arXiv subjects

Yang Huang

Publications and source records attributed to Yang Huang.

At least 19 recordsLinked to original sources

arXiv2026

Mass Distribution of M31 from its Rotation Curve up to 167 kpc

The mass distribution of the Andromeda galaxy (M31) is fundamental for understanding its dark matter content, assembly history, and dynamical role within the Local Group. However, estimates of M31's total mass remain quite uncertain, partly because of limited dynamical constraints at large galactocentric radii. Moreover, M31's recent merger history has produced abundant tidal substructures and may have left its outer halo out of dynamical equilibrium, complicating equilibrium-based mass estimates. We use over 8,500 tracers with line-of-sight velocities to constrain the rotation curve (RC) of M31 over R=2--167 kpc. In the disk, we derive the RC from a clean tracer sample using the Jeans equation and find good agreement with previous H I measurements. In the halo, we use Gaussian mixture modeling to account for tidal substructures and Galactic foreground contamination and constrain the kinematics of the residual dynamically hot halo. Assuming this halo population can be treated as an equilibrium tracer, we infer the outer RC and constrain M31's mass distribution for different velocity anisotropies beta and dark matter halo profiles. The NFW and Dekel--Zhao (DZ) models yield broadly consistent total masses ranging from 1.12(+0.11/-0.10) to 1.63(+0.21/-0.17)x10^12 Msun for beta=0.3--0.7. In contrast, the Einasto profile favors substantially lower masses of 0.41(+0.09/-0.06) to 0.86(+0.21/-0.16)x10^12 Msun, highlighting significant dependence on the adopted halo profile, although Einasto solutions are more in tension with independent mass estimates based on M31 satellite galaxies than the NFW and DZ models at fixed beta. Overall, our analysis provides a comprehensive measurement of M31's mass distribution under the assumption of dynamical equilibrium, while also quantifying the systematic uncertainties associated with halo velocity anisotropy and the choice of dark matter density profile.

astro-ph.GA↗

arXiv2026

Distorting Kerr Images with Parity-Odd Scalar Hair

We investigate thin-disk imaging of Kerr black holes with synchronized scalar hair, focusing on backreacted parity-odd excited states of a complex scalar field minimally coupled to Einstein gravity. The spacetime displays a core-double-torus lensing structure, with a central black hole surrounded by two scalar clouds. We study the dependence of the images on hair strength and viewing angle, identifying a weak-hair regime close to Kerr. With increasing hair, the photon ring and shadow region shrink and become more distorted. In the strong-hair regime, gravitational lensing produces new features, including multiple disconnected shadow components, crescent-shaped structures, and signatures of chaotic lensing. For nearly edge-on viewing angles, repeated equatorial crossings generate nested ring-like patterns. These results highlight possible geometric signatures of black holes with excited scalar hair.

gr-qc↗

arXiv2026

High Resolution Spectroscopic Follow-up Observation Results for 13 EMP Candidates Selected by Narrow-band Photometry

Extremely metal-poor (EMP; [Fe/H] < -3.0) stars preserve key information about the earliest stages of Galactic chemical evolution. Based on a training set of spectroscopic metallicities, we constructed catalogs of more than 120,000 EMP candidates from large-scale narrow-band photometric surveys. To validate this approach, we obtained high-resolution CFHT/ESPaDOnS spectra for 13 candidates selected from the SkyMapper-based catalog. The follow-up observation confirms that the narrow-band photometric selection is effective in identifying very metal-poor stars and retaining a substantial EMP fraction: four targets are confirmed as EMP stars, and all targets remain very metal-poor with [Fe/H] < -2.4. The photometric metallicities are systematically lower than the spectroscopic values by 0.44 dex, with a relatively small scatter of 0.19 dex, indicating a systematic offset in the photometric metallicity scale that could be reduced through improved calibration. Abundances for more than 20 chemical species are derived, leading to the identification of a new potassium-enhanced r-II star, J2001-1215. By combining chemical abundances with orbital properties, five stars are found to be dynamically consistent with known Galactic substructures, including Gaia--Sausage--Enceladus, Thamnos, and Sequoia. These stars provide high-resolution chemical measurements for the extremely metal-poor regime of these Galactic substructures. These results underscore the role of narrow-band photometry in efficiently selecting EMP candidates for targeted high-resolution spectroscopic follow-up, thereby enabling detailed chemical and dynamical studies of the early Milky Way.

