arXiv subjects

Chao Chen

Publications and source records attributed to Chao Chen.

At least 469 records · Page 26Linked to original sources

arXiv2024

Design and Implementation of TAO DAQ System

Purpose: The Taishan Antineutrino Observatory (TAO) is a satellite experiment of the Jiangmen Underground Neutrino Observatory (JUNO), also known as JUNO-TAO. Located close to one of the reactors of the Taishan Nuclear Power Plant, TAO will measure the antineutrino energy spectrum precisely as a reference spectrum for JUNO. The data acquisition (DAQ) system is designed to acquire data from the TAO readout electronics and process it with software trigger and data compression algorithms. The data storage bandwidth is limited by the onsite network to be less than 100 Mb/s. Methods: The system is designed based on a distributed architecture, with fully decoupled modules to facilitate customized design and implementation. It is divided into two main components: the data flow system and the online software. The online software serves as the foundation, providing the electronics configuration, the process management, the run control, and the information sharing. The data flow system facilitates continuous data acquisition from various electronic boards or trigger systems, assembles and processes raw data, and ultimately stores it on the disk. Results: The core functionality of the system has been designed and developed. The usability of the data flow system interface and the software trigger results have been verified during the pre-installation testing phase. Conclusion: The DAQ system has been deployed for the TAO experiment. It has also successfully been applied to the integration test of the detector and electronics prototypes.

physics.ins-det↗

arXiv2024

An Efficient and Generalizable Symbolic Regression Method for Time Series Analysis

Time series analysis and prediction methods currently excel in quantitative analysis, offering accurate future predictions and diverse statistical indicators, but generally falling short in elucidating the underlying evolution patterns of time series. To gain a more comprehensive understanding and provide insightful explanations, we utilize symbolic regression techniques to derive explicit expressions for the non-linear dynamics in the evolution of time series variables. However, these techniques face challenges in computational efficiency and generalizability across diverse real-world time series data. To overcome these challenges, we propose \textbf{N}eural-\textbf{E}nhanced \textbf{Mo}nte-Carlo \textbf{T}ree \textbf{S}earch (NEMoTS) for time series. NEMoTS leverages the exploration-exploitation balance of Monte-Carlo Tree Search (MCTS), significantly reducing the search space in symbolic regression and improving expression quality. Furthermore, by integrating neural networks with MCTS, NEMoTS not only capitalizes on their superior fitting capabilities to concentrate on more pertinent operations post-search space reduction, but also replaces the complex and time-consuming simulation process, thereby substantially improving computational efficiency and generalizability in time series analysis. NEMoTS offers an efficient and comprehensive approach to time series analysis. Experiments with three real-world datasets demonstrate NEMoTS's significant superiority in performance, efficiency, reliability, and interpretability, making it well-suited for large-scale real-world time series data.

cs.LG↗

arXiv2024

Study of the decay $D^0\rightarrow ρ(770)^-e^+ν_e$

We present a study of the semileptonic decay $D^0\rightarrow π^-π^0e^{+}ν_{e}$ using an $e^+e^-$ annihilation data sample of $7.93~\mathrm{fb}^{-1}$ collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The branching fraction of $D^0\to ρ(770)^-e^+ν_e$ is measured to be $(1.439 \pm 0.033(\rm stat.) \pm 0.027(\rm syst.)) \times10^{-3}$, which is a factor 1.6 more precise than previous measurements. By performing an amplitude analysis, we measure the hadronic form-factor ratios of $D^0\to ρ(770)^-e^+ν_e$ at $q^2=0$ assuming the single-pole-dominance parametrization: $r_{V}=V(0)/A_1(0)=1.548\pm0.079(\rm stat.)\pm0.041(\rm syst.)$ and $r_{2}=A_2(0)/A_1(0)=0.823\pm0.056(\rm stat.)\pm0.026(\rm syst.)$.

hep-ex↗

arXiv2024

Learning Local Pattern Modularization for Point Cloud Reconstruction from Unseen Classes

