Subjects: Physics >> Nuclear Physics submitted time 2023-12-28
Yuemei Peng
Jianshe Cao
Jinhui Chen
Haiyi Dong
Ping He
Yi Jiao
Ling Kang
WenKang
Jian Li
Jingyi Li
Guoping Lin
Fang Liu
Fengli Long
Cai Meng
Xin Qi
Huamin Qu
Yanfeng Sui
Sheng Wang
Gang Xu
Qiang Ye
Abstract： The high-energy photon source (HEPS) is the first fourth-generation synchrotron light source facility in China. The HEPS injector consists of a linear accelerator (Linac) and a full energy booster. The booster captures the electron beam from the Linac and increases its energy to the value required for the storage ring. The full-energy beam could be injected to the storage ring directly or after “high-energy accumulation.” On November 17, 2023, the key booster parameters successfully reached their corresponding target values. These milestone results were achieved based on numerous contributions, including nearly a decade of physical design, years of equipment development and installation, and months of beam commissioning. As measured at the extraction energy of 6 GeV, the averaged beam current and emittance reached 8.57 mA with 5 bunches and 30.37 nm.rad with a single-bunch charge of 5.58 nC, compared with the corresponding target values of 6.6 mA and 35 nm.rad, respectively. This paper presents the physical design, equipment development, installation, and commissioning process of the HEPS booster.
Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2023-03-22
Abstract： To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes. However, recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced datasets, suggesting the existence of other factors that affect model bias. In this work, we systematically propose a series of geometric measurements for perceptual manifolds in deep neural networks, and then explore the effect of the geometric characteristics of perceptual manifolds on classification difficulty and how learning shapes the geometric characteristics of perceptual manifolds. An unanticipated finding is that the correlation between the class accuracy and the separation degree of perceptual manifolds gradually decreases during training, while the negative correlation with the curvature gradually increases, implying that curvature imbalance leads to model bias. Therefore, we propose curvature regularization to facilitate the model to learn curvature-balanced and flatter perceptual manifolds. Evaluations on multiple long-tailed and non-longtailed datasets show the excellent performance and exciting generality of our approach, especially in achieving significant performance improvements based on current state-ofthe-art techniques. Our work opens up a geometric analysis perspective on model bias and reminds researchers to pay attention to model bias on non-long-tailed and even samplebalanced datasets. The code and model will be made public.
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
JUNO Collaboration
Jie Zhao
Baobiao Yue
Haoqi Lu
Yufeng Li
Jiajie Ling
Zeyuan Yu
Angel Abusleme
Thomas Adam
Shakeel Ahmad
Rizwan Ahmed
Sebastiano Aiello
Muhammad Akram
Abid Aleem
Tsagkarakis Alexandros
Fengpeng An
Qi An
Giuseppe Andronico
Nikolay Anfimov
Vito Antonelli
Abstract： The physics potential of detecting $^8$B solar neutrinos is exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model independent manner by using three distinct channels of the charged-current (CC), neutral-current (NC) and elastic scattering (ES) interactions. Due to the largest-ever mass of $^{13}$C nuclei in the liquid-scintillator detectors and the potential low background level, $^8$B solar neutrinos would be observable in the CC and NC interactions on $^{13}$C for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC and ES channels to guarantee the $^8$B solar neutrino observation. From the sensitivity studies performed in this work, we show that one can reach the precision levels of 5%, 8% and 20% for the $^8$B neutrino flux, $\sin^2\theta_{12}$, and $\Delta m^2_{21}$, respectively, using ten years of JUNO data. It would be unique and helpful to probe the details of both solar physics and neutrino physics. In addition, when combined with SNO, the world-best precision of 3% is expected for the $^8$B neutrino flux measurement.
