arXiv subjects

Chuan Tian

Publications and source records attributed to Chuan Tian.

16 recordsLinked to original sources

arXiv2026

AGN-DB: A Unified Multi-Wavelength Database of Active Galactic Nuclei

We present the Active Galactic Nuclei Database (AGN-DB), a comprehensive, multi-wavelength catalog compiled from more than 100 publicly available AGN catalogs and samples released by the end of 2025, spanning radio to $\gamma$-ray wavelengths. The database contains approximately 8.1 million unique sources, approximately 7.8 million of which remain after flagging stellar contaminants, and approximately 6.8 million of these are classified as AGN. Source cross-matching across catalogs is performed using Lyra, a Bayesian likelihood-ratio framework that jointly considers positional uncertainties, source densities, and photometric information to compute posterior match probabilities. The resulting catalog provides astrometric coordinates, redshifts, photometry, and classifications for each unique source. All multi-catalog provenance is preserved. For every property, we store the full array of values and originating catalog identifiers, enabling multi-epoch and multi-survey analyses. In this paper, we describe the AGN-DB pipeline, including the cross-matching methodology, and present the statistical properties of the v1.0 catalog. AGN-DB is designed to enable population studies, spectral energy distribution modeling, AGN classification, and variability analyses at an unprecedented scale. Its pipeline is designed to facilitate the integration of new catalogs, allowing AGN-DB to be updated regularly, with releases planned at least annually.

astro-ph.GA↗

arXiv2026

SH-SAW Acousto-Electric Amplifier in Epitaxial InGaAs on Lithium Niobate on Insulator

This work demonstrates shear-horizontal surface acoustic wave (SH-SAW) acoustoelectric (AE) amplification on an epitaxial InGaAs / X-cut lithium niobate on insulator (LNOI) heterostructure formed by Al2O3-mediated wafer bonding. Deployable passivated devices show a stable fundamental-mode non-reciprocity of 32 dB/mm at 1.11 GHz (30 V bias, 64 mW consumed), while unpassivated devices reach 174 dB/mm across 1.1-2.8 GHz, reported as upper bounds. Device characterization establishes the role of mode-dependent K^2 in determining the achievable gain. Hall-effect measurements of the transferred InGaAs serve as a quantitative diagnostic: the extracted carrier density, elevated by unintentional silicon doping during epitaxy, accounts for the absolute AE gain when inserted into the analytical model and identifies epitaxial process control as a clear lever for further enhancement. We further identify ambient oxidation of the bare InGaAs surface as a distinct aging mechanism that extinguishes the AE response within weeks, and show that an InP or ALD Al2O3 passivation layer suppresses it, at the cost of redistributing the piezoelectric field away from the channel. These results establish InGaAs-on-LNOI as a compact, low-power platform for non-reciprocal RF components and acoustoelectric delay lines, with strong relevance to in-band full-duplex (IBFD) transceivers and spectrum-efficient wireless front ends.

physics.app-ph↗

arXiv2026

Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs

Personalizing large language models (LLMs) to individual users requires incorporating extensive interaction histories and profiles, but input token constraints make this impractical due to high inference latency and API costs. Existing approaches rely on heuristic methods such as selecting recent interactions or prompting summarization models to compress user profiles. However, these methods treat context as a monolithic whole and fail to consider how LLMs internally process and prioritize different profile components. We investigate whether LLMs' attention patterns can effectively identify important personalization signals for intelligent context compression. Through preliminary studies on representative personalization tasks, we discover that (a) LLMs' attention patterns naturally reveal important signals, and (b) fine-tuning enhances LLMs' ability to distinguish between relevant and irrelevant information. Based on these insights, we propose Attn-GS, an attention-guided context compression framework that leverages attention feedback from a marking model to mark important personalization sentences, then guides a compression model to generate task-relevant, high-quality compressed user contexts. Extensive experiments demonstrate that Attn-GS significantly outperforms various baselines across different tasks, token limits, and settings, achieving performance close to using full context while reducing token usage by 50 times.

cs.CL↗

arXiv2026

Obscured AGN at z < 1.5: X-ray to Far-Infrared SEDs and Host Galaxy Morphologies in the GOODS Fields

