arXiv subjects

Xiaolei Li

Publications and source records attributed to Xiaolei Li.

At least 19 recordsLinked to original sources

arXiv2026

GraphDroid: Asynchronous LLM-Based Mobile App GUI Testing via History-Aware Exploration and Hybrid Intent Fulfillment

Automated GUI testing is a widely adopted technique for ensuring mobile application quality by simulating user interactions to exercise functionalities. Despite the research breakthroughs in the past decades, covering complex functionalities that require multi-step action sequences still remains challenging. Traditional tools lack semantic understanding capability and can rarely synthesize such action sequences. Recent LLM-based tools can generate test intents describing target functionalities and leverage the LLM to fulfill the intents, but suffer from three key limitations: 1) loss of historical context for identifying uncovered functionalities, 2) synchronous intent generation that blocks exploration, and 3) per-step LLM-driven fulfillment incurring high cost and latency. To address these limitations, we propose GraphDroid, an intent-driven GUI testing framework that integrates a cluster-based memory mechanism to effectively identify uncovered functionalities from historically visited states for comprehensive application testing. For improving testing efficiency, GraphDroid adopts an asynchronous intent generation paradigm that eliminates the latency bottleneck and a hybrid intent fulfillment strategy that reserves the LLM for fulfilling complex intents while delegating simple intents to a lightweight heuristic algorithm. We evaluate GraphDroid on 41 real-world Android apps against six state-of-the-art baselines. Results show that GraphDroid outperforms all baselines, achieving up to 36.4% higher code coverage while incurring less than one eighth of the cost of the best pure LLM-based baseline. GraphDroid also exposes 19 bugs in the 41 apps and detects 13 of 52 crashes in the Themis bug benchmark, surpassing all the six baselines. Seven of the 19 bugs were previously unknown and we reported them to the developers. So far, four bugs have been confirmed and fixed.

cs.SE↗

arXiv2026

Splashing-regime transitions and secondary-droplet scaling in oblique drop impacts on a deep pool

Oblique drop impact onto a deep liquid pool produces asymmetric crowns, directional jetting, and splashing transitions that cannot be characterized by the total impact inertia alone. We numerically investigate water drops impacting a quiescent deep pool over $41\leq We\leq1790$ and $10^\circ\leqθ\leq90^\circ$. The simulations reproduce the principal features observed experimentally and identify five post-impact regimes in the $We$--$θ$ plane: deposition, front splashing, side splashing, side-front splashing, and crown splashing. The deposition--front-splashing transition is described by the tangential-inertial parameter $K_s=We\cosθ$, with $K_s^c\approx120$. This criterion follows from the competition between downstream crown-rim inertia and capillary retraction at the Taylor--Culick velocity. The transition from front to side-front splashing is instead governed primarily by normal impact inertia, with a critical normal Weber number $We_N^c\approx318$. Beyond these regime transitions, the secondary-droplet statistics reveal fragmentation behavior common to the different splashing regimes. The droplet-size distributions are positively skewed, and the median diameter follows $d_{s,\mathrm{med}}/D\sim We^{-3/5}$. Second-order velocity structure functions support a scale-dependent capillary--inertial description of rim and ligament breakup. Combined with mass conservation, this scaling gives $N_s\sim We^{9/5}$, providing a numerical explanation for the secondary-droplet-number scaling observed experimentally. Thus, directional impact inertia governs the macroscopic selection of splashing regimes, whereas the secondary-droplet populations across these regimes exhibit a common capillary--inertial fragmentation scaling.

physics.flu-dyn↗

arXiv2026

Radial Evolution of Near-Sun Magnetic Switchbacks Alfvenicity, Occurrence Rate, and Size

Magnetic switchbacks, characterized by reversals of magnetic field direction, are widely observed in the inner heliosphere by Parker Solar Probe (PSP). With PSP reaching perihelia near 10Rs, observations from the first 24 encounters enable studies of near-Sun switchback evolution at r > 10Rs. We construct a switchback catalog within 10 < r < 55Rs by identifying magnetic field reversals with stable field magnitude and strahl-electron polarity. Statistical analysis shows that switchback Alfvenicity decreases with increasing radial distance, consistent with solar wind evolution beyond the Alfven critical point. Meanwhile, switchback occurrence rate and spatial size increase with distance, suggesting continued generation and expansion during solar wind propagation. At a given radial distance, the fraction of solar wind containing switchbacks is positively correlated with background solar wind radial velocity (VR) and Alfven Mach number (MA), while the local occurrence rate is mainly controlled by MA. These results suggest that switchback patches preferentially form in faster and higher-MA solar wind. The spatial size of switchbacks shows no clear dependence on MA or VR, implying that their size evolution is probably not determined by source conditions. Solar activity influences switchback evolution through changes in background solar wind properties, with a larger fraction of higher-MA switchbacks during solar minimum. We further identify anisotropy relative to the background magnetic field direction: the local occurrence rate and spatial size are approximately 1.5 times as large in the perpendicular direction as in the parallel direction, indicating distinct magnetic topology of switchback patches

