force Evaluation on the Lateral Resolution of Kelvin Probe Force Microscopy

Authors: Kun Shi, Pengshun Luo, Jinquan Liu, Hang Yin, Zebing Zhou

Abstract: Kelvin Probe Force Microscopy (KPFM) is widely used to measure the surface potential on samples, from which electrostatic patch force can be calculated. However, since the KPFM measurements represent a weighted average of local potentials on the sample, the accuracy of the evaluation critically depends on the precision and lateral resolution of the method. In this paper, we investigate the influen… ▽ More Kelvin Probe Force Microscopy (KPFM) is widely used to measure the surface potential on samples, from which electrostatic patch force can be calculated. However, since the KPFM measurements represent a weighted average of local potentials on the sample, the accuracy of the evaluation critically depends on the precision and lateral resolution of the method. In this paper, we investigate the influence of this averaging effect on patch force estimations using both analytic and numerical methods. First, we derive the correlation functions of patch potential and establish the formulas for calculating the electrostatic patch forces in the parallel-plate geometry, with and without consideration of the KPFM measurement effect. Thus, an analytic method is established to determine the accuracy of patch force evaluation when the statistical parameters of the patch potential and the lateral resolution of the KPFM are given. Second, numerical simulations are employed to explore the dependence of estimated patch forces on the KPFM's lateral resolution under more realistic conditions. Both analytic and numerical results show a similar dependence of the patch force estimation on the patch characteristic size, potential fluctuation and the lateral resolution of the KPFM. It is also found that the underestimation of the patch force becomes less sensitive to the KPFM's resolution as the separation between plates increases. The results of this study could provide useful guidance for the accurate evaluation of electrostatic patch forces using KPFM. △ Less

Submitted 3 November, 2024; originally announced November 2024.

Journal ref: Phys. Rev. D 110, 122007 (2024)

Pervasive impact of spatial dependence on predictability

Authors: Peng Luo, Yongze Song, Wenwen Li, Liqiu Meng

Abstract: Understanding the complex nature of spatial information is crucial for problem solving in social and environmental sciences. This study investigates how the underlying patterns of spatial data can significantly influence the outcomes of spatial predictions. Recognizing unique characteristics of spatial data, such as spatial dependence and spatial heterogeneity, we delve into the fundamental differ… ▽ More Understanding the complex nature of spatial information is crucial for problem solving in social and environmental sciences. This study investigates how the underlying patterns of spatial data can significantly influence the outcomes of spatial predictions. Recognizing unique characteristics of spatial data, such as spatial dependence and spatial heterogeneity, we delve into the fundamental differences and similarities between spatial and non-geospatial prediction models. Through the analysis of six different datasets of environment and socio-economic variables, comparing geospatial models with non-geospatial models, our research highlights the pervasive nature of spatial dependence beyond geographical boundaries. This innovative approach not only recognizes spatial dependence in geographic spaces defined by latitude and longitude but also identifies its presence in non-geographic, attribute-based dimensions. Our findings reveal the pervasive influence of spatial dependence on prediction outcomes across various domains, and spatial dependence significantly influences prediction performance across all spaces. Our findings suggest that the strongest spatial dependence is typically found in geographic space for environment variables, a trend that does not uniformly apply to socio-economic variables. This investigation not only advances the theoretical framework for spatial data analysis, but also proposes new methodologies for accurately capturing and expressing spatial dependence under complex conditions. Our research extends spatial analysis to non-geographic dimensions such as social networks and gene expression patterns, emphasizing the role of spatial dependence in improving prediction accuracy, thereby supporting interdisciplinary applications across fields such as geographic information science, environmental science, economics, sociology, and bioinformatics. △ Less

Submitted 15 September, 2024; v1 submitted 26 August, 2024; originally announced August 2024.

Microwave field vector detector based on the nonresonant spin rectification effect

Authors: Peiwen Luo, Bin Peng, Wanli Zhang, Wenxu Zhang

Abstract: Normal microwave (MW) electromagnetic field detectors convert microwave power into voltages, which results in the loss of the vector characteristics of the microwave field. In this work, we developed a MW magnetic field (h-field) vector detector based on the nonresonant spin rectification effect. By measuring and analyzing the angle dependence of the rectification voltages under nonresonant condit… ▽ More Normal microwave (MW) electromagnetic field detectors convert microwave power into voltages, which results in the loss of the vector characteristics of the microwave field. In this work, we developed a MW magnetic field (h-field) vector detector based on the nonresonant spin rectification effect. By measuring and analyzing the angle dependence of the rectification voltages under nonresonant conditions, we can extract the three components of the h-field. As an initial test of this method, we obtained the h-field distributions at 5.4 GHz generated by a coplanar waveguide with sub-wavelength resolution. Compared to methods using ferromagnetic resonance, this technique offers a faster and more convenient way to determine the spatial distribution of the h-field, which can be used for MW integrated circuit optimization and fault diagnosis. △ Less

Submitted 25 July, 2024; originally announced July 2024.