We present an analysis of spectral energy distributions (SEDs), galaxy light profiles, and visual morphological classifications for 194 X-ray luminous AGN (intrinsic absorption-corrected log10 LX(0.5 to 7 keV) less than 42.5, with a maximum of 45.2 ergs per second) at redshift z less than 1.5 in the GOODS fields. We generate X-ray to far-infrared SEDs normalized at 1 micron for all AGN and sort them according to their emission slopes in the ultraviolet and infrared. We visually classify their host galaxy morphologies and compute their bulge-to-total light ratios using the software Galaxy Shapes of Light (galight). Most (94 percent) GOODS AGN exhibit obscured SEDs, defined by diminished ultraviolet and/or mid-infrared emission, while only 6 percent show unobscured, quasar-like SEDs. Secular processes appear to play a large role in stimulating AGN emission, as only around one-third of galaxies are undergoing interactions. We also describe the morphological identification of a population of suspected post-merger spheroid galaxies with obscured ultraviolet and infrared SEDs, and distinguish them from the host galaxies of AGN with less obscuration in the ultraviolet or infrared.

astro-ph.GA↗

arXiv2025

Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within $z < 1.4$ in the Hyper Supreme-Cam Wide Survey

We present a composite machine learning framework to estimate posterior probability distributions of bulge-to-total light ratio, half-light radius, and flux for Active Galactic Nucleus (AGN) host galaxies within $z<1.4$ and $m<23$ in the Hyper Supreme-Cam Wide survey. We divide the data into five redshift bins: low ($0<z<0.25$), mid ($0.25<z<0.5$), high ($0.5<z<0.9$), extra ($0.9<z<1.1$) and extreme ($1.1<z<1.4$), and train our models independently in each bin. We use PSFGAN to decompose the AGN point source light from its host galaxy, and invoke the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters of the recovered host galaxy. We first trained our models on simulated data, and then fine-tuned our algorithm via transfer learning using labeled real data. To create training labels for transfer learning, we used GALFIT to fit $\sim 20,000$ real HSC galaxies in each redshift bin. We comprehensively examined that the predicted values from our final models agree well with the GALFIT values for the vast majority of cases. Our PSFGAN + GaMPEN framework runs at least three orders of magnitude faster than traditional light-profile fitting methods, and can be easily retrained for other morphological parameters or on other datasets with diverse ranges of resolutions, seeing conditions, and signal-to-noise ratios, making it an ideal tool for analyzing AGN host galaxies from large surveys coming soon from the Rubin-LSST, Euclid, and Roman telescopes.

astro-ph.GA↗

arXiv2024

Design of a variable-Mach-number waverider by the osculating-curved-cone method using a rational distribution function and incorporating the equilibrium-gas model

When a waverider flies at hypersonic speed, the thermodynamic properties of the surrounding gas change because of the rapid increase in temperature, so it is reasonable to consider real-gas effects in the vehicle design. In addition, a hypersonic waverider usually travels at varying speed during flight, and deviating from the default speed designed in terms of a constant Mach number often creates difficulties in preserving the expected performance. Therefore, research on the design of variable-Mach-number waveriders considering equilibrium-gas effects is important for applications. In this paper, a design method for a variable-Mach-number osculating-curved-cone waverider (VMOCCW) considering equilibrium-gas effects is introduced, then the influences of different gas models on the waverider design are studied by taking a VMOCCW designed with a linear Mach-number distribution as an example. Furthermore, a new Mach-number distribution method is proposed by using a parameterized rational function, which is combined with different gas models to achieve VMOCCW design. For comparison, waveriders designed with quadratic concave and convex increasing functions are also selected for comparison of their layouts and aerodynamic performances under design and off-design conditions. The results show that waveriders designed with the equilibrium-gas model exhibit differences in geometric features (e.g., volume and volumetric efficiency) and aerodynamic characteristics (e.g., lift-to-drag ratio and pitching moment coefficient) compared to those designed with the ideal-gas model. Specifically, waveriders designed with a rational function for the Ma distribution have a wing-like structure, and overall they have more-balanced geometric and aerodynamic characteristics than those designed with quadratic concave and convex functions.

