arXiv subjects

Haiyan Du

Publications and source records attributed to Haiyan Du.

6 recordsLinked to original sources

arXiv2026

Sparse $ε$ insensitive zone bounded asymmetric elastic net support vector machines for pattern classification

Existing support vector machines(SVM) models are sensitive to noise and lack sparsity, which limits their performance. To address these issues, we combine the elastic net loss with a robust loss framework to construct a sparse $\varepsilon$-insensitive bounded asymmetric elastic net loss, and integrate it with SVM to build $\varepsilon$ Insensitive Zone Bounded Asymmetric Elastic Net Loss-based SVM($\varepsilon$-BAEN-SVM). $\varepsilon$-BAEN-SVM is both sparse and robust. Sparsity is proven by showing that samples inside the $\varepsilon$-insensitive band are not support vectors. Robustness is theoretically guaranteed because the influence function is bounded. To solve the non-convex optimization problem, we design a half-quadratic algorithm based on clipping dual coordinate descent. It transforms the problem into a series of weighted subproblems, improving computational efficiency via the $\varepsilon$ parameter. Experiments on simulated and real datasets show that $\varepsilon$-BAEN-SVM outperforms traditional and existing robust SVMs. It balances sparsity and robustness well in noisy environments. Statistical tests confirm its superiority. Under the Gaussian kernel, it achieves better accuracy and noise insensitivity, validating its effectiveness and practical value.

stat.ML↗

arXiv2026

Robust support vector model based on bounded asymmetric elastic net loss for binary classification

In this paper, we propose a novel bounded asymmetric elastic net ($L_{baen}$) loss function and combine it with the support vector machine (SVM), resulting in the BAEN-SVM. The $L_{baen}$ is bounded and asymmetric and can degrade to the asymmetric elastic net hinge loss, pinball loss, and asymmetric least squares loss. BAEN-SVM not only effectively handles noise-contaminated data but also addresses the geometric irrationalities in the traditional SVM. By proving the violation tolerance upper bound (VTUB) of BAEN-SVM, we show that the model is geometrically well-defined. Furthermore, we derive that the influence function of BAEN-SVM is bounded, providing a theoretical guarantee of its robustness to noise. The Fisher consistency of the model further ensures its generalization capability. Since the $ L_{\text{baen}} $ loss is non-convex, we designed a clipping dual coordinate descent-based half-quadratic algorithm to solve the non-convex optimization problem efficiently. Experimental results on artificial and benchmark datasets indicate that the proposed method outperforms classical and advanced SVMs, particularly in noisy environments.

stat.ML↗

arXiv2023

Calibration of a Micromegas-based Gaseous Time Projection Chamber Using Cosmic Ray Muons

We report the calibration of a gaseous Time Projection Chamber based on Micromegas charge readout modules with cosmic ray muons, utilizing their penetrating power and relatively uniform energy deposition per unit length. Muon events were selected through track reconstruction to characterize detector performances, such as the drift velocity, electron lifetime, detector gain, and electric field distortion. The evolution of detector performances with gas purities and electric drift fields over a 50-day data-taking cycle was measured by the muon calibration method. For instance, the drift velocity degraded with gas impurities from ${3.40\pm 0.07 ~ cm/μs}$ to ${3.06\pm 0.06 ~ cm/μs}$, and then recovered with gas purification. A ${^{137}Cs}$ calibration source was also placed inside the detector as a reference for muon calibrations.

physics.ins-det↗

arXiv2022

A Gaseous Time Projection Chamber with Micromegas Readout for Low Radioactive Material Screening

Low radioactive material screening is becoming essential for rare event search experiments, such as neutrinoless double beta decay and dark matter searches in underground laboratories. A gaseous time projection chamber (TPC) can be used for such purposes with large active areas and high efficiency. A gaseous TPC with a Micromegas readout plane of approximately 20$\times$20 cm$^2$ is successfully constructed for surface alpha contamination measurements. We have characterized the energy resolution, gain stability, and tracking capability with calibration sources. With the unique track-related background suppression cuts of the gaseous TPC, we have established that the alpha background rate of the TPC is 0.13$\pm$0.03 $μ$Bq/cm$^2$, comparable to the leading commercial solutions.

physics.ins-det↗

arXiv2022

Measurement of high-pressure xenon gas absorption in acrylic

Acrylic is a popular structural material in experiments requiring low background because of its radio-purity, machinability, and mechanical strength. However, its porosity may cause significant gas absorption and influence the detector stability in the long term. The interaction between acrylic and other detector materials becomes one of the key concerns in the detector design. In this paper, we carry out an experiment to measure quantitatively the absorption process of high-pressure xenon gas into acrylic. A specific setup is designed for the measurement, and systematic measurements are done to obtain a result of the absorption amount: 0.98 g xenon into 332 g of acrylic.

physics.ins-det↗

arXiv2021

Screener3D: a Gaseous Time Projection Chamber for Ultra-low Radioactive Material Screening

In experiments searching for rare signals, background events from the detector itself are some of the major factors limiting search sensitivity.Screening for ultra-low radioactive detector materials is becoming ever more essential. We propose to develop a gaseous time projection chamber (TPC) with a Micromegas readout for radio screening. The TPC records three-dimensional trajectories of charged particles emitted from a flat sample placed in the active volume of the detector. The detector can distinguish the origin of an event and identify the particle types with information from trajectories, which significantly increases the screening sensitivity. For $α$ particles from the sample surface, we observe that our proposed detector can reach a sensitivity higher than 100 $μ$Bq$\cdot$m$^{-2}$ within two days.

physics.ins-det↗