Long-term evolution of solar activity and prediction of the following solar cycles

Authors: Peixin Luo, Baolin Tan

Abstract: Solar activities have a great impact on modern high-tech systems, such as human aerospace, satellite communication and navigation, deep space exploration, and related scientific research. Therefore, studying the long - term evolution trend of solar activity and accurately predicting the future solar cycles is highly anticipated. Based on wavelet transform and empirical function fitting of the long… ▽ More Solar activities have a great impact on modern high-tech systems, such as human aerospace, satellite communication and navigation, deep space exploration, and related scientific research. Therefore, studying the long - term evolution trend of solar activity and accurately predicting the future solar cycles is highly anticipated. Based on wavelet transform and empirical function fitting of the longest recorded data of the annual average relative sunspot number (ASN) series of 323 years to date, this work decisively verified the existence of the solar century cycles and confirmed that its length is about 104.0 years, and the magnitude has a slightly increasing trend on the time scale of several hundreds of years. Based on this long-term evolutionary trend, we predicted solar cycle 25 and 26 by using phase similar prediction methods. As for the solar cycle 25, its maximum ASN will be about $146.7\pm 33.40$, obviously stronger than solar cycle 24. The peak year will occur approximately in 2024, and the period is about $11\pm 1$ years. As for the solar cycle 26, it will start around 2030, reach the maximum between 2035 and 2036, with maximum ASN of about $133.0\pm 3.200$, and the period is about 10 years. △ Less

Submitted 20 February, 2024; originally announced February 2024.

Uncover the nature of overlapping community in cities

Authors: Peng Luo, Di Zhu

Abstract: Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) in Minnesota, USA, our findings reveal th… ▽ More Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) in Minnesota, USA, our findings reveal that 95.7 % of urban functional complexity stems from the overlapping structure of communities during weekdays. Significantly, our research not only quantifies these overlaps but also reveals their compelling correlations with income and racial indicators, unraveling the complex segregation patterns in U.S. cities. As the first to elucidate the overlapping nature of urban communities, this work offers a unique geospatial perspective on looking at urban structures, highlighting the nuanced interplay of socioeconomic dynamics within cities. △ Less

Submitted 31 January, 2024; originally announced February 2024.

First report of a solar energetic particle event observed by China's Tianwen-1 mission in transit to Mars

Authors: Shuai Fu, Zheyi Ding, Yongjie Zhang, Xiaoping Zhang, Cunhui Li, Gang Li, Shuwen Tang, Haiyan Zhang, Yi Xu, Yuming Wang, Jingnan Guo, Lingling Zhao, Yi Wang, Xiangyu Hu, Pengwei Luo, Zhiyu Sun, Yuhong Yu, Lianghai Xie

Abstract: Solar energetic particles (SEPs) associated with flares and/or coronal mass ejection (CME)-driven shocks can impose acute radiation hazards to space explorations. To measure energetic particles in near-Mars space, the Mars Energetic Particle Analyzer (MEPA) instrument onboard China's Tianwen-1 (TW-1) mission was designed. Here, we report the first MEPA measurements of the widespread SEP event occu… ▽ More Solar energetic particles (SEPs) associated with flares and/or coronal mass ejection (CME)-driven shocks can impose acute radiation hazards to space explorations. To measure energetic particles in near-Mars space, the Mars Energetic Particle Analyzer (MEPA) instrument onboard China's Tianwen-1 (TW-1) mission was designed. Here, we report the first MEPA measurements of the widespread SEP event occurring on 29 November 2020 when TW-1 was in transit to Mars. This event occurred when TW-1 and Earth were magnetically well connected, known as the Hohmann-Parker effect, thus offering a rare opportunity to understand the underlying particle acceleration and transport process. Measurements from TW-1 and near-Earth spacecraft show similar double-power-law spectra and a radial dependence of the SEP peak intensities. Moreover, the decay phases of the time-intensity profiles at different locations clearly show the reservoir effect. We conclude that the double-power-law spectrum is likely generated at the acceleration site, and that a small but finite cross-field diffusion is crucial to understand the formation of the SEP reservoir phenomenon. These results provide insight into particle acceleration and transport associated with CME-driven shocks, which may contribute to the improvement of relevant physical models. △ Less

Submitted 14 July, 2022; originally announced July 2022.

