arXiv subjects

Jiahui Shen

Publications and source records attributed to Jiahui Shen.

5 recordsLinked to original sources

arXiv2026

Magnetic-Field-Calibration-Free Determination of the Hyperfine Constant $A$ in Ultracold Fermi gases of $^{40}$K

Hyperfine constant $A$ is a key parameter of the hyperfine structure and underpins precision spectroscopy and metrology. In this Letter, we develop a magnetic-field-calibration-free method for determining the ground-state hyperfine constant $A$ in an ultracold $^{40}$K Fermi gas by utilizing a pair of magnetically insensitive ("clock") transitions. This overcomes the stringent magnetic-field calibration requirements of conventional methods. We measure the transition frequency between these two magnetically insensitive transitions with Hz-level resolution over a range of magnetic fields, and obtain the ground-state hyperfine constant $A = -h\times 285.730536(2)\,\mathrm{MHz}$, corresponding to an absolute uncertainty of about $2\,\mathrm{Hz}$. Our value reduces the uncertainty by nearly three orders of magnitude compared with previous determinations, providing a substantially improved reference for high-precision spectroscopy and metrology with $^{40}$K.

cond-mat.quant-gas↗

arXiv2026

Geometric phase-space nonseparability triggers giant optical shifts

Nonseparability among multiple degrees of freedom has enabled fundamental advances in structured light and related applications. Here we unveil a previously overlooked form of nonseparability in phase space, which we term geometric phase-space nonseparability. The latter arises solely from the wavefront curvature of a conventional wave packet, such as a fundamental Gaussian beam. This phase-space structure manifests as a position-dependent transverse-momentum distribution across the beam profile leading to the giant spatial and angular beam shifts upon reflection at a planar interface that we predict analytically and observe experimentally. Remarkably, the curvature-induced phase-space correlation remains robust against spatial-coherence degradation, allowing the giant shifts to persist even in the nearly incoherent regime. Our results establish wavefront curvature as a general mechanism for engineering beam shifts across optical, acoustic, and matter-wave systems.

physics.optics↗

arXiv2026

Experimental study of magnetically insensitive transitions in ultracold Fermi gas of $^{40}$K

This paper presents an experimental study of microwave single-photon transitions that are magnetic-field-insensitive in degenerate Fermi gases of $^{40}$K. This contrasts with microwave single-photon clock transitions for 0-0 magnetic-field-insensitive states and two-photon clock transitions for non 0-0 magnetic-field-insensitive states in bosonic alkali metal atoms. We show that there are two sets of special transitions between two different hyperfine ground states ($|F$=9/2, $m_{F}$=1/2$\rangle$ $\Leftrightarrow$ $|$7/2, -1/2$\rangle$ and $|$9/2, -1/2$\rangle$ $\Leftrightarrow$ $|$7/2, 1/2$\rangle$), whose microwave single-photon transition frequency is insensitive to low magnetic fields, as the first-order Zeeman shift is almost completely canceled. By using the microwave spectrum and Ramsey interference fringes, we demonstrate the long-time stability of the coherent transition under magnetic field fluctuations. These magnetic-field-insensitive microwave hyperfine transitions in ultracold $^{40}$K Fermi gases offer promising applications in quantum information and precision measurements.

cond-mat.quant-gas↗

arXiv2023

Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studies

Outliers widely occur in big-data applications and may severely affect statistical estimation and inference. In this paper, a framework of outlier-resistant estimation is introduced to robustify an arbitrarily given loss function. It has a close connection to the method of trimming and includes explicit outlyingness parameters for all samples, which in turn facilitates computation, theory, and parameter tuning. To tackle the issues of nonconvexity and nonsmoothness, we develop scalable algorithms with implementation ease and guaranteed fast convergence. In particular, a new technique is proposed to alleviate the requirement on the starting point such that on regular datasets, the number of data resamplings can be substantially reduced. Based on combined statistical and computational treatments, we are able to perform nonasymptotic analysis beyond M-estimation. The obtained resistant estimators, though not necessarily globally or even locally optimal, enjoy minimax rate optimality in both low dimensions and high dimensions. Experiments in regression, classification, and neural networks show excellent performance of the proposed methodology at the occurrence of gross outliers.

stat.ME↗

arXiv2022

Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction

Modern high-dimensional methods often adopt the "bet on sparsity" principle, while in supervised multivariate learning statisticians may face "dense" problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modeling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed-point estimators, though not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale-free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real-data experiments demonstrate the statistical accuracy and interpretability of the proposed method.

stat.ML↗