arXiv subjects

Tao Zhong

Publications and source records attributed to Tao Zhong.

At least 55 records · Page 3Linked to original sources

arXiv2025

$η_c$ leading-twist distribution amplitude and the $B_c \to η_c\ell\barν_\ell$ semileptonic decays using QCD Sum Rules

In this paper, we investigate the semileptonic decays $B_c \to η_c\ell\barν_\ell$ using the quantum chromodynamics(QCD) sum rules within the framework of Standard Model (SM). We further explore the potential to probe signatures of new Physics (NP) beyond the SM through these decays. First, we derive the $ξ$-moments $\langleξ_{2;η_c}^{n}\rangle$ of the $η_c$-meson leading-twist distribution amplitude $ϕ_{2;η_c}$ using the QCD sum rules within the background field theory. Considering contributions from the vacuum condensates up to dimension-six, the first two nonzero $ξ$-moments at the scale of $4$ GeV are found to be $\langleξ_{2;η_c}^{2}\rangle = 0.103^{+0.009}_{-0.009}$ and $\langleξ_{2;η_c}^{4}\rangle = 0.031^{+0.003}_{-0.003}$. Using these moments, we then fix the Gegenbauer expansion series of $ϕ_{2;η_c}$ and apply it to compute the $B_c \to η_c$ transition form factors (TFFs) using QCD light cone sum rules. Second, we extrapolate those TFFs to physically allowable $q^2$-range via a simplified series expansion, and we obtain $R_{η_c}|_{\rm SM} = 0.308^{+0.084}_{-0.062}$. Furthermore, we explore the potential impacts of various NP scenarios on $R_{η_c}$. Specifically, we compute the forward-backward asymmetry $\mathcal{A}_{\rm FB}({q^2})$, the convexity parameter $\mathcal{C}_F^τ({q^2})$, and the longitudinal and transverse polarizations $\mathcal{P}_L ({q^2})$ and $\mathcal{P}_T ({q^2})$ for $B_c \to η_c$ transitions within both the SM and two types of NP scenarios. Our results contribute to a deeper understanding of $B_c$-meson semileptonic decays and provide insights into the search for the NP beyond the SM.

hep-ph↗

arXiv2025

Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognition

Accurate recognition of dysarthric and elderly speech remains challenging to date. While privacy concerns have driven a shift from centralized approaches to federated learning (FL) to ensure data confidentiality, this further exacerbates the challenges of data scarcity, imbalanced data distribution and speaker heterogeneity. To this end, this paper conducts a systematic investigation of regularized FL techniques for privacy-preserving dysarthric and elderly speech recognition, addressing different levels of the FL process by 1) parameter-based, 2) embedding-based and 3) novel loss-based regularization. Experiments on the benchmark UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest that regularized FL systems consistently outperform the baseline FedAvg system by statistically significant WER reductions of up to 0.55\% absolute (2.13\% relative). Further increasing communication frequency to one exchange per batch approaches centralized training performance.

eess.AS↗

arXiv2025

BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving

LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in operations research domain lack detailed annotations of the modeling process, such as variable definitions, focusing solely on objective values, which hinders reinforcement learning applications. To address this, we release the StructuredOR dataset, annotated with comprehensive labels that capture the complete mathematical modeling process. We further propose BPP-Search, an algorithm that integrates reinforcement learning into a tree-of-thought structure using Beam search, a Process reward model, and a pairwise Preference algorithm. This approach enables efficient exploration of tree structures, avoiding exhaustive search while improving accuracy. Extensive experiments on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets show that BPP-Search significantly outperforms state-of-the-art methods. In tree-based reasoning, BPP-Search excels in accuracy and efficiency, enabling faster retrieval of correct solutions. The StructuredOR dataset is available on Huggingface https://huggingface.co/datasets/LLM4OR/StructuredOR and GitHub https://github.com/LLM4OR/StructuredOR.