physics.flu-dyn↗

arXiv2024

Purrfessor: A Fine-tuned Multimodal LLaVA Diet Health Chatbot

This study introduces Purrfessor, an innovative AI chatbot designed to provide personalized dietary guidance through interactive, multimodal engagement. Leveraging the Large Language-and-Vision Assistant (LLaVA) model fine-tuned with food and nutrition data and a human-in-the-loop approach, Purrfessor integrates visual meal analysis with contextual advice to enhance user experience and engagement. We conducted two studies to evaluate the chatbot's performance and user experience: (a) simulation assessments and human validation were conducted to examine the performance of the fine-tuned model; (b) a 2 (Profile: Bot vs. Pet) by 3 (Model: GPT-4 vs. LLaVA vs. Fine-tuned LLaVA) experiment revealed that Purrfessor significantly enhanced users' perceptions of care ($β= 1.59$, $p = 0.04$) and interest ($β= 2.26$, $p = 0.01$) compared to the GPT-4 bot. Additionally, user interviews highlighted the importance of interaction design details, emphasizing the need for responsiveness, personalization, and guidance to improve user engagement.

cs.HC↗

arXiv2024

Collaboration Dynamics and Reliability Challenges of Multi-Agent LLM Systems in Finite Element Analysis

Large Language Model (LLM)-based multi-agent systems are increasingly applied to automate computational workflows in science and engineering. However, how inter-agent dynamics influence reasoning quality and verification reliability remains unclear. We study these mechanisms using an AutoGen-based multi-agent framework for linear-elastic Finite Element Analysis (FEA), evaluating seven role configurations across four tasks under a fixed 12-turn conversation limit. From 1,120 controlled trials, we find that collaboration effectiveness depends more on functional complementarity than team size: the three-agent Coder-Executor-Critic configuration uniquely produced physically and visually correct solutions, while adding redundant reviewers reduced success rates. Yet three systematic failure modes persist: (1) affirmation bias, where the Rebuttal agent endorsed rather than challenged outputs (85-92% agreement, including errors); (2) premature consensus caused by redundant reviewers; and (3) a verification-validation gap where executable but physically incorrect code passed undetected. No agent combination successfully validated constitutive relations in complex tasks. Building on theories of functional diversity, role differentiation, and computational validation, we propose actionable design principles: (i) assign complementary agent roles, (ii) enforce multi-level validation (execution, specification, physics), and (iii) prevent early consensus through adversarial or trigger-based interaction control. These findings establish a principled foundation for designing trustworthy LLM collaborations in engineering workflows.

cs.AI↗

arXiv2024

Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for $\sim 8$ million galaxies in the Hyper Suprime-Cam (HSC) Wide survey with $z \leq 0.75$ and $m \leq 23$. GaMPEN is a machine learning framework that estimates Bayesian posteriors for a galaxy's bulge-to-total light ratio ($L_B/L_T$), effective radius ($R_e$), and flux ($F$). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with $<1\%$ of our dataset. This two-step process will be critical for applying machine learning algorithms to future large imaging surveys, such as the Rubin-Legacy Survey of Space and Time (LSST), the Nancy Grace Roman Space Telescope (NGRST), and Euclid. By comparing our results to those obtained using light-profile fitting, we demonstrate that GaMPEN's predicted posterior distributions are well-calibrated ($\lesssim 5\%$ deviation) and accurate. This represents a significant improvement over light profile fitting algorithms which underestimate uncertainties by as much as $\sim60\%$. For an overlapping sub-sample, we also compare the derived morphological parameters with values in two external catalogs and find that the results agree within the limits of uncertainties predicted by GaMPEN. This step also permits us to define an empirical relationship between the Sérsic index and $L_B/L_T$ that can be used to convert between these two parameters. The catalog presented here represents a significant improvement in size ($\sim10 \times $), depth ($\sim4$ magnitudes), and uncertainty quantification over previous state-of-the-art bulge+disk decomposition catalogs. With this work, we also release GaMPEN's source code and trained models, which can be adapted to other datasets.

astro-ph.GA↗

arXiv2022

Using Machine Learning to Determine Morphologies of $z<1$ AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey

We present a machine-learning framework to accurately characterize morphologies of Active Galactic Nucleus (AGN) host galaxies within $z<1$. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low $(0<z<0.25)$, medium $(0.25<z<0.5)$, and high $(0.5<z<1.0)$. By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for $\sim$ $60\%-70\%$ host galaxies from test sets, with a classification precision of $\sim$ $80\%-95\%$, depending on redshift bin. Specifically, our models achieve disk precision of $96\%/82\%/79\%$ and bulge precision of $90\%/90\%/80\%$ (for the 3 redshift bins), at thresholds corresponding to indeterminate fractions of $30\%/43\%/42\%$. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNet framework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging survey.

astro-ph.GA↗

arXiv2022

On the cosmic evolution of AGN obscuration and the X-ray luminosity function: XMM-Newton and Chandra spectral analysis of the 31.3 deg$^2$ Stripe 82X

We present X-ray spectral analysis of XMM and Chandra observations in the 31.3 deg$^2$ Stripe-82X (S82X) field. Of the 6181 X-ray sources in this field, we analyze a sample of 2937 active galactic nuclei (AGN) with solid redshifts and sufficient counts determined by simulations. Our results show a population with median values of spectral index $Γ=1.94_{-0.39}^{+0.31}$, column density log$\,N_{\mathrm{H}}/\mathrm{cm}^{-2}=20.7_{-0.5}^{+1.2}$ and intrinsic, de-absorbed, 2-10 keV luminosity log$\,L_{\mathrm{X}}/\mathrm{erg\,s}^{-1}=44.0_{-1.0}^{+0.7}$, in the redshift range 0-4. We derive the intrinsic fraction of AGN that are obscured ($22\leq\mathrm{log}\,N_{\mathrm{H}}/\mathrm{cm}^{-2}<24$), finding a significant increase in the obscured AGN fraction with redshift and a decline with increasing luminosity. The average obscured AGN fraction is $57\pm4\%$ for log$\,L_{\mathrm{X}}/\mathrm{erg\,s}^{-1}>43$. This work constrains the AGN obscuration and spectral shape of the still uncertain high-luminosity and high-redshift regimes (log$\,L_{\mathrm{X}}/\mathrm{erg\,s}^{-1}>45.5$, $z>3$), where the obscured AGN fraction rises to $64\pm12\%$. We report a luminosity and density evolution of the X-ray luminosity function, with obscured AGN dominating at all luminosities at $z>2$ and unobscured sources prevailing at log$\,L_{\mathrm{X}}/\mathrm{erg\,s}^{-1}>45$ at lower redshifts. Our results agree with evolutionary models in which the bulk of AGN activity is triggered by gas-rich environments and in a downsizing scenario. Also, the black hole accretion density (BHAD) is found to evolve similarly to the star formation rate density, confirming the co-evolution between AGN and host-galaxy, but suggesting different time scales in their growing history. The derived BHAD evolution shows that Compton-thick AGN contribute to the accretion history of AGN as much as all other AGN populations combined.

astro-ph.GA↗

arXiv2022

GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters

We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy's bulge-to-total light ratio ($L_B/L_T$), effective radius ($R_e$), and flux ($F$). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a Spatial Transformer Network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to match $z < 0.25$ galaxies in Hyper Suprime-Cam Wide $g$-band images, we demonstrate that GaMPEN achieves typical errors of $0.1$ in $L_B/L_T$, $0.17$ arcsec ($\sim 7\%$) in $R_e$, and $6.3\times10^4$ nJy ($\sim 1\%$) in $F$. GaMPEN's predicted uncertainties are well-calibrated and accurate ($<5\%$ deviation) -- for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We also demonstrate that we can apply categorical labels (i.e., classifications such as "highly bulge-dominated") to predictions in regions with high residuals and verify that those labels are $\gtrsim 97\%$ accurate. To the best of our knowledge, GaMPEN is the first machine learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN to optical imaging in astronomy.

astro-ph.GA↗

arXiv2022

Reference-Invariant Inverse Covariance Estimation with Application to Microbial Network Recovery