astro-ph.SR↗

arXiv2026

MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry

Robust inertial odometry is essential for various carriers when external sensing is unreliable. Learning-based methods reduce integration drift by capturing local motion priors, but these methods often remain tied to a particular carrier, limiting generalization across heterogeneous platforms. We present MosaicIMU, a carrier-conditioned Mixture-of-Experts (MoE) pretraining-and-adaptation framework for generalizable neural inertial odometry. MosaicIMU uses a prototype-based router to compose carrier-specific expert features, decodes local velocity and uncertainty constraints, and integrates them with a history-aware EKF. For unseen domain adaptation, it freezes the pretrained base model and learns a new lightweight expert residual branch. For edge-deployment, it further reuses the router to select informative online samples for efficient incremental updates. Experiments show that MosaicIMU consistently outperforms learning-based baselines, reducing average ATE and RTE-10s by 40% and 34%, respectively. These results highlight that MosaicIMU provides a scalable pretraining-to-deployment paradigm for generalizable and adaptive neural inertial odometry.

cs.RO↗

arXiv2026

Metastability in Emergent Dark Energy: A New Framework Confronting Cosmological Observations

We propose the Metastable Emergent Dark Energy (MEDE) model, a novel phenomenological extension of the Phenomenological (PEDE) and Generalized (GEDE) Emergent Dark Energy frameworks, in which dark energy exhibits a transitionary behavior, appearing at late times and vanishing toward the future. This model naturally enables a smooth crossing of the phantom divide line in the dark energy equation of state, as hinted at by recent observations. The MEDE model is defined by a hyperbolic tangent dark energy equation of state $w(z)=-1-Δ\tanh[\log_{10}((1+z)/(1+z_t))]$, introducing only two free parameters, the transition redshift $z_t$ and the variation amplitude $Δ$, allowing both the emergent and transitionary behavior of dark energy. We constrain the MEDE model using a combined dataset of Planck CMB, DESI DR2 BAO, and different compilations of Type Ia supernovae, obtaining $z_t=0.425^{+0.084}_{-0.120}$ and $Δ=0.87^{+0.29}_{-0.35}$ (for CMB+DESI+PantheonPlus), indicating a statistically significant deviation from the cosmological constant. Statistical comparisons show that the MEDE model is preferred over $Λ$CDM by the combined dataset, with $Δ\rm DIC_{ MEDE-ΛCDM}= -9.29$. The MEDE model performs comparably to the CPL dynamical dark energy parametrization ($Δ\rm DIC_{MEDE-CPL} = 0.74$), with no strong statistical distinction from CPL using current data. Notably, MEDE preserves the success of $Λ$CDM in describing early-universe physics and naturally accommodates the phantom-crossing signature indicated by the latest low-redshift observations. The MEDE scenario provides a compelling dark energy phenomenology that may guide us toward interesting theoretical implications.

astro-ph.CO↗

arXiv2026

From Exploration to Exploitation: A Two-Stage Entropy RLVR Approach for Noise-Tolerant MLLM Training

Reinforcement Learning with Verifiable Rewards (RLVR) for Multimodal Large Language Models (MLLMs) is highly dependent on high-quality labeled data, which is often scarce and prone to substantial annotation noise in real-world scenarios. Existing unsupervised RLVR methods, including pure entropy minimization, can overfit to incorrect labels and limit the crucial reward ranking signal for Group-Relative Policy Optimization (GRPO). To address these challenges and enhance noise tolerance, we propose a novel two-stage, token-level entropy optimization method for RLVR. This approach dynamically guides the model from exploration to exploitation during training. In the initial exploration phase, token-level entropy maximization promotes diverse and stochastic output generation, serving as a strong regularizer that prevents premature convergence to noisy labels and ensures sufficient intra-group variation, which enables more reliable reward gradient estimation in GRPO. As training progresses, the method transitions into the exploitation phase, where token-level entropy minimization encourages the model to produce confident and deterministic outputs, thereby consolidating acquired knowledge and refining prediction accuracy. Empirically, across three MLLM backbones - Qwen2-VL-2B, Qwen2-VL-7B, and Qwen2.5-VL-3B - spanning diverse noise settings and multiple tasks, our phased strategy consistently outperforms prior approaches by unifying and enhancing external, internal, and entropy-based methods, delivering robust and superior performance across the board.

