arXiv subjects

Yulei Zhang

Publications and source records attributed to Yulei Zhang.

14 recordsLinked to original sources

arXiv2026

High-Fidelity Hole Spin Qubits Reveal Quadrupolar Nuclear-Bath Dynamics in Isotopically Purified Planar Germanium

Planar Germanium has emerged as a promising platform to build spin-based large scale quantum computers. By exploiting the anisotropic hyperfine interaction of holes in Ge, qubits with long T2* have been recently realized. While the performance of single qubits is still more or less limited by 73Ge nuclear spin fluctuations, the site-to-site variation of qubit sweet spot becomes obstacles to maintaining high fidelity of each qubit across the whole wafer. To achieve high performance Ge-based quantum circuit, it is therefore essential to eliminate the origin source of hyperfine noise. In its Silicon counterparts, reduction of 29Si abundance enables exceptional high-fidelity operation. In contrast, hole qubits based on isotopically purified Ge have not been demonstrated. Here, we report the synthesis of high quality 2-dimensional hole gas (2DHG) with enriched 70GeH4 precursor. Due to the suppression of nonzero spin nucleus, the qubits' T2* on the sweet spot is moderately extended beyond 20 us, surpassing the previous best reported Ge hole qubits. More importantly, the qubits' T2* off the sweet spot is enhanced to above 3 us, enabling single qubit gate fidelity exceeding 99.9% in both operating regimes. Hahn-echo spectroscopy further resolves a finite-frequency nuclear-noise channel that is distinct from the conventional Larmor-linked hyperfine response. We associate this channel with quadrupole-modified dynamics of residual 73Ge nuclei sampling local electric-field gradients near the Ge/SiGe interface. Its field scaling and angle-dependent visibility are consistent with a qubit-visible quadrupolar nuclear-noise component transduced through the anisotropic hyperfine interaction of Ge holes. These results establish isotopically purified planar Ge as a high-coherence scalable platform for hole spin qubits and provide a spectroscopic probe of interfacial quadrupolar nuclear dynamics.

quant-ph↗

arXiv2026

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology

Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allows accurate constraints on the cosmological model. In recent years, high-order statistics and machine learning (ML) techniques have been applied to weak lensing data to extract the nonlinear information beyond traditional two-point analysis. However, these methods typically rely on cosmological simulations, which poses several challenges: simulations are computationally expensive, limiting most realistic setups to a low training data regime; inaccurate modeling of systematics in the simulations create distribution shifts that can bias cosmological parameter constraints; and varying simulation setups across studies make method comparison difficult. To address these difficulties, we present the first weak lensing benchmark dataset with several realistic systematics and launch the FAIR Universe Weak Lensing Machine Learning Uncertainty Challenge. The challenge focuses on measuring the fundamental properties of the universe from weak lensing data with limited training set and potential distribution shifts, while providing a standardized benchmark for rigorous comparison across methods. Organized in two phases, the challenge will bring together the physics and ML communities to advance the methodologies for handling systematic uncertainties, data efficiency, and distribution shifts in weak lensing analysis with ML, ultimately facilitating the deployment of ML approaches into upcoming weak lensing survey analysis.

astro-ph.CO↗

arXiv2026

DREAMuS: Dark matter REsearch with Advanced Muon Source

We propose DREAMuS, a fixed-target experiment at the High Intensity Heavy-Ion Accelerator Facility (HIAF), to search for muon-philic dark matter mediated by light flavor-violating bosons. DREAMuS is designed to probe the parameter space of a muon-philic dark matter (DM) mediated by a light flavor-violating boson, specifically a vector $Z'$ (or a scalar $\phi$) which is produced in muon-nucleus interactions and decays into dark matter particles with a distinctive detector signature. Precision tracking and time-of-flight measurements are used to suppress the Standard Model backgrounds. We find that DREAMuS can achieve competitive sensitivity in the GeV-scale muon-philic dark matter parameter space, reaching sensitivity to couplings at the $10^{-4}$, especially in the few-hundred-MeV region.In addition to a $\mu^-$ run, we highlight the potential of a complementary $\mu^+$ beam option, further improving sensitivity to dark matter below 200 $\mathrm{MeV}$ by an order of magnitude.

hep-ph↗

arXiv2026

EveNet: A Foundation Model for Particle Collision Data Analysis

While deep learning is transforming data analysis in high-energy physics, computational challenges limit its potential. We address these challenges in the context of collider physics by introducing EveNet, an event-level foundation model pretrained on 500 million simulated collision events using a hybrid objective of self-supervised learning and physics-informed supervision. By leveraging a shared particle-cloud representation, EveNet outperforms state-of-the-art baselines across diverse tasks, including searches for heavy resonances and exotic Higgs decays, and demonstrates exceptional data efficiency in low-statistics regimes. Crucially, we validate the transferability of the model to experimental data by rediscovering the $\Upsilon$ meson in CMS Open Data and show its capacity for precision physics through the robust extraction of quantum correlation observables stable against systematic uncertainties. These results indicate that EveNet can successfully encode the fundamental physical structure of particle interactions, which offers a unified and resource-efficient framework to accelerate discovery at current and future colliders.