cs.AI↗

arXiv2025

MoLAE: Mixture of Latent Experts for Parameter-Efficient Language Models

Mixture of Experts (MoE) has become a key architectural paradigm for efficiently scaling Large Language Models (LLMs) by selectively activating a subset of parameters for each input token. However, standard MoE architectures face significant challenges, including high memory consumption and communication overhead during distributed training. In this paper, we introduce Mixture of Latent Experts (MoLAE), a novel parameterization that addresses these limitations by reformulating expert operations through a shared projection into a lower-dimensional latent space, followed by expert-specific transformations. This factorized approach substantially reduces parameter count and computational requirements, particularly in existing LLMs where hidden dimensions significantly exceed MoE intermediate dimensions. We provide a rigorous mathematical framework for transforming pre-trained MoE models into MoLAE architecture, characterizing conditions for optimal factorization, and developing a systematic two-step algorithm for this conversion. Our comprehensive theoretical analysis demonstrates that MoLAE significantly improves efficiency across multiple dimensions while preserving model capabilities. Experimental results confirm that MoLAE achieves comparable performance to standard MoE with substantially reduced resource requirements.

cs.LG↗

arXiv2025

LoRE-Merging: Exploring Low-Rank Estimation For Large Language Model Merging

While most current approaches rely on further training techniques, such as fine-tuning or reinforcement learning, to enhance model capacities, model merging stands out for its ability of improving models without requiring any additional training. In this paper, we propose a unified framework for model merging based on low-rank estimation of task vectors without the need for access to the base model, named \textsc{LoRE-Merging}. Our approach is motivated by the observation that task vectors from fine-tuned models frequently exhibit a limited number of dominant singular values, making low-rank estimations less prone to interference. We implement the method by formulating the merging problem as an optimization problem. Extensive empirical experiments demonstrate the effectiveness of our framework in mitigating interference and preserving task-specific information, thereby advancing the state-of-the-art performance in model merging techniques.

cs.CL↗

arXiv2025

Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging

The transition from System 1 to System 2 reasoning in large language models (LLMs) has marked significant advancements in handling complex tasks through deliberate, iterative thinking. However, this progress often comes at the cost of efficiency, as models tend to overthink, generating redundant reasoning steps without proportional improvements in output quality. Long-to-Short (L2S) reasoning has emerged as a promising solution to this challenge, aiming to balance reasoning depth with practical efficiency. While existing approaches, such as supervised fine-tuning (SFT), reinforcement learning (RL), and prompt engineering, have shown potential, they are either computationally expensive or unstable. Model merging, on the other hand, offers a cost-effective and robust alternative by integrating the quick-thinking capabilities of System 1 models with the methodical reasoning of System 2 models. In this work, we present a comprehensive empirical study on model merging for L2S reasoning, exploring diverse methodologies, including task-vector-based, SVD-based, and activation-informed merging. Our experiments reveal that model merging can reduce average response length by up to 55% while preserving or even improving baseline performance. We also identify a strong correlation between model scale and merging efficacy with extensive evaluations on 1.5B/7B/14B/32B models. Furthermore, we investigate the merged model's ability to self-critique and self-correct, as well as its adaptive response length based on task complexity. Our findings highlight model merging as a highly efficient and effective paradigm for L2S reasoning, offering a practical solution to the overthinking problem while maintaining the robustness of System 2 reasoning. This work can be found on Github https://github.com/hahahawu/Long-to-Short-via-Model-Merging.

cs.CL↗

arXiv2025

Footprint in fitting $B \to D$ vector form factor and determination for $D$-meson leading-twist LCDA