It is challenging to reconstruct 3D point clouds in unseen classes from single 2D images. Instead of object-centered coordinate system, current methods generalized global priors learned in seen classes to reconstruct 3D shapes from unseen classes in viewer-centered coordinate system. However, the reconstruction accuracy and interpretability are still eager to get improved. To resolve this issue, we introduce to learn local pattern modularization for reconstructing 3D shapes in unseen classes, which achieves both good generalization ability and high reconstruction accuracy. Our insight is to learn a local prior which is class-agnostic and easy to generalize in object-centered coordinate system. Specifically, the local prior is learned via a process of learning and customizing local pattern modularization in seen classes. During this process, we first learn a set of patterns in local regions, which is the basis in the object-centered coordinate system to represent an arbitrary region on shapes across different classes. Then, we modularize each region on an initially reconstructed shape using the learned local patterns. Based on that, we customize the local pattern modularization using the input image by refining the reconstruction with more details. Our method enables to reconstruct high fidelity point clouds from unseen classes in object-centered coordinate system without requiring a large number of patterns or any additional information, such as segmentation supervision or camera poses. Our experimental results under widely used benchmarks show that our method achieves the state-of-the-art reconstruction accuracy for shapes from unseen classes. The code is available at https://github.com/chenchao15/Unseen.

cs.CV↗

arXiv2024

Spatial Diffusion for Cell Layout Generation

Generative models, such as GANs and diffusion models, have been used to augment training sets and boost performances in different tasks. We focus on generative models for cell detection instead, i.e., locating and classifying cells in given pathology images. One important information that has been largely overlooked is the spatial patterns of the cells. In this paper, we propose a spatial-pattern-guided generative model for cell layout generation. Specifically, a novel diffusion model guided by spatial features and generates realistic cell layouts has been proposed. We explore different density models as spatial features for the diffusion model. In downstream tasks, we show that the generated cell layouts can be used to guide the generation of high-quality pathology images. Augmenting with these images can significantly boost the performance of SOTA cell detection methods. The code is available at https://github.com/superlc1995/Diffusion-cell.

cs.CV↗

arXiv2024

Search for the radiative transition $χ_{c1}(3872)\toγψ_2(3823)$

Using 9.0 $\rm fb^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies from 4.178 to 4.278 GeV with the BESIII detector at the BEPCII collider, we perform the first search for the radiative transition $χ_{c1}(3872)\toγψ_2(3823)$. No $χ_{c1}(3872)\toγψ_2(3823)$ signal is observed. The upper limit on the ratio of branching fractions $\mathcal{B}(χ_{c1}(3872)\toγψ_2(3823), ψ_2(3823)\toγχ_{c1})/\mathcal{B}(χ_{c1}(3872)\toπ^+π^- J/ψ)$ is set as 0.075 at the 90\% confidence level. Our result contradicts theoretical predictions under the assumption that the $χ_{c1}(3872)$ is the pure charmonium state $χ_{c1}(2P)$.

hep-ex↗

arXiv2024

Study of $D^{+} \to K_{S}^{0}K^{*}(892)^{+}$ in $D^{+} \to K_{S}^{0} K_{S}^{0} π^{+}$

Using a data sample of $e^+e^-$ collisions corresponding to an integrated luminosity of 7.93 $\rm fb^{-1}$ collected with the BESIII detector at the center-of-mass energy 3.773~GeV, we perform the first amplitude analysis of the decay $D^{+} \to K_{S}^{0} K_{S}^{0} π^{+}$. The absolute branching fraction of $D^{+} \to K_{S}^{0}K_{S}^{0} π^{+}$ is measured to be $(2.97 \pm 0.09_{\rm stat.} \pm 0.05_{\rm syst.})\times10^{-3}$. The dominant intermediate process is $D^{+} \to K_{S}^{0}K^{*}(892)^{+}$, whose branching fraction is determined to be $(8.72 \pm 0.28_{\rm stat.} \pm 0.15_{\rm syst.}) \times 10^{-3}$, including all the $K^*(892)^+$ decays.

hep-ex↗

arXiv2024

Search for $C$-even states decaying to $D_{s}^{\pm}D_{s}^{*\mp}$ with masses between $4.08$ and $4.32~\mathrm{GeV}/c^{2}$

Six $C$-even states, denoted as $X$, with quantum numbers $J^{PC}=0^{-+}$, $1^{\pm+}$, or $2^{\pm+}$, are searched for via the $e^+e^-\toγD_{s}^{\pm}D_{s}^{*\mp}$ process using $(1667.39\pm8.84)~\mathrm{pb}^{-1}$ of $e^+e^-$ collision data collected with the BESIII detector operating at the BEPCII storage ring at center-of-mass energy of $\sqrt{s}=(4681.92\pm0.30)~\mathrm{MeV}$. No statistically significant signal is observed in the mass range from $4.08$ to $4.32~\mathrm{GeV}/c^{2}$. The upper limits of $σ[e^+e^- \to γX] \cdot \mathcal{B}[X \to D_{s}^{\pm} D_{s}^{*\mp}]$ at a $90\%$ confidence level are determined.

