arXiv subjects

Lexu Zhao

Publications and source records attributed to Lexu Zhao.

4 recordsLinked to original sources

arXiv2026

Two-magnon response from light scattering in altermagnets

Altermagnetism is a recently established class of magnetic order that combines fully compensated moments with momentum-dependent spin splitting, yet identifying its spectroscopic fingerprints remains an open challenge. In this work, we investigate finite-momentum two-magnon excitations in a two-dimensional $d$-wave altermagnet by calculating the two-magnon light scattering intensity in different polarization channels. Using linear spin-wave theory and further incorporating the leading $1/S$ quantum corrections, we demonstrate that the characteristic magnon splitting of altermagnets shifts the upper edge of the two-magnon continuum and the corresponding spectral features to higher energy at $\boldsymbol{X}=(\pi,0)$ relative to the conventional antiferromagnet, by an amount linear in the exchange anisotropy $|\delta J_2|$, while leaving the response at the Brillouin-zone center unchanged. Although magnon-magnon interactions strongly redistribute spectral weight toward lower energies, the energy scale associated with the high-energy momentum-selective reconstruction remains robust. The interactions additionally generate a pronounced low-energy two-peak structure that has no counterpart within linear spin-wave theory. At $\boldsymbol{K}=(\pi/2,\pi/2)$, we show that the interacting two-magnon resonance is twofold degenerate in the conventional antiferromagnetic phase, while a finite altermagnetic exchange anisotropy lifts this degeneracy, producing two peaks whose separation is linear in $|\delta J_2|$. The characteristic energy scales underlying both features are intrinsic to the two-magnon sector rather than to a specific scattering operator. These findings highlight the potential of finite-momentum two-magnon spectroscopy for identifying altermagnetic order in insulating magnets and motivate momentum-resolved resonant inelastic x-ray scattering studies of candidate altermagnetic materials.

cond-mat.str-el↗

arXiv2026

Ideal Bands in Tight-Binding Models

A band is called ideal when its Dirichlet functional saturates the topological lower bound. We study ideal bands in finite-band tight-binding models with conventional two-dimensional lattice translation symmetries, allowing the bands to have non-flat dispersion. We first provide an analytic construction of isolated Chern-ideal bands with Chern number $|\mathrm{Ch}|=1$ in finite-band models with exponentially decaying hopping. This construction applies only when at least two orbitals have different embedded positions (modulo lattice vectors), complementing the previously known construction for $|\mathrm{Ch}|>1$. We then show that isolated Chern-ideal bands with any nonzero Chern number cannot exist in finite-band models with finite-range hopping, regardless of the embedded orbital positions. The conclusion holds even if there are isolated band touching points, as long as the Berry curvature does not diverge anywhere in the Brillouin zone. We finally generalize the conclusions to Wilson-loop-ideal bands with zero total Chern number, such as Kane-Mele $\mathbb{Z}_2$-ideal bands.

cond-mat.mes-hall↗

arXiv2025

Reduced Density Matrices Through Machine Learning

$n$-particle reduced density matrices ($n$-RDMs) play a central role in understanding correlated phases of matter, but their calculation is often computationally inefficient for strongly-correlated states at large system sizes. In this work, we use neural network (NN) architectures to accelerate and even predict $n$-RDMs for large systems. Our underlying intuition is that, for gapped states, $n$-RDMs are often smooth functions over the Brillouin zone (BZ) and are therefore interpolable, allowing NNs trained on small-size systems to predict large-size ones. Building on this, we devise two NNs: (i) a self-attention NN that maps random RDMs to physical ones, and (ii) a Sinusoidal Representation Network (SIREN) that directly maps momentum-space coordinates to RDM values. We test the NNs on RDMs in three 2D models: the pair-pair correlation functions of the Richardson model of superconductivity, the translationally-invariant Hartree-Fock (HF) 1-RDM in a four-band repulsive model, and the translation-breaking HF 1-RDM in the half-filled Hubbard model. We find that a SIREN trained on a $6\times 6$ momentum mesh and a SIREN trained on $4$ tilted meshes (each of which has $12$ momentum points) can predict the $18\times 18$ pair-pair correlation function with a relative accuracy of $94.29\%$ and $93.77\%$, respectively. NNs trained on $6\times 6$ and $8\times 8$ meshes provide high-quality initial guesses for $50\times 50$ translation-invariant HF and $30\times 30$ fully translation-breaking-allowed HF, reducing the required number of iterations by up to $91.63\%$ and $92.78\%$, respectively, compared to random initializations. Our results illustrate the potential of NN-based methods for interpolable $n$-RDMs, which might open a new avenue for future research on strongly correlated phases.

cond-mat.str-el↗

arXiv2025

Classifying Cool Dwarfs: Comprehensive Spectral Typing of Field and Peculiar Dwarfs Using Machine Learning

Low-mass stars and brown dwarfs -- spectral types (SpTs) M0 and later -- play a significant role in studying stellar and substellar processes and demographics, reaching down to planetary-mass objects. Currently, the classification of these sources remains heavily reliant on visual inspection of spectral features, equivalent width measurements, or narrow-/wide-band spectral indices. Recent advances in machine learning (ML) methods offer automated approaches for spectral typing, which are becoming increasingly important as large spectroscopic surveys such as Gaia, SDSS, and SPHEREx generate datasets containing millions of spectra. We investigate the application of ML in spectral type classification on low-resolution (R $\sim$ 120) near-infrared spectra of M0--T9 dwarfs obtained with the SpeX instrument on the NASA Infrared Telescope Facility. We specifically aim to classify the gravity- and metallicity-dependent subclasses for late-type dwarfs. We used binned fluxes as input features and compared the efficacy of spectral type estimators built using Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) models. We tested the influence of different normalizations and analyzed the relative importance of different spectral regions for surface gravity and metallicity subclass classification. Our best-performing model (using KNN) classifies 95.5 $\pm$ 0.6% of sources to within $\pm$1 SpT, and assigns surface gravity and metallicity subclasses with 89.5 $\pm$ 0.9% accuracy. We test the dependence of signal-to-noise ratio on classification accuracy and find sources with SNR $\gtrsim$ 60 have $\gtrsim$ 95% accuracy. We also find that zy-band plays the most prominent role in the RF model, with FeH and TiO having the highest feature importance.

astro-ph.SR↗