Comments: This manuscript has been accepted for publication in The Astrophysical Journal Letters

Prospects for Detecting the Diffuse Supernova Neutrino Background with JUNO

Authors: JUNO Collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Muhammad Akram, Fengpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Nikita Balashov, Wander Baldini, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Bellato, Antonio Bergnoli, Thilo Birkenfeld, Sylvie Blin , et al. (577 additional authors not shown)

Abstract: We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced n… ▽ More We present the detection potential for the diffuse supernova neutrino background (DSNB) at the Jiangmen Underground Neutrino Observatory (JUNO), using the inverse-beta-decay (IBD) detection channel on free protons. We employ the latest information on the DSNB flux predictions, and investigate in detail the background and its reduction for the DSNB search at JUNO. The atmospheric neutrino induced neutral current (NC) background turns out to be the most critical background, whose uncertainty is carefully evaluated from both the spread of model predictions and an envisaged \textit{in situ} measurement. We also make a careful study on the background suppression with the pulse shape discrimination (PSD) and triple coincidence (TC) cuts. With latest DSNB signal predictions, more realistic background evaluation and PSD efficiency optimization, and additional TC cut, JUNO can reach the significance of 3$σ$ for 3 years of data taking, and achieve better than 5$σ$ after 10 years for a reference DSNB model. In the pessimistic scenario of non-observation, JUNO would strongly improve the limits and exclude a significant region of the model parameter space. △ Less

Submitted 13 October, 2022; v1 submitted 18 May, 2022; originally announced May 2022.

Comments: 29 pages, 11 figures, final published version in JCAP

Journal ref: JCAP 10 (2022) 033

Comparison of Anomalous and Galactic Cosmic Ray Oxygen at 1 au during 1997-2020

Authors: Shuai Fu, Lingling Zhao, Xiaoping Zhang, Pengwei Luo, Yong Li

Abstract: Using quiet-time measurements of element oxygen within the energy range 7.3--237.9 MeV nuc$^{-1}$ from the ACE spacecraft at 1 au, we compare the energy spectra and intensities of anomalous and Galactic cosmic rays (ACRs and GCRs, respectively) during 1997--2020. Our analysis shows that the transition from ACR-dominated spectrum to GCR-dominated spectrum occurs at energies $\sim$15 to $\sim$35 MeV… ▽ More Using quiet-time measurements of element oxygen within the energy range 7.3--237.9 MeV nuc$^{-1}$ from the ACE spacecraft at 1 au, we compare the energy spectra and intensities of anomalous and Galactic cosmic rays (ACRs and GCRs, respectively) during 1997--2020. Our analysis shows that the transition from ACR-dominated spectrum to GCR-dominated spectrum occurs at energies $\sim$15 to $\sim$35 MeV nuc$^{-1}$, and the transition energy $E_t$ is found to be well anticorrelated with varying solar activity. This is the first study of ACR-GCR transition energy dependence on the solar cycle variation. At energies below $E_t$, the index of the power-law ACR-dominated spectrum ($γ_1$) ranges from -2.0 to -0.5, whereas the GCR-dominated spectrum has a power-law index ($γ_2$) changing from 0.3 to 0.8 at energies ranging from $E_t$ to 237.9 MeV nuc$^{-1}$. Both $γ_1$ and $γ_2$ are positively correlated with solar activity. In addition, during the solar cycle 24/25 minimum period, the peak GCR intensity observed by ACE spacecraft is about 8\% above its 2009 value, setting a new record since the space age, while the peak ACR intensity is almost similar to that of the previous two solar cycles with the same pattern of solar magnetic polarity, indicating a different modulation mechanism between ACRs and GCRs. △ Less

Submitted 22 September, 2021; originally announced September 2021.

Radioactivity control strategy for the JUNO detector

Authors: JUNO collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Muhammad Akram, Fengpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Bellato, Antonio Bergnoli, Thilo Birkenfeld, Sylvie Blin , et al. (578 additional authors not shown)

Abstract: JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particula… ▽ More JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz in the default fiducial volume, above an energy threshold of 0.7 MeV. △ Less

Submitted 13 October, 2021; v1 submitted 8 July, 2021; originally announced July 2021.

