arXiv subjects

Pu Jiao

Publications and source records attributed to Pu Jiao.

8 recordsLinked to original sources

arXiv2026

Improving Progressive Compression with Adaptive Interpolation and Coefficient Decomposition

Exascale simulations generate data far faster than it can be stored or analyzed, making efficient data reduction essential. Error-controlled lossy compression offers high compression ratios under user-specified error bounds, but the target tolerance must be fixed at compression time. Progressive compression relaxes this restriction, yet existing methods still rely on fixed refactoring strategies and do not fully exploit correlations among decomposed coefficients, limiting the efficiency of progressive retrieval. In this work, we present an adaptive progressive compression framework that improves retrieval efficiency for two common targets, namely error-bound and peak Signal-to-Noise ratios. Our contributions are fourfold. (1) We propose to leverage two complementary interpolation schemes for adaptive progressive compression toward different targets, and we optimize them to achieve high efficiency. (2) We propose coefficient decomposition, a novel method that exploits the commonly overlooked spatial correlations among decorrelated data, which further improves the efficiency. (3) We develop the adaptive progressive compression workflow with automatic selection of the best-fit refactoring pipeline and tailored optimizations. (4) We evaluate the proposed framework on five real-world scientific datasets against three state-of-the-art progressive compressors. Experimental results demonstrate that the proposed framework improves the compression ratio by up to $42.3\%$ under the same requested error tolerance and up to $92.5\%$ at the same PSNR, compared with the best-performing existing methods. When transferring $512$ GB of scientific data to remote sites, the framework delivers up to $1.26\times$ speedup in the end-to-end data transfer performance. Furthermore, our method achieves the highest visualization quality while retrieving the least amount of data from storage.

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arXiv2026

Precision extraction of the deuteron electric polarizability via the Baldin sum rule with full low-energy coverage

The photodisintegration cross sections of the deuteron have been systematically measured over the photon energy range of 2.33-19.65 MeV at the Shanghai Laser Electron Gamma Source (SLEGS). By applying the well-established Baldin sum rule to the newly obtained data, the sum of the electric and magnetic dipole polarizabilities of the deuteron is extracted for the first time based solely on a dense and continuous experimental dataset, yielding αE +\{beta}M = 0.719\pm0.009stat\pm0.014algo\pm0.023syst fm3 . With theoretical values of the magnetic polarizability \{beta}M calculated from the pionless effective field theory, a new value of the electric polarizability is obtained as αE = 0.637 \pm 0.009stat \pm 0.014algo \pm 0.023syst \pm 0.004theo fm3 , which is in excellent agreement with current theoretical predictions. This result resolves the previous discrepancy between experimental measurements from elastic scattering and theory, providing a high-precision benchmark for nuclear interaction models.

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arXiv2026

Time-varying Vector Field Compression with Preserved Critical Point Trajectories

Scientific simulations and observations are producing vast amounts of time-varying vector field data, making it hard to store them for archival purposes and transmit them for analysis. Lossy compression is considered a promising approach to reducing these data because lossless compression yields low compression ratios that barely mitigate the problem. However, directly applying existing lossy compression methods to timevarying vector fields may introduce undesired distortions in critical-point trajectories, a crucial feature that encodes key properties of the vector field. In this work, we propose an efficient lossy compression framework that exactly preserves all critical-point trajectories in time-varying vector fields. Our contributions are threefold. First, we extend the theory for preserving critical points in space to preserving critical-point trajectories in space-time, and develop a compression framework to realize the functionality. Second, we propose a semi-Lagrange predictor to exploit the spatiotemporal correlations in advectiondominated regions, and combine it with the traditional Lorenzo predictor for improved compression efficiency. Third, we evaluate our method against state-of-the-art lossy and lossless compressors using four real-world scientific datasets. Experimental results demonstrate that the proposed method delivers up to 124.48X compression ratios while effectively preserving all critical-point trajectories. This compression ratio is up to 56.07X higher than that of the best lossless compressors, and none of the existing lossy compressors can preserve all critical-point trajectories at similar compression ratios.

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arXiv2026

Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation

Error-bounded lossy compression has been regarded as a promising way to address the ever-increasing amount of scientific data in today's high-performance computing systems. Pre-quantization, a critical technique to remove sequential dependency and enable high parallelism, is widely used to design and develop high-throughput error-controlled data compressors. Despite the extremely high throughput of pre-quantization based compressors, they generally suffer from low data quality with medium or large user-specified error bounds. In this paper, we investigate the artifacts generated by pre-quantization based compressors and propose a novel algorithm to mitigate them. Our contributions are fourfold: (1) We carefully characterize the artifacts in pre-quantization based compressors to understand the correlation between the quantization index and compression error; (2) We propose a novel quantization-aware interpolation algorithm to improve the decompressed data; (3) We parallelize our algorithm in both shared-memory and distributed-memory environments to obtain high performance; (4) We evaluate our algorithm and validate it with two leading pre-quantization based compressors using five real-world datasets. Experiments demonstrate that our artifact mitigation algorithm can effectively improve the quality of decompressed data produced by pre-quantization based compressors while maintaining their high compression throughput.