The interactions between microbial taxa in microbiome data has been under great research interest in the science community. In particular, several methods such as SPIEC-EASI, gCoda, and CD-trace have been proposed to model the conditional dependency between microbial taxa, in order to eliminate the detection of spurious correlations. However, all those methods are built upon the central log-ratio (CLR) transformation, which results in a degenerate covariance matrix and thus an undefined inverse covariance matrix as the estimation of the underlying network. Jiang et al. (2021) and Tian et al. (2022) proposed bias-corrected graphical lasso and compositional graphical lasso based on the additive log-ratio (ALR) transformation, which first selects a reference taxon and then computes the log ratios of the abundances of all the other taxa with respect to that of the reference. One concern of the ALR transformation would be the invariance of the estimated network with respect to the choice of reference. In this paper, we first establish the reference-invariance property of a subnetwork of interest based on the ALR transformed data. Then, we propose a reference-invariant version of the compositional graphical lasso by modifying the penalty in its objective function, penalizing only the invariant subnetwork. We validate the reference-invariance property of the proposed method under a variety of simulation scenarios as well as through the application to an oceanic microbiome data set.

stat.ME↗

arXiv2022

Compositional Graphical Lasso Resolves the Impact of Parasitic Infection on Gut Microbial Interaction Networks in a Zebrafish Model

Understanding how microbes interact with each other is key to revealing the underlying role that microorganisms play in the host or environment and to identifying microorganisms as an agent that can potentially alter the host or environment. For example, understanding how the microbial interactions associate with parasitic infection can help resolve potential drug or diagnostic test for parasitic infection. To unravel the microbial interactions, existing tools often rely on graphical models to infer the conditional dependence of microbial abundances to represent their interactions. However, current methods do not simultaneously account for the discreteness, compositionality, and heterogeneity inherent to microbiome data. Thus, we build a new approach called "compositional graphical lasso" upon existing tools by incorporating the above characteristics into the graphical model explicitly. We illustrate the advantage of compositional graphical lasso over current methods under a variety of simulation scenarios and on a benchmark study, the Tara Oceans Project. Moreover, we present our results from the analysis of a dataset from the Zebrafish Parasite Infection Study. Our approach identifies changes in interaction degree between infected and uninfected individuals for three taxa, Photobacterium, Gemmobacter, and Paucibacter, which are inversely predicted by other methods. Further investigation of these method-specific taxa interaction changes reveals their biological plausibility. In particular, we speculate on the potential pathobiotic roles of Photobacterium and Gemmobacter in the zebrafish gut, and the potential probiotic role of Paucibacter. Collectively, our analyses demonstrate that compositional graphical lasso provides a powerful means of accurately resolving interactions between microbiota and can thus drive novel biological discovery.

stat.AP↗

arXiv2022

Relaxation Oscillations of an Exciton-polariton Condensate Driven by Parametric Scattering

We report observation of coherent oscillations in the relaxation dynamics of an exciton-polariton condensate driven by parametric scattering processes. As a result of the interbranch scattering scheme and the nonlinear polariton-polariton interactions, such parametric scatterings exhibit high scattering efficiency, which leads to fast depletion of the polariton condensate and periodic shut-off of the bosonic stimulation processes, eventually causing relaxation oscillations. Employing polariton-reservoir interactions, the oscillation dynamics in the time domain can be projected onto the energy space. In theory, our simulations using the open-dissipative Gross-Pitaevskii equation are in excellent agreement with experimental observations. Surprisingly, the oscillation patterns are clearly visible in our time-integrated images including many excitation pulses, implying the high stability of the relaxation oscillations driven by polariton parametric scatterings.

physics.optics↗

arXiv2009

Magnetic structure and ferroelectric polarization of MnWO4 investigated by density functional calculations and classical spin analysis

The ordered magnetic states of MnWO4 at low temperatures were examined by evaluating the spin exchange interactions between the Mn2+ ions of MnWO4 on the basis of first principles density functional calculations and by performing classical spin analysis with the resulting spin exchange parameters. Our work shows that the spin exchange interactions are frustrated within each zigzag chain of Mn2+ ions along the c-direction and between such chains of Mn2+ ions along the a-direction. This explains the occurrence of a spiral-spin order along the c- and a-directions in the incommensurate magnetic state AF2, and that of a uudd spin order along the c- and a-directions in the commensurate magnetic state AF1. The ferroelectric polarization of MnWO4 in the spiral-spin state AF2 was examined by performing Berry phase calculations for a model superstructure to find that the ferroelectric polarization occurs along the b-direction, in agreement with experiment.

cond-mat.mtrl-sci↗