Comments: 35 pages, 12 figures

The Design and Sensitivity of JUNO's scintillator radiopurity pre-detector OSIRIS

Authors: JUNO Collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Muhammad Akram, Fengpeng An, Guangpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Bellato, Antonio Bergnoli, Thilo Birkenfeld , et al. (582 additional authors not shown)

Abstract: The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $10^{-16}$ g/g of… ▽ More The OSIRIS detector is a subsystem of the liquid scintillator fillling chain of the JUNO reactor neutrino experiment. Its purpose is to validate the radiopurity of the scintillator to assure that all components of the JUNO scintillator system work to specifications and only neutrino-grade scintillator is filled into the JUNO Central Detector. The aspired sensitivity level of $10^{-16}$ g/g of $^{238}$U and $^{232}$Th requires a large ($\sim$20 m$^3$) detection volume and ultralow background levels. The present paper reports on the design and major components of the OSIRIS detector, the detector simulation as well as the measuring strategies foreseen and the sensitivity levels to U/Th that can be reached in this setup. △ Less

Submitted 31 March, 2021; originally announced March 2021.

Comments: 32 pages, 22 figures

Influence of Micro-turbulence on Neoclassical Tearing Mode Onset

Authors: Tonghui Shi, L. Wei, H. H. Wang, E. Li, B. Shen, J. P. Qian, Y. M. Wang, T. Zhang, H. L. Zhao, L. Zeng, Y. Zhang, H. Q. Liu, Q. Ma, D. L. Chen, Z. P. Luo, Y. Y. Li, Z. C. Shen, L. Q. Xu, B. Zhang, M. H. Li, Z. X. Wang, B. L. Ling, X. Z. Gong, Y. Sun, B. Wan

Abstract: Direct evidence of micro-turbulence effect on the onset of neoclassical tearing mode (NTM) is reported for the first time in this letter. A puzzling positive correlation between critical width of seed island of NTM and normalized plasma pressure beta_p is first observed employing a novel method for clearly separating the processes of seed island and the onset of NTM in the EAST tokamak. Different… ▽ More Direct evidence of micro-turbulence effect on the onset of neoclassical tearing mode (NTM) is reported for the first time in this letter. A puzzling positive correlation between critical width of seed island of NTM and normalized plasma pressure beta_p is first observed employing a novel method for clearly separating the processes of seed island and the onset of NTM in the EAST tokamak. Different from the methods developed before, the width of the seed island is well controlled by slowly ramping up the current in resonant magnetic perturbation coils. It is revealed that the positive correlation is mainly attributed to the enhancement of perpendicular transport by micro-turbulence, which overcomes the destabilizing effect of beta_p on the onset of NTM. Reduced magnetohydrodynamics (MHD) modeling well reproduced the two states of nonlinear bifurcations observed in this experiment by including the finite transport effect. This result provides a new route for understanding multi-scale interaction in plasma physics. △ Less

Submitted 29 March, 2021; originally announced March 2021.

Calibration Strategy of the JUNO Experiment

Authors: JUNO collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Muhammad Akram, Fengpeng An, Guangpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Eric Baussan, Marco Bellato, Antonio Bergnoli, Enrico Bernieri, Thilo Birkenfeld , et al. (571 additional authors not shown)

Abstract: We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector ca… ▽ More We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector can achieve a better than 1% energy linearity and a 3% effective energy resolution, required by the neutrino mass ordering determination. △ Less

Submitted 20 January, 2021; v1 submitted 12 November, 2020; originally announced November 2020.