cs.LG↗

arXiv2026

Breaking the Mass-sheet Degeneracy in Time-delay Cosmology with Lensed and Unlensed Type Ia Supernovae

This study introduces an innovative framework aimed at overcoming the ongoing issue of mass-sheet degeneracy (MSD) in time-delay cosmography by incorporating observations of both gravitationally lensed and unlensed Type Ia supernovae (SNe Ia). By simultaneously using lensing magnification measurements $μ^{\rm{obs}}$ and cosmic distance ratios ($D_s/D_{ds}$), we develop a Bayesian framework capable of breaking the MSD. Specifically, we reconstruct the distance-redshift and magnitude-redshift relations from unlensed Type Ia supernovae using Gaussian process to avoid dependence on specific cosmological models. Our framework shows substantial efficacy in resolving the MSD by imposing constraints on the MSD parameter $λ$. Furthermore, we extend this framework to analyze multiple gravitational lensing systems. The results show strong agreement with the fiducial MSD parameters used in the data simulation, confirming our method's effectiveness in mitigating the MSD. Ultimately, this technique enables the derivation of corrected time-delay distance measurements under the MSD, improving the precision of cosmological parameters inferred from strong lensing systems.

astro-ph.CO↗

arXiv2026

Depth estimation of a monoharmonic source using a vertical linear array at fixed distance

Estimating the depth of a monoharmonic sound source at a fixed range using a vertical linear array (VLA) is challenging in the absence of seabed environmental parameters, and relevant research remains scarce. The orthogonality constrained modal search based depth estimation (OCMS-D) method is proposed in this paper, which enables the estimation of the depth of a monoharmonic source at a fixed range using a VLA under unknown seabed parameters. Using the sparsity of propagating normal modes and the orthogonality of mode depth functions, OCMS-D estimates the normal mode parameters under a fixed source-array distance at first. The estimated normal mode parameters are then used to estimate the source depth. To ensure the precision of the source depth estimation, the method utilizes information on both the amplitude distribution and the sign (positive/negative) patterns of the estimated mode depth functions at the inferred source depth. Numerical simulations evaluate the performance of OCMS-D under different conditions. The effectiveness of OCMS-D is also verified by the Yellow Sea experiment and the SWellEx-96 experiment. In the Yellow Sea experiment, the depth estimation absolute errors by OCMS-D with a 4-second time window are less than 2.4 m. And the depth estimation absolute errors in the SWellEx-96 experiment with a 10-second time window are less than 5.4 m for the shallow source and less than 10.8 m for the deep source.

eess.SP↗

arXiv2025

Resilient Multi-Dimensional Consensus and Distributed Optimization against Agent-Based and Denial-of-Service Attacks

In this paper, we consider the resilient multi-dimensional consensus and distributed optimization problems of multi-agent systems (MASs) in the presence of both agent-based and denial-of-service (DoS) attacks. The considered agent-based attacks can cover malicious, Byzantine, and stubborn agents. The links between agents in the network can be blocked by DoS attacks, which may lead the digraph to be time-varying and even disconnected. The objective is to ensure that the remaining benign agents achieve consensus. To this end, an "auxiliary point"-based resilient control algorithm is proposed for MASs. Under the proposed algorithm, each healthy agent constructs a "safe kernel" utilizing the states of its in-neighbors and updates its state toward a specific point within this kernel at each iteration. If an agent cannot receive its neighbors' states owing to DoS attacks, it will use the states received immediately before the DoS period. Moreover, a resilient multi-dimensional distributed optimization (RMDO) algorithm is also proposed. Theoretical proofs and numerical examples are presented to demonstrate the effectiveness of the proposed algorithms.