hep-ex↗

arXiv2024

AdaOcc: Adaptive-Resolution Occupancy Prediction

Autonomous driving in complex urban scenarios requires 3D perception to be both comprehensive and precise. Traditional 3D perception methods focus on object detection, resulting in sparse representations that lack environmental detail. Recent approaches estimate 3D occupancy around vehicles for a more comprehensive scene representation. However, dense 3D occupancy prediction increases computational demands, challenging the balance between efficiency and resolution. High-resolution occupancy grids offer accuracy but demand substantial computational resources, while low-resolution grids are efficient but lack detail. To address this dilemma, we introduce AdaOcc, a novel adaptive-resolution, multi-modal prediction approach. Our method integrates object-centric 3D reconstruction and holistic occupancy prediction within a single framework, performing highly detailed and precise 3D reconstruction only in regions of interest (ROIs). These high-detailed 3D surfaces are represented in point clouds, thus their precision is not constrained by the predefined grid resolution of the occupancy map. We conducted comprehensive experiments on the nuScenes dataset, demonstrating significant improvements over existing methods. In close-range scenarios, we surpass previous baselines by over 13% in IOU, and over 40% in Hausdorff distance. In summary, AdaOcc offers a more versatile and effective framework for delivering accurate 3D semantic occupancy prediction across diverse driving scenarios.

cs.CV↗

arXiv2024

Precise Measurement of Born Cross Sections for $e^+e^-\to D\bar{D}$ at $\sqrt{s} = 3.80-4.95$ GeV

Using data samples collected with the BESIII detector at the BEPCII collider at center-of-mass energies ranging from 3.80 to 4.95 GeV, corresponding to an integrated luminosity of 20 fb$^{-1}$, a measurement of Born cross sections for the $e^+e^-\to D^{0}\bar{D}^{0}$ and $D^{+}D^{-}$ processes is presented with unprecedented precision. Many clear peaks in the line shape of $e^+e^-\to D^{0}\bar{D}^{0}$ and $D^{+}D^{-}$ around the mass range of $G(3900)$, $ψ(4040)$, $ψ(4160)$, $Y(4260)$, and $ψ(4415)$, etc., are foreseen. These results offer crucial experimental insights into the nature of hadron production in the open-charm region.

hep-ex↗

arXiv2024

EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning

Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics directly in continuous time domain for its flexibility. This paper aims to design an easy-to-use pipeline (termed as EasyDGL which is also due to its implementation by DGL toolkit) composed of three key modules with both strong fitting ability and interpretability. Specifically the proposed pipeline which involves encoding, training and interpreting: i) a temporal point process (TPP) modulated attention architecture to endow the continuous-time resolution with the coupled spatiotemporal dynamics of the observed graph with edge-addition events; ii) a principled loss composed of task-agnostic TPP posterior maximization based on observed events on the graph, and a task-aware loss with a masking strategy over dynamic graph, where the covered tasks include dynamic link prediction, dynamic node classification and node traffic forecasting; iii) interpretation of the model outputs (e.g., representations and predictions) with scalable perturbation-based quantitative analysis in the graph Fourier domain, which could more comprehensively reflect the behavior of the learned model. Extensive experimental results on public benchmarks show the superior performance of our EasyDGL for time-conditioned predictive tasks, and in particular demonstrate that EasyDGL can effectively quantify the predictive power of frequency content that a model learn from the evolving graph data.

cs.LG↗

arXiv2024

Search for the rare decay $J/ψ\to γD^0+c.c.$ at BESIII

Using $(10087\pm44)\times10^6J/ψ$ events collected with the BESIII detector, we search for the rare decay $J/ψ\to γD^0+c.c.$ for the first time. No obvious signal is observed and the upper limit on the branching fraction is determined to be ${\cal B}(J/ψ\to γD^{0}+c.c.)< 9.1 \times 10^{-8}$ at 90\% confidence level.

