arXiv subjects

Tuan Do

Publications and source records attributed to Tuan Do.

At least 19 recordsLinked to original sources

arXiv2026

Absolute Motion of the Infrared Counterpart to Sagittarius A* in the Gaia Celestial Reference Frame 3 and Limits on an Intermediate-mass Black Hole Companion

We report the first proper motion and acceleration measurements of the infrared (IR) counterpart to Sagittarius A* (Sgr A*-IR), the supermassive black hole (SMBH) at the center of our Galaxy, in the Gaia-Celestial Reference Frame (Gaia-CRF3). This reference frame realizes the International Celestial Reference System (ICRS), which is an absolute reference coordinate system defined by quasars. A combination of Gaia and Hubble Space Telescope data was used to transform Keck adaptive optics (AO) observations into Gaia-CRF3. We developed a method for selecting reference stars that minimizes astrometric transformation errors (statistical error = $0.10-0.63$ mas) and drift of the coordinate system (systematic error $\sim 0.01$ mas/yr). We find the proper motion of Sgr A*-IR in Gaia-CRF3 to be $\mu_{\alpha^{*}} = -3.093 \pm 0.085$ mas yr$^{-1}$ and $\mu_{\delta}= -5.62 \pm 0.13$ mas yr$^{-1}$ with the initial position at $t_{0} = 2016.0$ of R.A. = 266.41680848 $\pm$ 0.00000029 deg and DEC = -29.00783947 $\pm$ 0.00000050 deg, which translates to a precision of 1.05 mas in R.A. and 1.79 mas DEC. This is consistent with the astrometric measurements of the radio counterpart to Sgr A* by \citet{Xu_2022}. We also place a $2\sigma$ upper constraint of the acceleration of Sgr A*-IR on the sky at 0.061 mas yr$^{-2}$. This acceleration limit on Sgr A*-IR excludes any intermediate-mass black hole companion with mass $\gtrsim 4\times 10^{4}$ $M_{\odot}$ within a distance of $\sim$0.01 pc, consistent with previous studies. With the release of Gaia Data Release 4, we predict these limits will be improved by at least a factor of two.

astro-ph.GA↗

arXiv2026

AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups

Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows. However, using proprietary commercial AI systems raises concerns about transparency, reproducibility and privacy, which are essential for scientific practices. To this end, AquiLLM was developed as an open-source modular RAG-LLM framework using open-weight models, designed to support research groups in capturing tacit knowledge. In this work, we present a series of architectural improvements and feature enhancements to AquiLLM, including local embedding and reranking, multimodal capabilities, OpenAI-compatible inference interfaces, user interface improvements, semantic and episodic memory capabilities, and skills support. These enhancements were informed by discussions with domain experts, including astrophysicists and environmental researchers, and represent a step toward AI systems more closely aligned with scientific research practices.

cs.AI↗

arXiv2026

Field Codes for Distributed Coupling Samplers and Certified Empirical Transport

In this paper, we formulate three communication tasks for empirical optimal transport: distributed coupling sampling, cost-evaluable coupling output, and scalar value-certified sampling. Our main result is a field-code compiler: any communicated transport field approximating an optimal empirical Monge map to error $\eta$ can be completed by sparse target-cell residuals into an exact-marginal value-certified sampler with scalar certificate $W_1(\mu,\nu)\leq U\leq W_1(\mu,\nu)+2\Delta$, where $\Delta$ is the public target-partition diameter. The certificate accuracy is controlled by $\Delta$ alone. The field error $\eta$ controls residual communication under a cell-margin condition; without a margin, $\eta$ alone does not bound residuals. We instantiate the compiler via adaptive local-affine and tensor-product spline codes with $d(m+1)^db$ field bits in the spline case, plus residual lists charged separately. For lower bounds, exact Gap-Hamming embeddings prove certified output is hard, including a smooth cell-packing diffeomorphism family requiring $\Omega(\varepsilon^{-2d/(d+4)})$ communication for any cost-evaluable, cost-certified, or value-certified protocol. The same gadgets admit zero-communication samplers, formally separating the sampler and certificate-bearing output models. These results identify the transport field as the right communicated object whenever a field code is available, primarily as a residual-sparsity tool.