In this paper, we fit the $B\to D$ vector transition form factor (TFF) by using the data measured by \textsl{BABAR} and Belle Collaborations within Monte Carlo (MC) method. Meanwhile, the $B\to D$ TFF is also calculated by using the QCD light-cone sum rules approach (LCSRs) within right-handed chiral current correlation function. In the TFF, $D$-meson leading-twist light-cone distribution amplitude (LCDA) is the most important non-perturbative input parameter, the precise behavior of which is mainly determined by the parameter $B_{2;D}$ in the light-cone harmonic oscillator model. Through fitting the TFF, we determine the value $B_{2;D}=0.445$. Then, we present the curve of $D$-meson leading-twist LCDA in comparison with other theoretical approaches. Subsequently, the $B\to D$ TFF $f_{+}^{BD}(q^2)$ at the large recoil region is $f_{+}^{BD}(0)=0.648_{-0.063}^{+0.067}$, which is compared in detail with theoretical estimates and experimental measurements. Furthermore, we calculated the decay width and branching fractions of the Cabibbo-favored semileptonic decays $B\to D\ell^+ν_{\ell}$, which lead to the results $\mathcal{B}(B^0\to D^-\ell ^+ν_{\ell}) =(2.10_{-0.38}^{+0.44})\times 10^{-2}$ and $\mathcal{B}(B^+\to \bar{D}^0\ell ^+ν_{\ell}) =(2.26_{-0.41}^{+0.48})\times 10^{-2}$. Finally, we predict the CKM matrix element with two scenarios $|V_{cb}|_{\rm SR}=41.55_{-4.50}^{+4.91}\times 10^{-3}$ and $|V_{cb} |_{\rm MC}=41.47_{-2.66}^{+2.55 }\times 10^{-3}$ from $B^0\to D^-\ell^+ν_{\ell}$, $|V_{cb}|_{\rm SR}=40.54_{-3.80}^{+4.33}\times 10^{-3}$ and $|V_{cb} |_{\rm MC}=40.46_{-1.38}^{+1.40}\times 10^{-3}$ from $B^+\to \bar{D}^0\ell^+ν_{\ell}$ which are in good agreement with theoretical and experimental predictions.

hep-ph↗

arXiv2025

Longitudinal leading-twist distribution amplitude of the $^1P_1$-state $b_1(1235)$ meson and its implications on $B\to b_1(1235)\ell^+ν_\ell$ decays

In the paper, we derive the $ξ$-moments $\langleξ_{2;b_1}^{n;\|}\rangle$ of the longitudinal leading-twist distribution amplitude $ϕ_{2;b_1}^{\|}$ for $^1P_1$-state $b_1(1235)$-meson by using the QCD sum rules under the background field theory. Considering the contributions from the vacuum condensates up to dimension-six, its first two non-zero $ξ$-moments at the scale 1 GeV are $\langleξ_{2;b_1}^{1;\|}\rangle= -0.647^{+0.118}_{-0.113}$ and $\langleξ_{2;b_1}^{3;\|}\rangle = -0.328^{+0.055}_{-0.052}$, respectively. Using those moments, we then fix the Gegenbauer expansion series of $ϕ_{2;b_1}^{\|}$ and apply it to calculate $B\to b_1(1235)$ transition form factors (TFFs) that are derived by using the QCD light-cone sum rules. Those TFFs are then extrapolated to the physically allowable $q^2$-range via the simplified series expansion. As for the branching fractions, we obtain ${\cal B}(\bar B^0 \to b_1^+(1235)e^- \barν_e) = 2.179^{+0.553}_{-0.422}\times 10^{-4}$, ${\cal B}(B^0 \to b_1^-(1235)μ^+ν_μ) = 2.166^{+0.544}_{-0.415}\times 10^{-4}$, ${\cal B}(B^+ \to b_1^0(1235)e^+ν_e) = 2.353^{+0.597}_{-0.456}\times 10^{-4}$, and ${\cal B}(B^+ \to b_1^0(1235)μ^+ν_μ) = 2.339^{+0.587}_{-0.448}\times 10^{-4}$, respectively.

hep-ph↗

arXiv2025

Automatic Operator-level Parallelism Planning for Distributed Deep Learning -- A Mixed-Integer Programming Approach