astro-ph.GA↗

arXiv2026

Decoding RR Lyrae light curves with deep learning for accurate absolute magnitude estimation

RR Lyrae stars are essential standard candles for distance measurements in the Milky Way and nearby galaxies. Traditional estimates rely on the Period--Absolute Magnitude--Metallicity relation but are limited by uncertainties in metallicity determinations. We present a deep learning approach that directly predicts absolute magnitudes from RRab and RRc light curves, eliminating the need for metallicity estimates. Our model achieves validation precisions of 0.053 mag and 0.036 mag (approximately 2.5% and 1.7% in distance) for individual RRab and RRc stars, respectively. Tests on globular clusters yield typical distance precisions of 1.0% for RRab and 1.7% for RRc stars. For the benchmark systems, combining the RRab- and RRc-based models yields distance moduli of 18.498 $\pm$ 0.001$_{\text{stat}}$ $\pm$ 0.018$_{\text{sys}}$ mag for the Large Magellanic Cloud and 19.564 $\pm$ 0.003$_{\text{stat}}$ $\pm$ 0.019$_{\text{sys}}$ mag for the Sculptor dwarf spheroidal galaxy. These measurements are in excellent agreement with previous results, achieve a distance precision of approximately 1%, and represent a 1.8-fold improvement over traditional RR Lyrae calibration relations. Our approach showcases the ability of AI to directly extract key physical parameters from complex, information-rich light curves, resolve degeneracies, and scale to broader applications.

astro-ph.GA↗

arXiv2026

Radio Emission with Dust: A Bow-Shock Interpretation for the TDE Candidate AT 2019avd

Forward shocks produced by interactions between a single ejected blob and the circumnuclear medium (CNM) around a supermassive black hole are widely invoked to explain radio emission from tidal disruption events (TDEs). However, recent observational evidence such as rapidly rising radio emission, double-peaked broadband spectral energy distributions, and declining shock energies at late times poses challenges to this picture. Alternative scenarios have therefore been proposed, including bow shocks arising from outflows colliding with dense clouds, the production of new ejecta at late times, and variations in microphysical parameters. To investigate the roles of these factors, we analyze the long-lived radio flare of the TDE candidate AT 2019avd, for which dust has been identified through mid-infrared observations, using a radio dataset spanning more than five years. We find that a bow-shock scenario can account for the majority of the observed radio emission, while a forward shock, or an additional bow shock involving different clouds, may begin to dominate at late times. However, a forward-shock scenario remains viable when allowing for evolving microphysical parameters and more complex CNM structures. We discuss the remaining tensions in both the outflow-CNM and outflow-cloud interaction scenarios.

astro-ph.HE↗

arXiv2026

Fourier Neural Operators for Composition-Driven Crystal Structure Discovery

Crystalline materials discovery is essential for energy, electronics, and catalysis, but the vast chemical and structural space makes exhaustive screening infeasible. Existing voxel-based methods are limited by the local receptive fields of three-dimensional convolutional neural networks and the posterior collapse of high-dimensional variational autoencoders. Here, we develop a Fourier Neural Operator (FNO)-based crystal-field solver that maps a prescribed chemical formula and lattice parameters to periodic number-density and electron-density fields. By operating on global Fourier modes, the solver captures long-range correlations in periodic crystal fields beyond conventional local convolutions. Building on this solver, we construct a coupled generation-solving framework in which a conditional variational autoencoder generates diverse candidate lattice parameters in a low-dimensional basis-coefficient space, followed by density-field prediction and atomic reconstruction through peak detection, position optimization, and weight optimization. The reconstructed structures are further screened using voxel-level filtering, machine-learning interatomic-potential relaxation, and first-principle calculations. The framework generates novel structures across 104 chemical formulas with competitive reconstruction accuracy, demonstrating high generative diversity and structural validity. By extending Fourier neural operators to periodic crystal fields and coupling them with composition-conditioned lattice generation, our approach provides a scalable route to crystal structure discovery from prescribed chemical compositions.