hep-ex↗

arXiv2024

Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving

Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse} (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: Sponge for triggering widespread misidentifications and Blinding for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement ($\sim$10\%-60\% absolute and $\sim$2-7$\times$ relative) in attack efficacy over state-of-the-art attacks.

cs.CV↗

arXiv2024

Measurements of $K_S^0$-$K_L^0$ asymmetries in the decays $Λ_c^+ \to pK_{L,S}^0$, $pK_{L,S}^0π^+π^-$ and $pK_{L,S}^0π^0$

Using $e^+e^-$ annihilation data sets corresponding to an integrated luminosity of 4.5 $\text{fb}^{-1}$, collected with the BESIII detector at center-of-mass energies between 4.600 and 4.699 GeV, we report the first measurements of the absolute branching fractions $\mathcal{B}(Λ_c^+\to pK_{L}^{0})=(1.67 \pm 0.06 \pm 0. 04)\%$, $\mathcal{B}(Λ_c^+\to pK_{L}^{0}π^+π^-)=(1.69 \pm 0.10 \pm 0.05)\%$, and $\mathcal{B}(Λ_c^+\to pK_{L}^{0}π^0)=(2.02 \pm 0.13 \pm 0.05)\%$, where the first uncertainties are statistical and the second systematic. Combining with the known branching fractions of $Λ_c^+ \to pK_{S}^{0}$, $Λ_c^+ \to pK_{S}^{0}π^+π^-$, and $Λ_c^+ \to pK_{S}^{0}π^0$, we present the first measurements of the $K_{S}^{0}$-$K_{L}^{0}$ asymmetries $R(Λ_c^+, K_{S,L}^0X) = \frac{\mathcal{B}(Λ_c^+ \to K_{S}^{0} X) - \mathcal{B}(Λ_c^+ \to K_{L}^{0} X)}{\mathcal{B}(Λ_c^+ \to K_{S}^{0} X) + \mathcal{B}(Λ_c^+ \to K_{L}^{0} X)}$ in charmed baryon decays: $R(Λ_c^+, pK_{S,L}^0) = -0.025 \pm 0.031$, $R(Λ_c^+, pK_{S,L}^0π^+π^-) = -0.027 \pm 0.048$, and $R(Λ_c^+, pK_{S,L}^0π^0) =-0.015 \pm 0.046$. No significant asymmetries within the uncertainties are observed.

hep-ex↗

arXiv2024

Search for $η_c(2S)\toωω$ and $ωϕ$ decays and measurements of $χ_{cJ}\toωω$ and $ωϕ$ in $ψ(2S)$ radiative processes

Using $(2712\pm 14)$ $\times$ 10$^{6}$ $ψ(2S)$ events collected with the BESIII detector at the BEPCII collider, we search for the decays $η_{c}(2S)\toωω$ and $η_{c}(2S)\toωϕ$ via the process $ψ(2S)\toγη_{c}(2S)$. Evidence of $η_{c}(2S)\toωω$ is found with a statistical significance of $3.2σ$. The branching fraction is measured to be $\mathcal{B}(η_{c}(2S)\toωω)=(5.65\pm3.77(\rm stat.)\pm5.32(\rm syst.))\times10^{-4}$. No statistically significant signal is observed for the decay $η_{c}(2S)\toωϕ$. The upper limit of the branching fraction at the 90\% confidence level is determined to be $\mathcal{B}(ψ(2S)\toγη_{c}(2S),η_{c}(2S)\toωϕ)<2.24\times 10^{-7}$. We also update the branching fractions of $χ_{cJ}\to ωω$ and $χ_{cJ}\toωϕ$ decays via the $ψ(2S)\toγχ_{cJ}$ transition. The branching fractions are determined to be $\mathcal{B}(χ_{c0}\toωω)=(10.63\pm0.11\pm0.46)\times 10^{-4}$, $\mathcal{B}(χ_{c1}\toωω)=(6.39\pm0.07\pm0.29)\times 10^{-4}$, $\mathcal{B}(χ_{c2}\toωω)=(8.50\pm0.08\pm0.38)\times 10^{-4}$, $\mathcal{B}(χ_{c0}\toωϕ)=(1.18\pm0.03\pm0.05)\times 10^{-4}$, $\mathcal{B}(χ_{c1}\toωϕ)=(2.03\pm0.15\pm0.12)\times 10^{-5}$, and $\mathcal{B}(χ_{c2}\toωϕ)=(9.37\pm1.07\pm0.59)\times 10^{-6}$, where the first uncertainties are statistical and the second are systematic.