Peer Review Status:
Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Abstract： Exceptional points (EPs) represent a distinct type of spectral singularity in non-Hermitian systems, and intriguing physics concepts have been studied with optical EPs recently. As a system beyond photonics, the mechanical oscillators coupling with many physical systems are expected to be further exploited EPs for mechanical sensing, topology energy transfer, nonreciprocal dynamics etc. In this study, we demonstrated on-chip mechanical EPs with a silicon optomechanical zipper cavity, wherein two near-degenerate mechanical breathing modes are coupled via a single co-localized optical mode. By tailoring the dissipative and coherent couplings between two mechanical oscillators, the spectral splitting with 1/2 order response, a distinctive feature of EP, was observed successfully. Our work provides an integrated platform for investigating the physics related to mechanical EPs on silicon chips and suggests their possible applications for ultrasensitive measurements.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Abstract： A beam splitter (BS) is one of the most critical building blocks in optical systems. Despite various attempts to miniaturize the conventional cube BS reported, it remains a challenge to realize an ultrathin BS with multi-port output, nonuniform splitting ratio and steerable outgoing directions. Herein, we have demonstrated a free-space optical multi-port beam splitter (MPBS) based on a polarization-independent all-dielectric metasurface. By utilizing an optimized phase-pattern paradigm via a gradient-descent-based iterative algorithm on amorphous silicon (a-Si) metasurfaces, we have prepared various MPBS samples with arbitrarily predetermined output port number (2~7), power ratio and spatial distribution of output beams. The experimental results reveal that the MPBSs could achieve high total splitting efficiency (TSE, above 74%) and beam-splitting ratio fidelity (SRF, above 0.992) within the bandwidth of 100nm (1500nm~1600nm). We envision that such MPBS could provide a fabulous flexibility for optical integrated systems design and diverse applications.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Abstract： In quantum optics, orbital angular momentum (OAM) is very promising to achieve high-dimensional quantum states due to the nature of infinite and discrete eigenvalue, which is quantized by the topological charge of l. Here, a heralded single-photon source with switchable OAM modes is proposed and demonstrated on silicon chip. At room-temperature, the heralded single photons with 11 OAM modes (l=2~6, -6~-1) have been successfully generated and switched through thermo-optical effect. We believe that such an integrated quantum source with multiple OAM modes and operating at room-temperature would provide a practical platform for high-dimensional quantum information processing. Moreover, our proposed architecture can also be extended to other material systems to further improve the performance of OAM quantum source.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Jingyuan Zheng
You Xiao
Mingzhong Hu
Yuchen Zhao
Hao Li
Lixing You
Xue Feng
Fang Liu
Kaiyu Cui
Yidong Huang
Wei Zhang
Abstract： Faint light spectroscopy has many important applications such as fluorescence spectroscopy, lidar and astronomical observations. However, long measurement time limit its application on real-time measurement. In this work, a photon counting reconstructive spectrometer combining metasurfaces and superconducting nanowire single photon detectors (SNSPDs) was proposed. A prototype device was fabricated on a silicon on isolator (SOI) substrate, and its performance was characterized. Experiment results show that this device support spectral reconstruction of mono-color lights with a resolution of 2 nm in the wavelength region of 1500 nm ~ 1600 nm. The detection efficiency of this device is 1.4% ~ 3.2% in this wavelength region. The measurement time required by this photon counting reconstructive spectrometer was also investigated experimentally, showing its potential to be applied in the scenarios requiring real-time measurement.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Abstract： On-chip spectral imaging based on engineered spectral modulation and computational spectral reconstruction provides a promising scheme for portable spectral cameras. However, the angle dependence of modulation units results in the angle sensitivity of spectral imaging, which limits its practical applications. Here, we proposed a design for angle-robust spectral recovery based on a group of topology-optimized plasmonic metasurface units under a 30{\deg} field-of-view, and demonstrate angle-insensitive on-chip spectral imaging in the wavelength range of 450 to 750 nm for average polarization. Furthermore, we experimentally verified the angle-insensitive spectral filtering effects of the fabricated metasurface units, and demonstrated angle-robust spectral reconstruction with a fidelity of 98%. Our approach expands the application scale of spectral imaging, and provides a guidance for designing other angle-robust devices.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Jiayi Ouyang
Yuxuan Liao
Zhiyao Ma
Deyang Kong
Xue Feng
Xiang Zhang
Xiaowen Dong
Kaiyu Cui
Fang Liu
Wei Zhang
Yidong Huang