Optimization of the JUNO liquid scintillator composition using a Daya Bay antineutrino detector

Authors: Daya Bay, JUNO collaborations, :, A. Abusleme, T. Adam, S. Ahmad, S. Aiello, M. Akram, N. Ali, F. P. An, G. P. An, Q. An, G. Andronico, N. Anfimov, V. Antonelli, T. Antoshkina, B. Asavapibhop, J. P. A. M. de André, A. Babic, A. B. Balantekin, W. Baldini, M. Baldoncini, H. R. Band, A. Barresi, E. Baussan , et al. (642 additional authors not shown)

Abstract: To maximize the light yield of the liquid scintillator (LS) for the Jiangmen Underground Neutrino Observatory (JUNO), a 20 t LS sample was produced in a pilot plant at Daya Bay. The optical properties of the new LS in various compositions were studied by replacing the gadolinium-loaded LS in one antineutrino detector. The concentrations of the fluor, PPO, and the wavelength shifter, bis-MSB, were… ▽ More To maximize the light yield of the liquid scintillator (LS) for the Jiangmen Underground Neutrino Observatory (JUNO), a 20 t LS sample was produced in a pilot plant at Daya Bay. The optical properties of the new LS in various compositions were studied by replacing the gadolinium-loaded LS in one antineutrino detector. The concentrations of the fluor, PPO, and the wavelength shifter, bis-MSB, were increased in 12 steps from 0.5 g/L and <0.01 mg/L to 4 g/L and 13 mg/L, respectively. The numbers of total detected photoelectrons suggest that, with the optically purified solvent, the bis-MSB concentration does not need to be more than 4 mg/L. To bridge the one order of magnitude in the detector size difference between Daya Bay and JUNO, the Daya Bay data were used to tune the parameters of a newly developed optical model. Then, the model and tuned parameters were used in the JUNO simulation. This enabled to determine the optimal composition for the JUNO LS: purified solvent LAB with 2.5 g/L PPO, and 1 to 4 mg/L bis-MSB. △ Less

Submitted 1 July, 2020; originally announced July 2020.

Comments: 13 pages, 8 figures

Feasibility and physics potential of detecting $^8$B solar neutrinos at JUNO

Authors: JUNO collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Sebastiano Aiello, Muhammad Akram, Nawab Ali, Fengpeng An, Guangpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Eric Baussan, Marco Bellato, Antonio Bergnoli, Enrico Bernieri, David Biare , et al. (572 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory~(JUNO) features a 20~kt multi-purpose underground liquid scintillator sphere as its main detector. Some of JUNO's features make it an excellent experiment for $^8$B solar neutrino measurements, such as its low-energy threshold, its high energy resolution compared to water Cherenkov detectors, and its much large target mass compared to previous liquid s… ▽ More The Jiangmen Underground Neutrino Observatory~(JUNO) features a 20~kt multi-purpose underground liquid scintillator sphere as its main detector. Some of JUNO's features make it an excellent experiment for $^8$B solar neutrino measurements, such as its low-energy threshold, its high energy resolution compared to water Cherenkov detectors, and its much large target mass compared to previous liquid scintillator detectors. In this paper we present a comprehensive assessment of JUNO's potential for detecting $^8$B solar neutrinos via the neutrino-electron elastic scattering process. A reduced 2~MeV threshold on the recoil electron energy is found to be achievable assuming the intrinsic radioactive background $^{238}$U and $^{232}$Th in the liquid scintillator can be controlled to 10$^{-17}$~g/g. With ten years of data taking, about 60,000 signal and 30,000 background events are expected. This large sample will enable an examination of the distortion of the recoil electron spectrum that is dominated by the neutrino flavor transformation in the dense solar matter, which will shed new light on the tension between the measured electron spectra and the predictions of the standard three-flavor neutrino oscillation framework. If $Δm^{2}_{21}=4.8\times10^{-5}~(7.5\times10^{-5})$~eV$^{2}$, JUNO can provide evidence of neutrino oscillation in the Earth at the about 3$σ$~(2$σ$) level by measuring the non-zero signal rate variation with respect to the solar zenith angle. Moveover, JUNO can simultaneously measure $Δm^2_{21}$ using $^8$B solar neutrinos to a precision of 20\% or better depending on the central value and to sub-percent precision using reactor antineutrinos. A comparison of these two measurements from the same detector will help elucidate the current tension between the value of $Δm^2_{21}$ reported by solar neutrino experiments and the KamLAND experiment. △ Less

Submitted 21 June, 2020; originally announced June 2020.