hep-ex↗

arXiv2024

Search for $e^{+}e^{-}\toη'ψ(2S)$ at center-of-mass energies from 4.66 to 4.95 GeV

Using data samples with an integrated luminosity of $4.67~\mathrm{fb}^{-1}$ collected by the BESIII detector operating at the BEPCII collider, we search for the process $e^+e^- \rightarrow η' ψ(2S)$ at center-of-mass energies from $4.66$ to $4.95~\mathrm{GeV}$. No significant signal is observed, and upper limits for the Born cross sections $σ^B(e^+e^-\rightarrowη'ψ(2S))$ at the 90\% confidence level are determined.

hep-ex↗

arXiv2024

Measurement of the Branching Fraction of \boldmath{$ψ(2S) \to γπ^0$}

Based on $(2712.4\pm14.1)\times10^{6}~ψ(2S)$ events, 7.9 fb$^{-1}$ $ψ(3773)$ data, and 0.8 fb$^{-1}$ off-resonance data samples collected with the BESIII detector, we measure the branching fraction of $ψ(2S)\rightarrowγπ^{0}$ and $e^{+}e^{-}\rightarrowγπ^{0}$ form factor at momentum transfers $Q^{2}\sim13$ GeV$^{2}$. The $e^{+}e^{-}\rightarrowγπ^{0}$ cross section is fitted with considering the interference between the $ψ(2S)$ and continuum amplitudes and two solutions are found, ${\cal B}=3.74\times10^{-7}$ with $ϕ=3.93$ rad and ${\cal B}=7.87\times10^{-7}$ with $ϕ=2.08$ rad. Here, ${\cal B}$ is the branching fraction of $ψ(2S)\rightarrowγπ^{0}$ and $ϕ$ is the relative phase angle between the $ψ(2S)$ and continuum amplitudes. Due to insufficient off-resonance data, the branching fraction ${\cal B}(ψ(2S)\rightarrowγπ^{0})$ is determined to be in the range $[2.7, 9.7]\times10^{-7}$ within one standard deviation of the contour region.

hep-ex↗

arXiv2024

Analysis of the dynamics of the decay $D^{+}\to K_{S}^{0} π^{0} e^{+}ν_{e}$

The branching fraction of $D^+\to K_{S}^{0} π^{0}e^+ν_e$ is measured for the first time using $7.93~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected at the center-of-mass energy $\sqrt{s}=3.773$~GeV with the BESIII detector operating at the BEPCII collider, and is determined to be ${\mathcal B}$($D^+\to K_S^0π^0e^+ν_e$) = $(0.881~\pm~0.017_{\rm stat.}~\pm~0.016_{\rm syst.})$\%. Based on an analysis of the $D^+\to K_S^0π^0e^+ν_e$ decay dynamics, we observe the $S\text{-}{\rm wave}$ and $P$-wave components with fractions of $f_{S\text{-}{\rm wave}}$ = $(6.13~\pm~0.27_{\rm stat.}~\pm ~0.30_{\rm syst.})\%$ and $f_{\bar K^{*}(892)^0}$ = $(93.88~\pm~0.27_{\rm stat.}~\pm~0.29_{\rm syst.})$\%, respectively. From these results, we obtain the branching fractions ${\mathcal B}$($D^+\to (K_S^0π^0)_{S\text{-}{\rm wave}}~e^+ν_e$) = $(5.41~\pm~0.35_{\rm stat.}~\pm~0.37_{\rm syst.})\times10^{-4}$ and ${\mathcal B}$($D^+\to \bar K^{*}(892)^0e^+ν_e$) = $(4.97~\pm~0.11_{\rm stat.}~\pm~0.12_{\rm syst.})$\%. In addition, the hadronic form-factor ratios of $D^{+} \to \bar {K}^{*}(892)^0e^+ν_e$ at $q^2=0$, assuming a single-pole dominance parameterization, are determined to be $r_V=\frac{V(0)}{A_1(0)}= 1.43~\pm~0.07_{\rm stat.}~\pm~0.03_{\rm syst.}$ and $r_2=\frac{A_2(0)}{A_1(0)}=0.72~\pm~0.06_{\rm stat.}~\pm~0.02_{\rm syst.}$.

hep-ex↗