cond-mat.mtrl-sci↗

arXiv2026

Evolution of Stellar Activity and Habitable Zone (EATEN): III. X-ray Activity of Dwarfs in Open Clusters and Field Stars

Stellar X-ray emission serves as a direct diagnostic of coronal activity, which is fundamentally linked to coronal heating processes. It also strongly influences the atmospheres and long-term habitability of orbiting exoplanets. Investigating how this high-energy emission evolves is therefore essential for understanding the evolution of stellar magnetic dynamos and planetary atmospheres and habitability. In this work, we investigate the evolution of X-ray activity and XUV irradiation for a sample of F-M dwarf stars based on Chandra and XMM-Newton observations. We find that F- and G-type stars broadly follow the traditional evolutionary picture of an early saturated (or weakly declining) phase followed by a modest decline, whereas K- and M-type stars exhibit a clear three-phase evolution of a saturated phase, an intermediate phase of rapid decay, and a final modest decline phase. By combining X-ray, ultraviolet, and Ca II H&K bands, we show that coronal emission becomes increasingly dominant toward lower-mass stars. Based on the cumulative XUV emission calculated from our fitted relation, planets around F- and G-type stars experience relatively moderate XUV environments, while those around K- and M-type stars may exceed the empirical cosmic shoreline shortly after reaching the main sequence, though this conclusion depends on the adopted shoreline value.

astro-ph.SR↗

arXiv2026

Cascading Relevance-driven Recommendation Network for CTR Prediction in Trigger-Introduced Recommendation

E-commerce has emerged as crucial platforms for people's daily consumption and shopping interests. There is a new recommendation scenario, Trigger-Introduced Recommendation (TIR), where users click interested product, which is defined as the trigger item, containing their instant interest, and in the undertaking page following the relevant target items. Distinguished from traditional search and recommendation scenarios, trigger contains relatively strong instant interest, which is more vague and implicit compared to search terms. Relying on large amounts of labeled data, existing methods lack the exploration of trigger relevance, which affects users' immersive experience. To alleviate this problem, we propose the Cascading Relevance-driven Recommendation Network (CRRN) to emphasize the interaction and relevance between trigger and target, comprising three essential components: 1) the Trigger-Target Interaction layer extracts interaction features of trigger and target based on personalized gating. 2) Cascading Interest Fusion module explicitly estimates users' trigger intention and fuses instant and personalized interests adaptively with cascading attention blocks. 3) Category-assisted Pairwise Loss enhances trigger relevance with the guidance of category association between trigger and target. Extensive experiment results show that CRRN outperforms recent state-of-the-art methods on both industrial and public datasets. Online A/B tests further validate the effectiveness of our method. Our code is available at https://github.com/a-little-cabbage/CRRN.

cs.IR↗

arXiv2026

Hot water emission during an outburst in a classical T Tauri star

In this paper, we present observations of an eruptive young star in the Rosette Nebula, identified by the Gaia Science Alerts system using Gaia time series data. We aim to investigate the evolution of the brightness and mass accretion rate of V557 Mon throughout its outburst and subsequent decline. In addition, we trace the evolution of the inner accretion disk during the outburst by monitoring molecular emission features. We compiled multi-band photometric time series from Gaia, ZTF, and several 1 m-class ground-based telescopes and obtained optical and near-infrared spectra at multiple epochs covering the outburst and fading phases. Stellar parameters were derived from quiescent colour/spectra and spectral energy distribution (SED) fitting. We also measured the mass accretion rate and fit models to molecular emission bands. Since late 2024, V557 Mon has undergone a year-long outburst consistent with EXor variability. Based on quiescent photometry, V557 Mon has a spectral type of M1 with an extinction of AV = 1.8+_0.3 mag, consistent with a 0.4-0.5 M_sol star at an age of 2 Myr. Our multi-epoch spectra and u-band photometry indicate a peak accretion rate of 6.3x10^(-7)M_sol/yr during the outburst, roughly 70 times higher than in quiescence. We report the detection of hot water vapour emission bands, together with TiO, VO, and CO emission features. Using ExoMol models, we measured the inner-disk temperature changed from 3000 K to 2000 K during the fading phase of the outburst. We report a recent EXor outburst in a low-mass Class II YSO. Our observations reveal the transient formation of a hot molecular inner disk, traced by variable water vapour emission during the EXor event. A positive correlation is found between the molecular excitation temperature and the overall stellar brightness.

astro-ph.SR↗

arXiv2026

Are Hot Jupiters Tidally Disrupted During Stellar Main Sequence?