Comments: 29 pages, 14 plots, 7 tables

TAO Conceptual Design Report: A Precision Measurement of the Reactor Antineutrino Spectrum with Sub-percent Energy Resolution

Authors: JUNO Collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad, Sebastiano Aiello, Muhammad Akram, Nawab Ali, Fengpeng An, Guangpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, João Pedro Athayde Marcondes de André, Didier Auguste, Andrej Babic, Wander Baldini, Andrea Barresi, Eric Baussan, Marco Bellato, Antonio Bergnoli, Enrico Bernieri, David Biare , et al. (568 additional authors not shown)

Abstract: The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future re… ▽ More The Taishan Antineutrino Observatory (TAO, also known as JUNO-TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO). A ton-level liquid scintillator detector will be placed at about 30 m from a core of the Taishan Nuclear Power Plant. The reactor antineutrino spectrum will be measured with sub-percent energy resolution, to provide a reference spectrum for future reactor neutrino experiments, and to provide a benchmark measurement to test nuclear databases. A spherical acrylic vessel containing 2.8 ton gadolinium-doped liquid scintillator will be viewed by 10 m^2 Silicon Photomultipliers (SiPMs) of >50% photon detection efficiency with almost full coverage. The photoelectron yield is about 4500 per MeV, an order higher than any existing large-scale liquid scintillator detectors. The detector operates at -50 degree C to lower the dark noise of SiPMs to an acceptable level. The detector will measure about 2000 reactor antineutrinos per day, and is designed to be well shielded from cosmogenic backgrounds and ambient radioactivities to have about 10% background-to-signal ratio. The experiment is expected to start operation in 2022. △ Less

Submitted 18 May, 2020; originally announced May 2020.

Comments: 134 pages, 114 figures

A new quantity for statistical analysis: "Scaling invariable Benford distance"

Authors: Peiyan Luo, Yongqing Li

Abstract: For the first time, we introduce "Scaling invariable Benford distance" and "Benford cyclic graph", which can be used to analyze any data set. Using the quantity and the graph, we analyze some date sets with common distributions, such as normal, exponent, etc., find that different data set has a much different value of "Scaling invariable Benford distance" and different figure feature of "Benford c… ▽ More For the first time, we introduce "Scaling invariable Benford distance" and "Benford cyclic graph", which can be used to analyze any data set. Using the quantity and the graph, we analyze some date sets with common distributions, such as normal, exponent, etc., find that different data set has a much different value of "Scaling invariable Benford distance" and different figure feature of "Benford cyclic graph". We also explore the influence of data size on "Scaling invariable Benford distance", and find that it firstly reduces with data size increasing, then approximate to a fixed value when the size is large enough. △ Less

Submitted 5 March, 2018; v1 submitted 3 March, 2018; originally announced March 2018.

Comments: 4 pages,4 figures

Mid-infrared dual-comb spectroscopy with electro-optic modulators

Authors: Ming Yan, Pei-Ling Luo, Kana Iwakuni, Guy Millot, Theodor W. Hänsch, Nathalie Picqué

Abstract: We demonstrate dual-comb spectroscopy based on difference frequency generation of frequency-agile near-infrared frequency combs, produced with the help of electro-optic modulators. The combs have a remarkably flat intensity distribution and their positions and line spacings can be selected freely by simply dialing a knob. We record, in the 3-micron region, Doppler-limited absorption spectra with r… ▽ More We demonstrate dual-comb spectroscopy based on difference frequency generation of frequency-agile near-infrared frequency combs, produced with the help of electro-optic modulators. The combs have a remarkably flat intensity distribution and their positions and line spacings can be selected freely by simply dialing a knob. We record, in the 3-micron region, Doppler-limited absorption spectra with resolved comb lines within milliseconds. Precise molecular line parameters are retrieved. Our technique holds promise for fast and sensitive time-resolved studies e.g. of trace gases. △ Less

Submitted 29 August, 2016; originally announced August 2016.

Journal ref: Light: Science and Applications 6, e17076 (2017)

Biomechanical conditions of walking

Authors: Y. F. Fan, L. P. Luo, Z. Y. Li, S. Y. Han, C. S. Lv, B. Zhang

Abstract: The development of rehabilitation training program for lower limb injury does not usually include gait pattern design. This paper introduced a gait pattern design by using equations (conditions of walking). Following the requirements of reducing force to the injured side to avoid further injury, we developed a lower limb gait pattern to shorten the stride length so as to reduce walking speed, to d… ▽ More The development of rehabilitation training program for lower limb injury does not usually include gait pattern design. This paper introduced a gait pattern design by using equations (conditions of walking). Following the requirements of reducing force to the injured side to avoid further injury, we developed a lower limb gait pattern to shorten the stride length so as to reduce walking speed, to delay the stance phase of the uninjured side and to reduce step length of the uninjured side. This gait pattern was then verified by the practice of a rehabilitation training of an Achilles tendon rupture patient, whose two-year rehabilitation training (with 24 tests) has proven that this pattern worked as intended. This indicates that rehabilitation training program for lower limb injury can rest on biomechanical conditions of walking based on experimental evidence. △ Less

Submitted 2 September, 2015; v1 submitted 14 August, 2015; originally announced August 2015.