eess.SY↗

arXiv2025

Asymmetric Information Enhanced Mapping Framework for Multirobot Exploration based on Deep Reinforcement Learning

Despite the great development of multirobot technologies, efficiently and collaboratively exploring an unknown environment is still a big challenge. In this paper, we propose AIM-Mapping, a Asymmetric InforMation Enhanced Mapping framework. The framework fully utilizes the privilege information in the training process to help construct the environment representation as well as the supervised signal in an asymmetric actor-critic training framework. Specifically, privilege information is used to evaluate the exploration performance through an asymmetric feature representation module and a mutual information evaluation module. The decision-making network uses the trained feature encoder to extract structure information from the environment and combines it with a topological map constructed based on geometric distance. Utilizing this kind of topological map representation, we employ topological graph matching to assign corresponding boundary points to each robot as long-term goal points. We conduct experiments in real-world-like scenarios using the Gibson simulation environments. It validates that the proposed method, when compared to existing methods, achieves great performance improvement.

cs.MA↗

arXiv2025

Normal mode parameters estimation by a VLA in single-shooting

This paper proposes an orthogonality-constrained modal search (OCMS) method for estimating modal wavenumbers and modal depth functions using a vertical linear array (VLA). Under the assumption of a known sound speed profile, OCMS leverages the orthogonality of distinct modal depth functions to extract both the modal depth functions and their corresponding wavenumbers, even when the VLA and a monochromatic sound source remain stationary.The performance of OCMS is evaluated through numerical simulations under varying signal-to-noise ratios (SNRs), different VLA apertures, varying numbers of VLA elements, VLA tilt and sound speed profile (SSP) uncertainty. The results demonstrate that OCMS is robust against noise, VLA aperture variations, and changes in the number of VLA elements, meanwhile, the algorithm maintains reliable performance when SSP uncertainty < 1 m/s and VLA tilt angle <5°. Furthermore, the effectiveness of OCMS is validated using SwellEx96 experimental data. The relative error between the modal wavenumbers derived from experimental data and those computed via Kraken is on the order of $10^{-4}$.

eess.SP↗

arXiv2025

Torsion cosmology in the light of DESI, supernovae and CMB observational constraints

In this work, we investigate a torsion-based cosmological model within the Einstei-Cartan framework, constrained by the latest combined datasets including DESI DR2 BAO, PantheonPlus and DESY5 supernovae, and the full Planck 2018 CMB measurements (temperature, polarization, and joint NPIPE PR4 + ACT DR6 lensing). The torsion parameter is constrained to $α= -0.00066 \pm 0.00098$ with the full dataset combination, consistent with zero at less than $1σ$, while yielding a Hubble constant $H_0 = 68.41 \pm 0.32$ km/s/Mpc and matter clustering amplitude $S_8 = 0.812 \pm 0.006$. The model shows notable potential in alleviating cosmological tensions, reducing the $S_8$ discrepancy with KiDS-1000 from $\sim 2.3σ$ in $Λ$CDM to only $0.1σ$. Model comparisons based on the Akaike information criterion show consistent improvements across all datasets, with $Δ{\rm AIC}$ values ranging from $-5.68$ to $-6.62$, indicating a statistically preferred fit for the torsion model. These results suggest that the torsion framework provides a physically well-motivated extension to $Λ$CDM, capable of simultaneously addressing key cosmological tensions while maintaining excellent agreement with diverse observational probes.

astro-ph.CO↗

arXiv2025

Event-Triggered Resilient Consensus of Networked Euler-Lagrange Systems Under Byzantine Attacks

The resilient consensus problem is investigated in this paper for a class of networked Euler-Lagrange systems with event-triggered communication in the presence of Byzantine attacks. One challenge that we face in addressing the considered problem is the inapplicability of existing resilient decision algorithms designed for one-dimensional multi-agent systems. This is because the networked Euler-Lagrange systems fall into the category of multi-dimensional multi-agent systems with coupling among state vector components. To address this problem, we propose a new resilient decision algorithm. This algorithm constructs auxiliary variables related to the coordinative objectives for each normal agent, and transforms the considered resilient consensus problem into the consensus problem of the designed auxiliary variables. Furthermore, to relax the constraints imposed on Byzantine agent behavior patterns within continuous-time scenarios, the event-triggered communication scheme is adopted. Finally, the effectiveness of the proposed algorithm is demonstrated through case studies.