Once hot Jupiters (HJs) reach their very close orbits, they are expected to experience orbital decay due to tidal interactions with their host star. However, the strength of tidal dissipation is highly uncertain, and it remains an open question whether HJs are tidally disrupted during the stellar main sequence. A previous study found that HJ hosts have a smaller Galactic total velocity dispersion than their field star counterparts, which they interpreted as evidence of tidal disruption. We revisit this study and find that, after using the more reliable vertical velocity dispersion ($σ_W$) as the age indicator and accounting for the heterogeneity and anisotropy of their HJ sample, the kinematic age difference between their HJ hosts and matched field stars is significantly reduced. As an independent check, we collect HJs newly discovered by TESS and find that their $σ_W$ is statistically similar to that of matched field stars. We also find no statistically significant $σ_W$ difference between the field stars and the theoretically vulnerable ultra-hot Jupiters with $P<2$ d. Our results suggest that, after accounting for systematics in the age--velocity dispersion relation, there is no statistically strong evidence from the stellar kinematics that a large fraction of hot Jupiters around Sun-like stars are tidally destroyed during the stellar main sequence.

astro-ph.EP↗

arXiv2026

Completing the Penrose Process without a Horizon

In its standard black-hole realization, the Penrose process uses an event horizon to remove a negative-energy fragment. We show that, at the kinematic level, horizon absorption can instead be replaced by confinement within a compact ergoregion, without imposing an absorbing or reflecting inner boundary. For a broad class of regular, stationary, axisymmetric, asymptotically flat horizonless spacetimes, we prove that every smooth connected component of the spatial ergosurface is a compact torus, independently of the field equations and matter content. Future-directed geodesics with negative Killing energy encounter a forbidden neighborhood of each such boundary component and are therefore confined. We derive an exterior no-barrier condition and identify an open set of on-shell, future-directed, four-momentum-conserving splittings producing both a confined negative-energy fragment and an amplified partner. Under equatorial reflection symmetry, once the latter enters the exterior channel on an outward branch, it necessarily reaches infinity. A rotating boson star explicitly realizes the complete process without a horizon.

gr-qc↗

arXiv2026

Impact of Interacting Dark Energy on the Growth of Matter Density Perturbations: Observational Constraints from DESI and Multi-Probe Data

We investigate the impact of a non-gravitational dark sector interaction on the growth of matter density perturbations within both the interacting $w$CDM and the dynamical Chevallier-Polarski-Linder (CPL) scenarios. For $w$CDM model, we develop a parameterization for the growth rate based on a second-order approximation for the growth index $γ$ that explicitly includes the coupling constant $α$. Our analysis reveals a theoretical degeneracy: the coupling induces a correction $Δγ\simeq 1.1α$ in both models, allowing an interacting dark energy model to mimic the growth index predicted by certain modified gravity theories. Then, we confront the models with the latest multi-probe observations, including the Pantheon+ sample of Type Ia supernovae, Baryon Acoustic Oscillation (BAO) data from the Sloan Digital Sky Survey (SDSS) and the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI), Cosmic Microwave Background (CMB) measurements, Hubble parameter $H(z)$ data, and redshift-space distortion (RSD) measurements. Our analysis finds that the coupling constant is consistent with zero at approximately the $3σ$ and $2σ$ confidence levels for $w$CDM and CPL models, respectively, showing no definitive statistical evidence for a departure from the standard $Λ$CDM cosmology. The observational constraints strongly disfavor the region of parameter space where interacting dark energy can mimic modified gravity, restricting the growth index to a common approximate interval of $0.53 \lesssim γ\lesssim 0.60$ for both models. This reinforces the growth index as a robust diagnostic for distinguishing between a non-minimal interaction in the dark sector and a genuine modification of gravity with current data.