Comments: 6 pages, 4 figures

Doppler-free intermodulated fluorescence spectroscopy of $^4He$ $2^3P-3^{1,3}D$ transitions at 588 nm with a one-watt compact laser system

Authors: Pei-Ling Luo, Jinmeng Hu, Yan Feng, Li-Bang Wang, Jow-Tsong Shy

Abstract: We have demonstrated Doppler-free intermodulated fluorescence spectroscopy of helium $2^3P-3^{1,3}D$ transitions in an rf discharged sealed-off cell using a compact laser system at 588 nm. An external cavity diode laser at 1176 nm was constructed to seed a Raman fiber amplifier. Laser power of more than one watt at 588 nm was produced by frequency doubling of the fiber amplifier output using a MgO… ▽ More We have demonstrated Doppler-free intermodulated fluorescence spectroscopy of helium $2^3P-3^{1,3}D$ transitions in an rf discharged sealed-off cell using a compact laser system at 588 nm. An external cavity diode laser at 1176 nm was constructed to seed a Raman fiber amplifier. Laser power of more than one watt at 588 nm was produced by frequency doubling of the fiber amplifier output using a MgO:PPLN crystal. A doubling efficiency of 23 % was achieved. The power-dependent spectra of the $2^3P-3^3D$ transitions were investigated. Furthermore, the Doppler-free spectrum of the spin-forbidden $2^3P-3^1D$ transitions was observed for the first time. Our results are crucial towards precision test of QED atomic calculations, especially for improving the determination of the helium $3^1D-3^3D$ separation. △ Less

Submitted 20 April, 2015; originally announced April 2015.

Comments: 6 pages, 5 figures

The study of neutron spectra in water bath from Pb target irradiated by 250MeV/u protons

Authors: Yanyan Li, Xueying Zhang, Yongqin Ju, Fei Ma, Hongbin Zhang, Liang Chen, Honglin Ge, Peng Luo, Bin Zhou, Yanbin Zhang, Jianyang Li, Junkui Xu, Songlin Wang, Yongwei Yang, Lei Yang

Abstract: The spallation neutrons were produced by the irradiation of Pb with 250 MeV protons. The Pb target was surrounded by water which was used to slow down the emitted neutrons. The moderated neutrons in the water bath were measured by using the resonance detectors of Au, Mn and In with Cd cover. According to the measured activities of the foils, the neutron flux at different resonance energy were dedu… ▽ More The spallation neutrons were produced by the irradiation of Pb with 250 MeV protons. The Pb target was surrounded by water which was used to slow down the emitted neutrons. The moderated neutrons in the water bath were measured by using the resonance detectors of Au, Mn and In with Cd cover. According to the measured activities of the foils, the neutron flux at different resonance energy were deduced and the epithermal neutron spectra were proposed. Corresponding results calculated with the Monte Carlo code MCNPX were compared with the experimental data to check the validity of the code. △ Less

Submitted 4 September, 2014; originally announced September 2014.

Comments: 6 pages,9 figures

A new network node similarity measure method and its applications

Authors: Yongli Li, Peng Luo, Chong Wu

Abstract: Network node similarity measure has been paid particular attention in the field of statistical physics. In this paper, we utilize the concept of information and information loss to measure the node similarity. The whole model is based on this idea that if two nodes are more similar than the others, then the information loss of seeing them as the same is less. The present new method has low algorit… ▽ More Network node similarity measure has been paid particular attention in the field of statistical physics. In this paper, we utilize the concept of information and information loss to measure the node similarity. The whole model is based on this idea that if two nodes are more similar than the others, then the information loss of seeing them as the same is less. The present new method has low algorithm complexity so that it can save much time and energy to deal with the large scale real-world network. We illustrate the availability of this approach based on two artificial examples and computer-generated networks by comparing its accuracy with the other selected approaches. The above tests demonstrate that the new method can provide more reasonable results consistent with our human common judgment. The new similarity measure method is also applied to predict the network evolution and predict unknown nodes' attributions in the two application examples. △ Less

Submitted 17 March, 2014; originally announced March 2014.

Comments: 12 pages, 5 figures