As the artificial intelligence community advances into the era of large models with billions of parameters, distributed training and inference have become essential. While various parallelism strategies-data, model, sequence, and pipeline-have been successfully implemented for popular neural networks on main-stream hardware, optimizing the distributed deployment schedule requires extensive expertise and manual effort. Further more, while existing frameworks with most simple chain-like structures, they struggle with complex non-linear architectures. Mixture-of-experts and multi-modal models feature intricate MIMO and branch-rich topologies that require fine-grained operator-level parallelization beyond the capabilities of existing frameworks. We propose formulating parallelism planning as a scheduling optimization problem using mixed-integer programming. We propose a bi-level solution framework balancing optimality with computational efficiency, automatically generating effective distributed plans that capture both the heterogeneous structure of modern neural networks and the underlying hardware constraints. In experiments comparing against expert-designed strategies like DeepSeek's DualPipe, our framework achieves comparable or superior performance, reducing computational bubbles by half under the same memory constraints. The framework's versatility extends beyond throughput optimization to incorporate hardware utilization maximization, memory capacity constraints, and other considerations or potential strategies. Such capabilities position our solution as both a valuable research tool for exploring optimal parallelization strategies and a practical industrial solution for large-scale AI deployment.

cs.LG↗

arXiv2025

Probing $D_s^*$-meson longitudinal twist-2 LCDA

In this paper, we carry on an investigation of the semileptonic decays $B_s\to D_s^*\ell \barν_{\ell}$. Firstly, we derive the moments of the $D_s^*$-meson longitudinal leading-twist light-cone distribution amplitude (LCDA) based on QCD sum rules within background field theory framework. Considering the contributions of the vacuum condensates up to dimension-six, its first ten non-zero $ξ$-moments are given. Meanwhile, we construct the $D_s^*$-meson longitudinal leading-twist LCDA by using the light-cone harmonic oscillator model. Then, using those moments, we fix the model parameters $α_{2;D_s^*}$ and $B_1^{2;D_s^*}$ by the least square method and apply them to calculate $B_s \to D_s^*$ transition form factors $A_1(q^2), A_2(q^2)$ and $V(q^2)$ that are derived by using the QCD light-cone sum rules. At the large recoil region, we obtain $A_1(0) =0.632_{-0.135}^{+0.228}, A_2(0) =0.706_{-0.092}^{+0.109}$ and $V(0) =0.647_{-0.069}^{+0.076}$. Those form factors are then extrapolated to the allowed whole physical $q^2$-region through the simplified series expansion. Finally, we obtain the branching fractions for the two decay channels $B_s\to D_s^*\ell\barν_\ell$, $\it i.e.$ ${\cal B}(B_s^0 \to D_s^{*+}e^-\barν_e)=(5.45_{-1.57}^{+2.15})\times 10^{-2}$, ${\cal B}(B_s^0 \to D_s^{*+}μ^-\barν_μ)=(5.43_{-1.57}^{+2.14})\times 10^{-2}$.

hep-ph↗

arXiv2025

GAGrasp: Geometric Algebra Diffusion for Dexterous Grasping

We propose GAGrasp, a novel framework for dexterous grasp generation that leverages geometric algebra representations to enforce equivariance to SE(3) transformations. By encoding the SE(3) symmetry constraint directly into the architecture, our method improves data and parameter efficiency while enabling robust grasp generation across diverse object poses. Additionally, we incorporate a differentiable physics-informed refinement layer, which ensures that generated grasps are physically plausible and stable. Extensive experiments demonstrate the model's superior performance in generalization, stability, and adaptability compared to existing methods. Additional details at https://gagrasp.github.io/