astro-ph.CO↗

arXiv2026

Generalization from Low- to Moderate-Resolution Spectra with Neural Networks for Stellar Parameter Estimation: A Case Study with DESI

Cross-survey generalization is a critical challenge in stellar spectral analysis, particularly in cases such as transferring from low- to moderate-resolution surveys. We investigate this problem using pre-trained models, focusing on simple neural networks such as multilayer perceptrons (MLPs), with a case study transferring from LAMOST low-resolution spectra (LRS) to DESI medium-resolution spectra (MRS). Specifically, we pre-train MLPs on either LRS or their embeddings and fine-tune them for application to DESI stellar spectra. We compare MLPs trained directly on spectra with those trained on embeddings derived from transformer-based models (self-supervised foundation models pre-trained for multiple downstream tasks). We also evaluate different fine-tuning strategies, including residual-head fine-tuning, LoRA, and full fine-tuning. We find that MLPs pre-trained on LAMOST LRS achieve strong performance, even without fine-tuning, and that modest fine-tuning with DESI spectra further improves the results. For iron abundance, embeddings from a transformer-based model yield advantages in the metal-rich ([Fe/H] > -1.0) regime, but underperform in the metal-poor regime compared to MLPs trained directly on LRS. We also show that the optimal fine-tuning strategy depends on the specific stellar parameter under consideration. These results highlight that simple pre-trained MLPs can provide competitive cross-survey generalization, while the role of spectral foundation models for cross-survey stellar parameter estimation requires further exploration.

astro-ph.SR↗

arXiv2026

When Good Equations Get Bad Scores: Improving Symbolic Regression Through Better Parameter Optimization

Symbolic Regression (SR) plays a central role in scientific knowledge discovery by distilling mathematical equations from observational data. Most existing SR methods function within a bi-level optimization framework: an outer loop that searches for the discrete equation structure, and an inner loop that optimizes the continuous parameters of that structure. Crucially, parameter-fitting quality directly determines a structure's score and thus the outer-loop search. However, nonlinear operators make the inner loop highly non-convex, and budget-driven reliance on fast local solvers (e.g., BFGS) often yields poor local minima and underestimated scores for correct structures. This ``Good Structure, Bad Score'' phenomenon becomes a key bottleneck, degrading efficiency and misguiding the search away from the true equation. To resolve this, we propose SAGE-Fit (Structure-Aware and Semantics-Guided Evaluator for Symbolic Regression), an SR-native fitting framework that exploits the dual native priors of symbolic expressions. By capitalizing on the structural and semantic priors unique to SR, we design tailored modules for each property, thereby effectively mitigating this optimization bottleneck. Extensive experiments demonstrate that our approach, as a plug-and-play module, significantly enhances evaluation fidelity and universally improves the performance of various SR systems.

cs.LG↗

arXiv2026

Disentangling the Morphology of Palomar~5: Effects of the Bar, Spiral Arms, LMC, and Halo Flattening

The Palomar~5 (Pal~5) globular cluster and its tidal tails provide a sensitive probe of globular-cluster evolution in the time-dependent Milky Way potential. We study the past 3~Gyr evolution of Pal~5 using collisional direct $N$-body simulations with \texttt{PeTar}, adopting Galactic potential models that include spiral arms, the Galactic bar, halo flattening, the Large Magellanic Cloud (LMC), and bar deceleration. We find that halo shape strongly influences the projected stream track, reflecting Pal~5's sensitivity to Galactic force-field flattening. The LMC causes only modest direct changes to the present-day projected stream morphology, but can alter the pericentric distance and hence the progenitor's mass evolution. The Galactic bar strongly affects stream length and debris redistribution along the tails, producing model-dependent density structures and leading--trailing asymmetries. Comparison with observations from Erkal et al. (2017) and Xiao et al. (2025) shows that no single model simultaneously reproduces all observed properties of Pal~5, including cluster evolution, stream length, track, width, and line-density profile. Although our simulations capture several global properties, the remaining discrepancies indicate that a more precise match likely requires better constraints on the initial properties of the Pal~5 progenitor and a more complex Galactic potential, including perturbations from small-scale perturbers such as dark matter subhalos and giant molecular clouds. Future work may combine self-consistent direct $N$-body simulations with particle-spray methods to investigate these discrepancies more efficiently.