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arXiv2026

High-Precision Measurement of D($γ$, $n$)$p$ Photodisintegration Reaction and Implications for Big-Bang Nucleosynthesis

We report on a high-precision measurement of the D($γ$,\,$n$)$p$ photodisintegration reaction at the newly commissioned Shanghai Laser Electron Gamma Source (SLEGS), employing a quasi-monochromatic $γ$-ray beam from Laser Compton Scattering. The cross sections were determined over $E_γ$=2.327--7.089 MeV, achieving up to a factor of 2.2 improvement in precision near the neutron separation threshold. Combined with previous data in a global Markov chain Monte Carlo (MCMC) analysis using dibaryon effective field theory, we obtained the unprecedentedly precise $p$($n$,\,$γ$)D cross sections and thermonuclear rate, with a precision up to $\approx$4 times higher than previous evaluations. Implemented in a standard Big-Bang Nucleosynthesis (BBN) framework, this new rate decreases uncertainty of the key cosmological parameter of baryon density $Ω_b h^2$ by up to $\approx$16\% relative to the LUNA result. A residual $\approx$1.2$σ$ tension between $Ω_b h^2$ constrained from primordial D/H observations and CMB measurements persists, highlighting the need for improved $dd$ reaction rates and offering potential hints of new physics beyond the standard model of cosmology.

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arXiv2025

New measurement of $^{51}$V($γ$,1n) cross section through the refined monochromatic cross section extraction method

The Giant Dipole Resonance (GDR) in $^{51}$V has been a long-term conflicting interpretation, with existing photoneutron cross section data suggesting either a single peak or a pronounced splitting, leading to opposite conclusions on nuclear deformation. A new measurement of the $^{51}$V($γ$,1n) cross section, performed at the Shanghai Laser Electron Gamma Source (SLEGS) facility, employs a refined monochromatic cross section extraction method. By integrating Polynomial Regression and Support Vector Regression (SVR) for robust interpolation and extrapolation, the new extracted monoenergetic cross sections exhibit a single, broad peak with no evidence of GDR splitting. This result provides new support for a spherical or near-spherical shape of $^{51}$V. Furthermore, we found that deliberately overfitting the data using an SVR model reproduces multi-peak structures similar to those reported in historical datasets, implying that the previously claimed splitting might originated from analysis artifacts rather than physical phenomena.

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arXiv2025

QPET: A Versatile and Portable Quantity-of-Interest-Preservation Framework for Error-Bounded Lossy Compression

Error-bounded lossy compression has been widely adopted in many scientific domains because it can address the challenges in storing, transferring, and analyzing unprecedented amounts of scientific data. Although error-bounded lossy compression offers general data distortion control by enforcing strict error bounds on raw data, it may fail to meet the quality requirements on the results of downstream analysis, a.k.a. Quantities of Interest (QoIs), derived from raw data. This may lead to uncertainties and even misinterpretations in scientific discoveries, significantly limiting the use of lossy compression in practice. In this paper, we propose QPET, a novel, versatile, and portable framework for QoI-preserving error-bounded lossy compression, which overcomes the challenges of modeling diverse QoIs by leveraging numerical strategies. QPET features (1) high portability to multiple existing lossy compressors, (2) versatile preservation to most differentiable univariate and multivariate QoIs, and (3) significant compression improvements in QoI-preservation tasks. Experiments with six real-world datasets demonstrate that integrating QPET into state-of-the-art error-bounded lossy compressors can gain 2x to 10x compression speedups of existing QoI-preserving error-bounded lossy compression solutions, up to 1000% compression ratio improvements to general-purpose compressors, and up to 133% compression ratio improvements to existing QoI-integrated scientific compressors.

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arXiv2025

First systematic experimental 2D mapping of linearly polarized $γ$-ray polarimetric distribution in relativistic Compton scattering

The interaction of photons with relativistic electrons constitutes a fundamental electromagnetic process whose polarization transfer mechanics remain incompletely characterized. We report the first systematic measurement of spatial polarization distribution for $γ$-rays generated via \SI{45}{\degree} slant inverse Compton scattering (ICS) between linearly polarized \SI{0.117}{\eV} photons and \SI{3.5}{\GeV} electrons, performing full 2D mapping of intensity, polarization angle (AOP), and degree of polarization (DOP). Measurements reveal an asymmetric beam profile along the laser's polarization direction that resembles \SI{180}{\degree} backward ICS observations. The central beam region exhibits DOP $\approx$ 1.0 with AOP rigidly aligned at \SI{45}{\degree}, while peripheral regions display complex non-uniform polarization distributions. These findings confirm quantum electrodynamics predictions of near-complete polarization transfer along the beam axis in slant geometries, thus establishing slant scattering as a viable alternative to head-on configurations for generating high DOP $γ$-rays.

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