cs.RO↗

arXiv2025

Streaming Video Question-Answering with In-context Video KV-Cache Retrieval

We propose ReKV, a novel training-free approach that enables efficient streaming video question-answering (StreamingVQA), by seamlessly integrating with existing Video Large Language Models (Video-LLMs). Traditional VideoQA systems struggle with long videos, as they must process entire videos before responding to queries, and repeat this process for each new question. In contrast, our approach analyzes long videos in a streaming manner, allowing for prompt responses as soon as user queries are received. Building on a common Video-LLM, we first incorporate a sliding-window attention mechanism, ensuring that input frames attend to a limited number of preceding frames, thereby reducing computational overhead. To prevent information loss, we store processed video key-value caches (KV-Caches) in RAM and disk, reloading them into GPU memory as needed. Additionally, we introduce a retrieval method that leverages an external retriever or the parameters within Video-LLMs to retrieve only query-relevant KV-Caches, ensuring both efficiency and accuracy in question answering. ReKV enables the separation of video encoding and question-answering across different processes and GPUs, significantly enhancing the efficiency of StreamingVQA. Through comprehensive experimentation, we validate the efficacy and practicality of our approach, which significantly boosts efficiency and enhances applicability over existing VideoQA models.

cs.CV↗

arXiv2025

Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework

Efficient multimodal large language models (EMLLMs), in contrast to multimodal large language models (MLLMs), reduce model size and computational costs and are often deployed on resource-constrained devices. However, due to data privacy concerns, existing open-source EMLLMs rarely have access to private domain-specific data during the pre-training process, making them difficult to directly apply in device-specific domains, such as certain business scenarios. To address this weakness, this paper focuses on the efficient adaptation of EMLLMs to private domains, specifically in two areas: 1) how to reduce data requirements, and 2) how to avoid parameter fine-tuning. Specifically, we propose a tun\textbf{\underline{I}}ng-free, a\textbf{\underline{D}}aptiv\textbf{\underline{E}}, univers\textbf{\underline{AL}} \textbf{\underline{Prompt}} Optimization Framework, abbreviated as \textit{\textbf{\ourmethod{}}} which consists of two stages: 1) Predefined Prompt, based on the reinforcement searching strategy, generate a prompt optimization strategy tree to acquire optimization priors; 2) Prompt Reflection initializes the prompt based on optimization priors, followed by self-reflection to further search and refine the prompt. By doing so, \ourmethod{} elegantly generates the ``ideal prompts'' for processing private domain-specific data. Note that our method requires no parameter fine-tuning and only a small amount of data to quickly adapt to the data distribution of private data. Extensive experiments across multiple tasks demonstrate that our proposed \ourmethod{} significantly improves both efficiency and performance compared to baselines.

cs.AI↗

arXiv2025

Decision Information Meets Large Language Models: The Future of Explainable Operations Research

Operations Research (OR) is vital for decision-making in many industries. While recent OR methods have seen significant improvements in automation and efficiency through integrating Large Language Models (LLMs), they still struggle to produce meaningful explanations. This lack of clarity raises concerns about transparency and trustworthiness in OR applications. To address these challenges, we propose a comprehensive framework, Explainable Operations Research (EOR), emphasizing actionable and understandable explanations accompanying optimization. The core of EOR is the concept of Decision Information, which emerges from what-if analysis and focuses on evaluating the impact of complex constraints (or parameters) changes on decision-making. Specifically, we utilize bipartite graphs to quantify the changes in the OR model and adopt LLMs to improve the explanation capabilities. Additionally, we introduce the first industrial benchmark to rigorously evaluate the effectiveness of explanations and analyses in OR, establishing a new standard for transparency and clarity in the field.

cs.AI↗

arXiv2025

Probing $|V_{cs}|$ and lepton flavor universality through $D\to K_0^\ast(1430)\ellν_{\ell}$ decay