cs.CC↗

arXiv2026

A Kinematic Study of Wolf-Rayet Stars at the Galactic Center I: Binary Candidates and Constraints on the Binary Fraction

We report the binary fraction of Wolf-Rayet (WR) stars within 0.5~pc of the Galactic center obtained through the longest time-baseline (1994-2024) kinematic study of this population of stars. The new radial velocity ($v_{z}$) data we present is primarily from the W. M. Keck Observatory, with additional $v_{z}$ measurements from Gemini North Observatory. When combining our new $v_{z}$ measurements with literature measurements, we find $v_{z}$ variations suggesting the presence of a companion for five out of 27 WR stars, of which two are newly identified here (IRS~13E4, S8-181), along with three previously detected binaries (IRS~16SW, IRS~16NE, S4-258). Based on our experimental sensitivity and expected properties of the underlying population, we infer the binary fraction of the WR stars in the Galactic center to be 0.56$\pm$0.18. This is consistent with previous photometric studies of the young stars in the Galactic center, and with the binary fraction of field WR stars. When our results are combined with the results of previous photometric work, we find a binary fraction of 0.69$\pm$0.17 for the WR stars in the Galactic center.

astro-ph.GA↗

arXiv2026

Beyond Task Success: Behavioral and Representational Diagnostics for WAM and VLA

Vision-language-action (VLA) policies and World-Action Models (WAM) represent two increasingly important paradigms for robotic manipulation. However, it remains unclear whether future prediction in WAMs leads to behaviorally meaningful improvements beyond final task success. In this paper, we ask whether WAMs merely add future prediction, or whether they change robot behavior and internal representations in ways that are actionable for control. We introduce a model-agnostic diagnostic framework that compares WAMs and VLAs through two complementary lenses: behavioral rollout analysis and sparse-autoencoder-based feature analysis. The behavioral protocol measures action dynamics consistency, target-object progress, distractor disturbance, and runtime cost. The feature-space protocol characterizes internal representations as memorized, reactive, or predictive, revealing whether models encode future-oriented structure. Across LIBERO and RoboTwin2.0, we evaluate 7 policies spanning direct VLAs and joint, sequential, and auxiliary WAMs. Our results show that success alone hides key differences: WAMs often improve object-level behavior and target selectivity, but their gains depend on architecture and incur higher inference cost. Sequential WAMs show the clearest predictive structure, while auxiliary and joint WAMs respectively compress or entangle future information. These findings suggest future directions for WAMs design to preserve behaviorally actionable future representations for efficient manipulation.

cs.RO↗

arXiv2026

EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection

Recent advances in foundation video generators such as Sora2, Veo3, and other commercial systems have produced highly realistic synthetic videos, exposing the limitations of existing detection methods that rely on shallow embedding trajectories, image-based adaptation, or computationally heavy MLLMs. We propose EA-Swin, an Embedding-Agnostic Swin Transformer that models spatiotemporal dependencies directly on pretrained video embeddings via a factorized windowed attention design, making it compatible with generic ViT-style patch-based encoders. Moreover, we construct the EA-Video dataset, a benchmark dataset comprising 130K videos that integrates newly collected samples with curated existing datasets, covering diverse commercial and open-source generators and including unseen-generator splits for rigorous cross-distribution evaluation. Extensive experiments show that EA-Swin achieves 0.97-0.99 accuracy across major generators, outperforming prior SoTA methods (typically 0.8-0.9) by a margin of 5-20\%, while maintaining strong generalization to unseen distributions, establishing a scalable and robust solution for modern AI-generated video detection.