astro-ph.GA↗

arXiv2026

Cheaper is Better: A Discount-Aware Network for Conversion Rate Prediction in E-commerce Recommendation System

Post-click conversion rate (CVR) is a crucial element in online recommendation systems, which addresses significant challenges such as data sparsity (DS), sample selection bias (SSB), and delayed feedback. However, the impact of item discount rate-a key factor influencing both pricing and user purchasing behavior, has received limited attention. In this paper, we introduce the Discount-Aware Network (DANet) to model the relationship between item discount rates and CVR. DANet comprises three main components: 1) a time-frequency transformation module that utilizes Fourier transform to derive the frequency spectrum and capture the long-term discount rate trends of items; 2) a distribution de-bias module designed to mitigate the biases in user-specific discount rates caused by various purchase combinations and promotional activities, as well as periodic deviations linked to different promotion periods on e-commerce platforms; and 3) a supervised regression auxiliary task that establishes the explicit item discount labels to enhance the model's performance in terms of value accuracy, facilitating an effective representation of item discount rates. Experimental results on real datasets demonstrate the superiority of DANet, with offline AUC improving by 1.61%, and online A/B test also shows that DANet achieves impressive gains of 3.63% on pCVR and 2.23% on GMV. DANet has been successfully deployed on Alibaba Tmall APP. The code is available at https://github.com/tangrc/DANet.

cs.IR↗

arXiv2026

Learning to Forget: Satiation-Aware Long-Sequence Transducers for Mitigating Post-Purchase Redundancy

Sequential recommendation models predominantly interpret user interactions as positive signals for preference accumulation. However, in e-commerce scenarios, a purchase action often signifies the termination of a specific intent ("Interest Exit") rather than its continuation. Existing models overlook this distinction, suffering from Action-Intent Asymmetry, which leads to severe post-purchase redundancy. In this paper, we propose the Satiation-Aware Mechanism (SAM), an end-to-end framework designed to explicitly model the lifecycle of user interests. SAM incorporates three key components: (1) A Dual-path Cross-Attention architecture that retroactively suppresses historical clicks associated with a fulfilled intent while simultaneously retrieving personalized replenishment rhythms from long-term purchase history; (2) An Adaptive Satiation Gating Unit (ASGU) that generates a time-sensitive soft mask to inhibit satisfied interests immediately after purchase and gradually "re-awaken" them as the predicted repurchase cycle approaches; and (3) A self-supervised Time-to-Next-Purchase (TTNP) auxiliary task to learn latent product lifecycles without manual annotation. Extensive offline experiments on industrial datasets and online A/B testing demonstrate that SAM significantly reduces the Post-Purchase Repeat Rate (PPRR) by over 60%.

cs.IR↗

arXiv2026

High-Fidelity One-Step Generative Visuomotor Policy via Recursive Correction, Frequency Consistency, and Contrastive Flow Matching

Generative models such as diffusion and flow matching have advanced robotic visuomotor policies by modeling multimodal action distributions, but their multi-step sampling or ODE solving introduces inference latency. Existing one-step acceleration methods often compress the whole generation process into a single large update, leading to spatial deviation, frequency distortion, and mode averaging. This paper proposes a high-fidelity one-step generative visuomotor policy framework that addresses these issues with three complementary mechanisms. Recursive Consistent Action Flow (RCAF) uses recursive correction to compensate for spatial truncation errors and align one-step predictions with refined flow trajectories. Dual-Timestep Frequency Consistency (DTFC) preserves high-frequency manipulation details through adaptive spectral consistency across flow timesteps. Contrastive Flow Matching (CFM) separates entangled action flows with a margin-based repulsive objective, reducing ambiguous actions in multimodal manipulation. Experiments on RoboTwin, RoboTwin 2.0, Adroit, DexArt, and real-world robot platforms show that the proposed method achieves competitive or superior performance compared with strong 10-step generative policy baselines while requiring only one forward pass (1 NFE), enabling low-latency visuomotor control.

cs.RO↗