arXiv subjects

Zhejun Huang

Publications and source records attributed to Zhejun Huang.

3 recordsLinked to original sources

arXiv2025

Search for a solar-bound axion halo using the Global Network of Optical Magnetometers for Exotic physics searches

We report on a search for a gravitationally bound solar axion halo using data from the Global Network of Optical Magnetometers for Exotic physics searches (GNOME), a worldwide array of magnetically shielded atomic magnetometers with sensitivity to exotic spin couplings. Motivated by recent theoretical work suggesting that self-interacting ultralight axions can be captured by the Sun's gravitational field and thermalize into the ground state, we develop a signal model for the pseudo-magnetic fields generated by axion-proton gradient couplings in such a halo. The analysis focuses on the fifth GNOME Science Run (69 days, 12 stations), employing a cross-correlation pipeline with time-shifted daily modulation templates to search for the global, direction-dependent, monochromatic signal expected from a solar axion halo. No statistically significant candidate signals are observed. We set 95% confidence-level upper limits on the amplitude of the axion-induced pseudo-magnetic field over the frequency range $\approx 0.05-20$ Hz, translating to constraints on the linear and quadratic axion-proton couplings for halo densities predicted by gravitational capture models and for the maximum overdensities allowed by planetary ephemerides. In the quadratic coupling case, our limits surpass existing astrophysical bounds by over two orders of magnitude across much of the accessible parameter space.

physics.atom-ph↗

arXiv2023

Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation

In intelligent transportation systems, traffic data imputation, estimating the missing value from partially observed data is an inevitable and challenging task. Previous studies have not fully considered traffic data's multidimensionality and spatiotemporal correlations, but they are vital to traffic data recovery, especially for high-level missing scenarios. To address this problem, we propose a novel spatiotemporal regularized Tucker decomposition method. First, the traffic matrix is converted into a third-order tensor. Then, based on Tucker decomposition, the tensor is approximated by multiplying non-negative factor matrices with a sparse core tensor. Notably, we do not need to set the tensor rank or determine it through matrix nuclear-norm minimization or tensor rank minimization. The low rankness is characterized by the $l_1$-norm of the core tensor, while the manifold regularization and temporal constraint are employed to capture spatiotemporal correlations and further improve imputation performance. We use an alternating proximal gradient method with guaranteed convergence to address the proposed model. Numerical experiments show that our proposal outperforms matrix-based and tensor-based baselines on real-world spatiotemporal traffic datasets in various missing scenarios.

stat.ML↗

arXiv2020

Sparse Gaussian Process Based On Hat Basis Functions

Gaussian process is one of the most popular non-parametric Bayesian methodologies for modeling the regression problem. It is completely determined by its mean and covariance functions. And its linear property makes it relatively straightforward to solve the prediction problem. Although Gaussian process has been successfully applied in many fields, it is still not enough to deal with physical systems that satisfy inequality constraints. This issue has been addressed by the so-called constrained Gaussian process in recent years. In this paper, we extend the core ideas of constrained Gaussian process. According to the range of training or test data, we redefine the hat basis functions mentioned in the constrained Gaussian process. Based on hat basis functions, we propose a new sparse Gaussian process method to solve the unconstrained regression problem. Similar to the exact Gaussian process and Gaussian process with Fully Independent Training Conditional approximation, our method obtains satisfactory approximate results on open-source datasets or analytical functions. In terms of performance, the proposed method reduces the overall computational complexity from $O(n^{3})$ computation in exact Gaussian process to $O(nm^{2})$ with $m$ hat basis functions and $n$ training data points.

stat.ML↗