Abstract： Photonic Ising machine is a new paradigm of optical computing, which is based on the characteristics of light wave propagation, parallel processing and low loss transmission. Thus, the process of solving the combinatorial optimization problems can be accelerated through photonic/optoelectronic devices. In this work, we have proposed and demonstrated the so-called Phase-Encoding and Intensity Detection Ising Annealer (PEIDIA) to solve arbitrary Ising problems on demand. The PEIDIA is based on the simulated annealing algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation. With PEIDIA, the Ising spins are encoded on the phase term of the optical field and only intensity detection is required during the solving process. As a proof of principle, several 20-dimensional Ising problems have been solved with high ground state probability (0.98 within 1000 iterations for the antiferromagnetic cubic model, >0.99 within 4000 iterations for two random spin-glass models, respectively). It should be mentioned that our proposal is also potential to be implemented with integrated photonic devices such as tunable metasurfaces to achieve large-scale and on-demand photonic Ising machines.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Abstract： Spectral imaging extends the concept of traditional color cameras to capture images across multiple spectral channels and has broad application prospects. Conventional spectral cameras based on scanning methods suffer from low acquisition speed and large volume. On-chip computational spectral imaging based on metasurface filters provides a promising scheme for portable applications, but endures long computation time for point-by-point iterative spectral reconstruction and mosaic effect in the reconstructed spectral images. In this study, we demonstrated on-chip rapid spectral imaging eliminating the mosaic effect in the spectral image by deep-learning-based spectral data cube reconstruction. We experimentally achieved four orders of magnitude speed improvement than iterative spectral reconstruction and high fidelity of spectral reconstruction over 99% for a standard color board. In particular, we demonstrated video-rate spectral imaging for moving objects and outdoor driving scenes with good performance for recognizing metamerism, where the concolorous sky and white cars can be distinguished via their spectra, showing great potential for autonomous driving and other practical applications in the field of intelligent perception.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Abstract： We have proposed and demonstrated a scalable and efficient scheme for programmable unitary operations in orbital angular momentum (OAM) domain. Based on matrix decomposition into diagonal and Fourier factors, arbitrary matrix operators can be implemented only by diagonal matrices alternately acting on orbital angular momentum domain and azimuthal angle domain, which are linked by Fourier transform. With numerical simulations, unitary matrices with dimensionality of 3*3 are designed and discussed for OAM domain. Meanwhile, the parallelism of our proposed scheme is also presented with two 3*3 matrices. Furthermore, as an alternative to verify our proposal, proof of principle experiments have been performed on path domain with the same matrix decomposition method, in which an average fidelity of 0.97 is evaluated through 80 experimental results with dimensionality of 3*3.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Jian Xiong
Xusheng Cai
Kaiyu Cui
Yidong Huang
Jiawei Yang
Hongbo Zhu
Zekun Zheng
Sheng Xu
Yuhan He
Fang Liu
Xue Feng
Wei Zhang
Abstract： One-shot spectral imaging that can obtain spectral information from different points in space at one time has always been difficult to achieve, and is extremely important for both fundamental scientific research and various practical applications. In this study, one-shot ultraspectral imaging by fitting thousands of micro-spectrometers on a chip, is proposed and demonstrated. Exotic light modulation is achieved by using a reconfigurable metasurface supercell, which enables 155,216 image-adaptive micro-spectrometers, simultaneously guaranteeing the spectral-pixel density and reconstructed spectral quality. By constructing a compressive-sensing algorithm, the device can reconstruct ultraspectral imaging ($\Delta\lambda$/$\lambda$~0.001) covering a 300-nm-wide visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme can be extended to almost any commercial camera with different spectral bands to seamlessly switch between image and spectral image, and opens up a new space for the application of spectral analysis combining with image recognition and intellisense.
Peer Review Status:Awaiting Review
Subjects: Optics >> Quantum optics submitted time 2023-02-19
Abstract： An optical neural network is proposed and demonstrated with programmable matrix transformation and nonlinear activation function of photodetection (square-law detection). Based on discrete phase-coherent spatial modes, the dimensionality of programmable optical matrix operations is 30~37, which is implemented by spatial light modulators. With this architecture, all-optical classification tasks of handwritten digits, objects and depth images are performed on the same platform with high accuracy. Due to the parallel nature of matrix multiplication, the processing speed of our proposed architecture is potentially as high as7.4T~74T FLOPs per second (with 10~100GHz detector)
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2023-02-16
Abstract：
Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is used to measure the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed natural and medical datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias. In addition, we look ahead to future challenges.
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