math.OC↗

arXiv2025

Dynamical Dark Energy in the Crosshairs: A Joint Analysis with DESI, Pantheon plus, and TDCOSMO Constraints

In this work, we perform a comprehensive joint analysis of three representative dark energy models - ${\rmΛCDM}$, the Chevallier-Polarski-Linder (CPL) parametrization, and the Generalised Emergent Dark Energy (GEDE) model - using the latest observational datasets: baryon acoustic oscillation (BAO) measurements from DESI Data Releases 1 and 2 (DR1/DR2), the Pantheon Plus sample of Type Ia supernovae (SNe Ia), and time-delay cosmography from TDCOSMO lensing. The CPL model yields a statistically significant improvement over ${\rmΛCDM}$, with $Δχ^2 = -3.6$ for DESI DR2+Pantheon Plus+TDCOSMO, favoring a quintessence-like behavior ($w_0=-0.87^{+0.05 }_{-0.05} $, $w_a=-0.41^{+ 0.28}_{-0.28}$ at 1$σ$ confidence level). The GEDE model also exhibits slightly favoring than ${\rmΛCDM}$ with observations, yielding $Δχ^2 =-2.83$ (DR1) and $Δχ^2 = -3.82$ (DR2). The captured potential dark energy evolution transition parameter $Δ$ in GEDE model is constrained to $-0.52^{+0.22}_{-0.20}$ (DR1) and $-0.50^{+0.18}_{-0.17}$ (DR2), showing a $2.4σ$ and $2.9σ$ deviation from zero respectively. This statistically significant ($>2σ$) non-zero value of $Δ$ provides evidence against a pure cosmological constant scenario. The negative sign indicates quintessence-like behavior ($w > -1$) in the late universe. Notably, the GEDE constraints show a $>3σ$ tension with the $Δ=1$ (Phenomenological Emergent Dark Energy model) predictions. This significant discrepancy implies that while both models belong to the emergent dark energy family, their fundamentally different transition mechanisms lead to distinct cosmological implications. Our results collectively suggest that GEDE provides a phenomenologically viable alternative to $Λ$CDM.

astro-ph.CO↗

arXiv2025

Revisiting the Hubble constant, sound horizon and cosmography from late-time Universe observations

The Hubble tension has become one of the central problems in cosmology. In this work, we determine the Hubble constant $H_0$ and sound horizon $r_d$ by using the combination of Baryon Acoustic Oscillations (BAOs) from DESI surveys, time-delay lensed quasars from H0LiCOW collaborations and the Pantheon supernovae observations. We consider two cosmological approaches, i.e., Taylor series and Padé polynomials, to avoid cosmological dependence. The reason for using this combination of data is that the absolute distance provided by strong gravitational lensing helps anchor the relative distance of BAO, and supernovae provide a robust history of universe evolution. {{Combining the 6 time-delay distance (6$D_{Δt}$) plus 4 angular diameter distance to the deflector (4$D_d$) measurements of time-delay lensed quasars,}} the BAO and the type Ia of supernovae (SNe Ia) datasets, we obtain a model-independent result of $r_d = 138.2_{-3.9}^{+3.3}$ Mpc and $H_0 = 72.9^{+1.8}_{-1.8}$ ${\mathrm{~km~s^{-1}~Mpc^{-1}}}$ for the Taylor series cosmography and $r_d = 137.0_{-3.7}^{+3.2}$ Mpc and $H_0 = 73.1_{-1.7}^{+1.8}$ ${\mathrm{~km~s^{-1}~Mpc^{-1}}}$ for the Padé polynomials cosmography. The determination of $r_d$ and $H_0$ prefers larger $H_0$ and smaller $r_d$ than Planck data under the assumption of flat-$Λ$CDM model. However, the values of $H_0$ are consistent with the $H_0$ determination from SH0ES collaboration.

astro-ph.CO↗

arXiv2025

Redshift evolution of the X-ray and UV luminosity relation of quasars: calibrated results from SNe Ia