hep-ex↗

arXiv2025

Search for Long-lived Particles at Future Lepton Colliders Using Deep Learning Techniques

Long-lived particles (LLPs) provide an unambiguous signal for physics beyond the Standard Model (BSM). They have a distinct detector signature, with decay lengths corresponding to lifetimes of around nanoseconds or longer. Lepton colliders allow LLP searches to be conducted in a clean environment, and such searches can reach their full physics potential when combined with machine learning (ML) techniques.This experimental study, utilizing comprehensive full simulation data samples, focuses on LLP searches resulting from Higgs decay in $e^+e^-\to ZH$. We demonstrate that, by employing deep neural network approaches the LLP signal efficiency can be improved up to 95\% for an LLP mass around 50 GeV and a lifetime of approximately 1 nanosecond, while rejecting all SM backgrounds. Furthermore, the signal sensitivity for the branching ratio of Higgs decaying into LLPs reaches a state-of-art limit of $1.0 \times 10^{-6}$ with a statistics of $4 \times 10^{6}$ Higgs.

hep-ex↗

arXiv2025

Quantum Information meets High-Energy Physics: Input to the update of the European Strategy for Particle Physics

Some of the most astonishing and prominent properties of Quantum Mechanics, such as entanglement and Bell nonlocality, have only been studied extensively in dedicated low-energy laboratory setups. The feasibility of these studies in the high-energy regime explored by particle colliders was only recently shown and has gathered the attention of the scientific community. For the range of particles and fundamental interactions involved, particle colliders provide a novel environment where quantum information theory can be probed, with energies exceeding by about 12 orders of magnitude those employed in dedicated laboratory setups. Furthermore, collider detectors have inherent advantages in performing certain quantum information measurements, and allow for the reconstruction of the state of the system under consideration via quantum state tomography. Here, we elaborate on the potential, challenges, and goals of this innovative and rapidly evolving line of research and discuss its expected impact on both quantum information theory and high-energy physics.

hep-ph↗

arXiv2025

Tunable Wigner Molecules in a Germanium Quantum Dot

The interplay between Coulomb interactions and kinetic energy underlies many exotic phases in condensed matter physics. In a two-dimensional electronic system, If Coulomb interaction dominates over kinetic energy, electrons condense into a crystalline phase which is referred as Wigner crystal. This ordered state manifests as Wigner molecule for few electrons at the microscopic scale. Observation of Wigner molecules has been reported in quantum dot and moire superlattice systems. Here we demonstrate hole Wigner molecules can be formed in a gate-defined germanium quantum dot with high tunability. By varying voltages applied to the quantum dot device, we can precisely tune the hole density by either changing the hole occupancy or the quantum dot size. For densities smaller than a certain critical value, Coulomb interaction localizes individual holes into ordered lattice sites, forming a Wigner molecule. By increasing the densities, melting process from a Wigner molecule to Fermi liquid-like particles is observed. An intermediate configuration which indicates the coexistence of ordered structure and disordered structure can be formed within a narrow effective density range. Our results provide a new platform for further exploration of the microscopic feature of strong correlated physics and open an avenue to exploit the application of Wigner molecules for quantum information in a very promising spin qubit platform.

cond-mat.mes-hall↗

arXiv2025

Fair Universe Higgs Uncertainty Challenge

This competition in high-energy physics (HEP) and machine learning was the first to strongly emphasise uncertainties in $(H \rightarrow \tau^+ \tau^-)$ cross-section measurement. Participants were tasked with developing advanced analysis techniques capable of dealing with uncertainties in the input training data and providing credible confidence intervals. The accuracy of these intervals was evaluated using pseudo-experiments to assess correct coverage. The dataset is now published in Zenodo, and the winning submissions are fully documented.

hep-ph↗

arXiv2025

FAIR Universe HiggsML Uncertainty Dataset and Competition

The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to compute and report confidence intervals for a parameter of interest regarding the Higgs boson while accounting for various systematic (epistemic) uncertainties. The dataset is a tabular dataset of 28 features and 280 million instances. Each instance represents a simulated proton-proton collision as observed at CERN's Large Hadron Collider in Geneva, Switzerland. The features of these simulations were chosen to capture key characteristics of different types of particles. These include primary attributes, such as the energy and three-dimensional momentum of the particles, as well as derived attributes, which are calculated from the primary ones using domain-specific knowledge. Additionally, a label feature designates each instance's type of proton-proton collision, distinguishing the Higgs boson events of interest from three background sources. As outlined in this paper, the permanent release of the dataset allows long-term benchmarking of new techniques. The leading submissions, including Contrastive Normalising Flows and Density Ratios estimation through classification, are described. Our challenge has brought together the physics and machine learning communities to advance our understanding and methodologies in handling systematic uncertainties within AI techniques.

hep-ph↗

arXiv2025

Entanglement and Bell Nonlocality in $\tau^+ \tau^-$ at the LHC using Machine Learning for Neutrino Reconstruction