In this paper, we calculate the semileptonic decays $D\to K_0^\ast(1430)\ellν_{\ell}$ with $\ell=(e,μ)$ induced by $c\to s\ellν_{\ell}$ transition. For the key component, $D\to K_0^\ast(1430)$ transition form factors (TFFs) $f_{\pm}(q^2)$ are calculated within the framework of QCD light cone sum rule. Then, we consider two scenarios for $K_0^\ast(1430)$-meson twist-2 distribution amplitude. For the scenario 1 (S1), we take the truncated form based on Gegenbauer polynomial series. Meanwhile, we also consider the scenario 2 (S2) constructed by light cone harmonic oscillator model, where the model parameters are fixed by the $K_0^\ast(1430)$-meson twist-2 distribution amplitude tenth-order $ξ$ moments calculated by using the background field theory. For the TFFs at a large recoil point, we have $f_+^{\rm (S1)}(0)=0.597^{+0.122}_{-0.121}$ and $f_-^{\rm (S1)}(0)=-0.136^{+0.023}_{-0.035}$, $f_+^{\rm (S2)}(0)= 0.663^{+0.135}_{-0.134}$, and $f_-^{\rm (S2)}(0)=-0.202^{+0.026}_{-0.046}$. After extrapolating TFFs to the whole physical $q^2$ region, we calculate the branching fractions of $D^0\to K_0^{\ast +}(1430)\ell^-\barν_\ell$ and $D^+\to K_0^{\ast 0}(1430)\ell^+ν_\ell$, which at $10^{-4}$-order level for the S1 and S2 cases. Meanwhile, we predict the CKM matrix $|V_{cs}|^{\rm (S1)}=0.973^{+0.259}_{-0.183}, |V_{cs}|^{\rm (S2)}=0.880^{+0.234}_{-0.165}$, and lepton flavor universality $\mathcal{R}^{\rm (S1)}_{K_0^*}=0.768^{+0.560}_{-0.368}, \mathcal{R}_{K_0^*}^{\rm (S2)}=0.764^{+0.555}_{-0.365}$. Finally, we discuss the angular observables of forward-backward asymmetries, lepton polarization asymmetries, and $q^2$-differential flat terms for this decay.

hep-ph↗

arXiv2024

Fast and Interpretable Mixed-Integer Linear Program Solving by Learning Model Reduction

By exploiting the correlation between the structure and the solution of Mixed-Integer Linear Programming (MILP), Machine Learning (ML) has become a promising method for solving large-scale MILP problems. Existing ML-based MILP solvers mainly focus on end-to-end solution learning, which suffers from the scalability issue due to the high dimensionality of the solution space. Instead of directly learning the optimal solution, this paper aims to learn a reduced and equivalent model of the original MILP as an intermediate step. The reduced model often corresponds to interpretable operations and is much simpler, enabling us to solve large-scale MILP problems much faster than existing commercial solvers. However, current approaches rely only on the optimal reduced model, overlooking the significant preference information of all reduced models. To address this issue, this paper proposes a preference-based model reduction learning method, which considers the relative performance (i.e., objective cost and constraint feasibility) of all reduced models on each MILP instance as preferences. We also introduce an attention mechanism to capture and represent preference information, which helps improve the performance of model reduction learning tasks. Moreover, we propose a SetCover based pruning method to control the number of reduced models (i.e., labels), thereby simplifying the learning process. Evaluation on real-world MILP problems shows that 1) compared to the state-of-the-art model reduction ML methods, our method obtains nearly 20% improvement on solution accuracy, and 2) compared to the commercial solver Gurobi, two to four orders of magnitude speedups are achieved.

cs.LG↗

arXiv2024

The rare decay $B^+ \to K^+\ell^+\ell^-(ν\barν)$ under the QCD sum rules approach