Quasars could serve as standard candles if the relation between their ultraviolet and X-ray luminosities can be accurately calibrated. Previously, we developed a model-independent method to calibrate quasar standard candles using the distances-redshift relation reconstructed from Type Ia supernova at z<2 using Gaussian process regression. Interestingly, we found that the calibrated quasar standard candle dataset preferred a deviation from $Λ$CDM at redshifts above z>2. One interpretation of these findings is that the calibration parameters of the quasar UV-X-ray luminosity relationship evolves with redshift. In order to test the redshift dependence of the quasar calibration in a model-independent manner, we divided the quasar sample whose redshift overlap with the redshift coverage of Pantheon+ Type Ia supernova compilation into two sub-samples: a low-redshift quasar sub-sample and a high-redshift quasar sub-sample. Our present results show that there is about a 4$σ$ inconsistency between the quasar parameters inferred from the high-redshift quasar sub-sample and from the low-redshift sub-sample if no evolution of the quasar relation is considered. This inconsistency suggests the necessity of considering redshift evolution for the relationship between the quasars$'$ ultraviolet and X-ray luminosities. We then test an explicit parametrization of the redshift evolution of the quasar calibration parameters via $γ(z) = γ_0+γ_1(1+z)$ and $β(z)=β_0+β_1(1+z)$. Combining this redshift-dependent calibration relationship with the distance-redshift relationship reconstructed from Pantheon+ supernova compilation, we find the high-redshift sub-sample and low-redshift sub-sample become consistent at the 1$σ$ level, which means that the parameterized form of $γ(z)$ and $β(z)$ works well at describing the evolution of the quasar calibration parameters.

astro-ph.CO↗

arXiv2025

ReuseDroid: A VLM-empowered Android UI Test Migrator Boosted by Active Feedback

GUI testing is an essential quality assurance process in mobile app development. However, the creation and maintenance of GUI tests for mobile apps are resource-intensive and costly. Recognizing that many apps share similar functionalities, researchers have proposed various techniques to migrate GUI tests from one app to another with similar features. For example, some techniques employ mapping-based approaches to align the GUI elements traversed by the tests of a source app to those present in the target app. Other test migration techniques have also been proposed to leverage large language models (LLMs) by adapting the GUI tasks in source tests. However, these techniques are ineffective in dealing with different operational logic between the source and target apps. The semantics of GUI elements may not be correctly inferred due to the missing analysis of these flows. In this work, we propose REUSEDROID, a novel multiagent framework for GUI test migration empowered by Large Vision-Language Models (VLMs). REUSEDROID is powered by multiple VLM-based agents, each tackling a stage of the test migration process by leveraging the relevant visual and textual information embedded in GUI pages. An insight of REUSEDROID is to migrate tests based only on the core logic shared across similar apps, while their entire operational logic could differ. We evaluate REUSEDROID on LinPro, a new test migration dataset that consists of 578 migration tasks for 39 popular apps across 4 categories. The experimental result shows that REUSEDROID can successfully migrate 90.3% of the migration tasks, outperforming the best mapping-based and LLM-based baselines by 318.1% and 109.1%, respectively.

cs.SE↗

arXiv2025

Cosmological distance forecasts for the CSST Galaxy Survey using BAO peaks

The measurement of cosmological distances using baryon acoustic oscillations (BAO) is crucial for studying the universe's expansion. The Chinese Space Station Telescope (CSST) galaxy redshift survey, with its vast volume and sky coverage, provides an opportunity to address key challenges in cosmology. However, redshift uncertainties in galaxy surveys can degrade both angular and radial distance estimates. In this study, we forecast the precision of BAO distance measurements using mock CSST galaxy samples, applying a two-point correlation function (2PCF) wedge approach to mitigate redshift errors. We simulate redshift uncertainties of $σ_0 = 0.003$ and $σ_0 = 0.006$, representative of expected CSST errors, and examine their effects on the BAO peak and distance scaling factors, $α_\perp$ and $α_\parallel$, across redshift bins within $0.0 < z \leqslant 1.0$. The wedge 2PCF method proves more effective in detecting the BAO peak compared to the monopole 2PCF, particularly for $σ_0 = 0.006$. Constraints on the BAO peaks show that $α_\perp$ is well constrained around 1.0, regardless of $σ_0$, with precision between 1% and 3% across redshift bins. In contrast, $α_\parallel$ measurements are more sensitive to increases in $σ_0$. For $σ_0 = 0.003$, the results remain close to the fiducial value, with uncertainties ranging between 4% and 9%; for $σ_0 = 0.006$, significant deviations from the fiducial value are observed. We also study the ability to measure parameters $(Ω_m, H_0r_\mathrm{d})$ using distance measurements, proving robust constraints as a cosmological probe under CSST-like redshift uncertainties.

astro-ph.CO↗