Experiments at the CERN Large Hadron Collider (LHC) have accumulated an unprecedented amount of data corresponding to a large variety of quantum states. Although searching for new particles beyond the Standard Model of particle physics remains a high priority for the LHC program, precision measurements of the physical processes predicted in the Standard Model continue to lead us to a deeper understanding of nature at high energies. We carry out detailed simulations for the process $pp \to \tau^+\tau^- X$ to perform quantum tomography and to measure the quantum entanglement and the Bell nonlocality of the $\tau^+\tau^-$ two qubit state, including both statistical and systematic uncertainties. By using advanced machine learning techniques for neutrino momentum reconstruction, we achieve precise measurements of the full spin density matrix, a critical advantage over previous studies limited by reconstruction challenges for missing momenta. Our analysis reveals a clear observation of Bell nonlocality with high statistical significance, surpassing 5$\sigma$, establishing $\tau^+ \tau^-$ as an ideal system for quantum information studies in high-energy collisions. Given its experimental feasibility and the high expected sensitivity for Bell nonlocality, we propose that $\tau^+ \tau^-$ should be regarded as the new benchmark system for quantum information studies at the LHC, complementing and extending the insights gained from the $t\bar{t}$ system.

hep-ph↗

arXiv2024

Design of a LYSO Crystal Electromagnetic Calorimeter for DarkSHINE Experiment

This paper presents the design and optimization of a LYSO crystal electromagnetic calorimeter (ECAL) for the DarkSHINE experiment, which aims to search for dark photons as potential mediators of dark forces. The ECAL design was evaluated through comprehensive simulations, focusing on optimizing dimensions, material selection, energy distribution, and energy resolution. The ECAL configuration consists of 21$\times$21$\times$11 LYSO crystals, each measuring 2.5$\times$2.5$\times$4 cm$^3$, arranged in a staggered layout to improve signal detection efficiency. A 4 GeV energy dynamic range was established to ensure accurate energy measurements without saturation, which is essential for background rejection and signal identification. A detailed digitization model was developed to simulate the scintillation, SiPM, and ADC behaviors, providing a more realistic representation of detector performance. Additionally, the study assessed radiation damage in the ECAL region, highlighting the necessity of radiation-resistant scintillators and silicon sensors.

physics.ins-det↗

arXiv2024

A Design of Hadronic Calorimeter for DarkSHINE Experiment

The sensitivity of the dark photon search through invisible decay final states in low background experiments significantly relies on the neutron and muon veto efficiency, which depends on the amount of material used and the design of detector geometry. This paper presents an optimized design of a hadronic calorimeter (HCAL) used for the DarkSHINE experiment, which is studied using a GEANT4-based simulation framework. The geometry is optimized by comparing a traditional design with uniform absorbers to one that uses different thicknesses at different locations of the detector, which enhances the efficiency of vetoing low-energy neutrons at the sub-GeV level. The overall size and total amount of material used in HCAL are optimized to be lower due to the load and budget requirements, while the overall performance is studied to meet the physical objectives.

physics.ins-det↗

arXiv2024

Application of Graph Neural Networks in Dark Photon Search with Visible Decays at Future Beam Dump Experiment

Beam dump experiments provide a distinctive opportunity to search for dark photons, which are compelling candidates for dark matter with low mass. In this study, we propose the application of Graph Neural Networks (GNN) in tracking reconstruction with beam dump experiments to obtain high resolution in both tracking and vertex reconstruction. Our findings demonstrate that in a typical 3-track scenario with the visible decay mode, the GNN approach significantly outperforms the traditional approach, improving the 3-track reconstruction efficiency by up to 88% in the low mass region. Furthermore, we show that improving the minimal vertex detection distance significantly impacts the signal sensitivity in dark photon searches with the visible decay mode. By reducing the minimal vertex distance from 5 mm to 0.1 mm, the exclusion upper limit on the dark photon mass ($m_A\prime$) can be improved by up to a factor of 3.

hep-ex↗

arXiv2023

MusicJam: Visualizing Music Insights via Generated Narrative Illustrations

Visualizing the insights of the invisible music is able to bring listeners an enjoyable and immersive listening experience, and therefore has attracted much attention in the field of information visualization. Over the past decades, various music visualization techniques have been introduced. However, most of them are manually designed by following the visual encoding rules, thus shown in form of a graphical visual representation whose visual encoding schema is usually taking effort to understand. Recently, some researchers use figures or illustrations to represent music moods, lyrics, and musical features, which are more intuitive and attractive. However, in these techniques, the figures are usually pre-selected or statically generated, so they cannot precisely convey insights of different pieces of music. To address this issue, in this paper, we introduce MusicJam, a music visualization system that is able to generate narrative illustrations to represent the insight of the input music. The system leverages a novel generation model designed based on GPT-2 to generate meaningful lyrics given the input music and then employs the stable diffusion model to transform the lyrics into coherent illustrations. Finally, the generated results are synchronized and rendered as an MP4 video accompanied by the input music. We evaluated the proposed lyric generation model by comparing it to the baseline models and conducted a user study to estimate the quality of the generated illustrations and the final music videos. The results showed the power of our technique.

cs.HC↗