In the paper, we conduct a detailed investigation of the rare decay processes of charged meson, specifically $B^+ \to K^+\ell^+\ell^-$ with $\ell=(e,μ,τ)$ and $B^+ \to K^+ν\barν$. These processes involve flavor-changing-neutral-current (FCNC) transitions, namely $b\to s\ell^+\ell^-$ and $b\to sν\barν$. The essential components $B\to K$ scalar, vector and tensor transition form factors (TFFs) are calculated by using the QCD light-cone sum rules approach up to next-to-leading order QCD corrections. In which, the kaon twist-2 and twist-3 light-cone distribution amplitudes are calculated from both the QCD sum rules within the framework of background field theory and the light-cone harmonic oscillator model. The TFFs at large recoil point are $f_+^{BK}(0)=f_0^{BK}(0) =0.328_{-0.028}^{+0.032}$ and $f_{\rm T}^{BK}(0)=0.277_{-0.024}^{+0.028}$, respectively. To achieve the behavior of those TFFs in the whole $q^2$-region, we extrapolate them by utilizing the simplified $z(q^2)$-series expansion. Furthermore, we compute the differential branching fractions with respect to the squared dilepton invariant mass for the two different decay channels and present the corresponding curves. Our predictions of total branching fraction are ${\cal B}(B^+\to K^+ e^+ e^-)=6.633_{-1.070}^{+1.341}\times 10^{-7}$, ${\cal B}(B^+\to K^+ μ^+ μ^-)=6.620_{-1.056}^{+1.323}\times 10^{-7}$, ${\cal B}(B^+\to K^+ τ^+ τ^-)=1.760_{-0.197}^{+0.241}\times 10^{-7}$, and ${\cal B}(B^+\to K^+ ν\barν)=4.135_{-0.655}^{+0.820}\times 10^{-6}$, respectively. Lastly, the observables such as the lepton universality $\mathcal{R}_{K}$ and the angular distribution `flat term' $F_{\rm H}^\ell$ are given, which show good agreement with the theoretical and experimental predictions.

hep-ph↗

arXiv2024

Prospective analysis of CKM element $|V_{cd}|$ and $D^+$-meson decay constant from leptonic decays $D^+ \to \ell^+ ν$

The leptonic decay of $D^+$-meson has attracted significant interest due to its unique characteristics. In this paper, we carry out an investigation into the $D^+$-meson leptonic decays $D^+\to \ell^+ν_{\ell}$ with $\ell=(e,μ,τ)$ by employing the QCD sum rules approach. In which the $D^+$-meson decay constant $f_{D^+}$ is an important input parameter in the process. To enhance the accuracy of our calculations for $f_{D^+}$, we consider the quark propagator and vertex up to dimension-six within the framework of background field theory. Consequently, we obtain the QCD sum rule expression for $f_{D^+}$ up to dimension-six condensates, yielding $f_{D^+}=203.0\pm1.5~\mathrm{MeV}$. Our results are in good agreement with BESIII measurements and theoretical predictions. We also present the integrated decay widths for the $D^+$-meson in three channels $Γ(D^+\to e^+ν_e)=(5.263_{-0.075}^{+0.076})\times10^{-21}~\mathrm{GeV}$, $Γ(D^+\to μ^+ν_μ)=(2.236_{-0.032}^{+0.032})\times10^{-16}~\mathrm{GeV}$ and $Γ(D^+\to τ^+ν_τ)=(5.958_{-0.085}^{+0.086})\times10^{-16}~\mathrm{GeV}$. Accordingly, we compute the branching fraction $\mathcal{B}(D^+\to\ell^+ν_{\ell})$ with the electron, muon and tau channels, which are $\mathcal{B}(D^+\to e^+ν_e)=(8.260_{-0.118}^{+0.119})\times10^{-9}$, $\mathcal{B}(D^+\toμ^+ν_μ)=(3.508_{-0.050}^{+0.051})\times10^{-4}$ and $\mathcal{B}(D^+\toτ^+ν_τ)=(0.935_{-0.013}^{+0.013})\times10^{-3}$. Furthermore, we present our prediction for the CKM matrix element $|V_{cd}|$ using the branching fraction $\mathcal{B}(D^+\toμ^+ν_μ)$ obtained from BESIII Collaboration, yielding $|V_{cd}|=0.227_{-0.001}^{+0.002}$.

hep-ph↗