cs.CV↗

arXiv2026

IRS 9: The Case for a Dynamically-Ejected Star from the Galactic Center

Measuring stellar motions at the Milky Way's Galactic center (GC) provides unique insight into the dynamical processes within galactic nuclei. We present proper motion measurements for 23 SiO-maser emitting stars within 45'' of SgrA*, including four previously reported to have velocities exceeding their local escape velocities (i.e., they are "locally unbound" from the GC). Derived from 14 epochs of HST WFC3-IR observations (2010 - 2023), our measurements have a median precision of 0.038 mas/yr - up to ~100x more precise then previous constraints for some sources. By combining these proper motions with published radial velocities, we derive updated 3D velocities for the masers and find that only one is locally unbound (IRS 9; v3d = 370 +/- 1.2 km/s). Orbit integrations place the first constraints on the orbit of IRS 9, which is bound to the GC at larger radii with r_peri >= 0.100 +/- 0.005 pc and r_apo >= 5.25 +/- 0.18 pc. IRS 9's high velocity relative to stars at similar radii in the Nuclear Star Cluster makes it a candidate to have experienced a strong dynamical interaction in order to place it on its orbit. We explore the Hills mechanism as a possible origin, but binary evaporation and ejection velocity limits indicate that IRS 9 is unlikely to have experienced such an event in the past 0.4 Myr (the timescale constrained by the orbit integrations). Alternative mechanisms that could produce IRS 9 include binary supernova disruption, two-body interactions, and stellar collisions. Identifying additional stars like IRS 9 will be essential for understanding these various dynamical processes.

astro-ph.GA↗

arXiv2026

Improving Generalization and Uncertainty Quantification of Photometric Redshift Models

Accurate redshift estimates are a vital component in understanding galaxy evolution and precision cosmology. In this paper, we explore approaches to increase the applicability of machine learning models for photometric redshift estimation on a broader range of galaxy types. Typical models are trained with ground-truth redshifts from spectroscopy. We test the utility and effectiveness of two approaches for combining spectroscopic redshifts and redshifts derived from multiband ($\sim$35 filters) photometry, which sample different types of galaxies compared to spectroscopic surveys. The two approaches are (1) training on a composite dataset and (2) transfer learning from one dataset to another. We compile photometric redshifts from the COSMOS2020 catalog (TransferZ) to complement an established spectroscopic redshift dataset (GalaxiesML). We used two architectures, deterministic neural networks (NN) and Bayesian neural networks (BNN), to examine and evaluate their performance with respect to the Legacy Survey of Space and Time (LSST) photo-$z$ science requirements. We also use split conformal prediction for calibrating uncertainty estimates and producing prediction intervals for the BNN and NN, respectively. We find that a NN trained on a composite dataset predicts photo-$z$'s that are 4.5 times less biased within the redshift range $0.3<z<1.5$, 1.1 times less scattered, and has a 1.4 times lower outlier rate than a model trained on only spectroscopic ground truths. We also find that BNNs produce reliable uncertainty estimates, but are sensitive to the different ground truths. This investigation leverages different sources of ground truths to develop models that can accurately predict photo-$z$'s for a broader population of galaxies crucial for surveys such as Euclid and LSST.

astro-ph.IM↗

arXiv2026

Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction

In this work, we demonstrate how Low-Rank Adaptation (LoRA) can be used to combine different galaxy imaging datasets to improve redshift estimation with CNN models for cosmology. LoRA is an established technique for large language models that adds adapter networks to adjust model weights and biases to efficiently fine-tune large base models without retraining. We train a base model using a photometric redshift ground truth dataset, which contains broad galaxy types but is less accurate. We then fine-tune using LoRA on a spectroscopic redshift ground truth dataset. These redshifts are more accurate but limited to bright galaxies and take orders of magnitude more time to obtain, so are less available for large surveys. Ideally, the combination of the two datasets would yield more accurate models that generalize well. The LoRA model performs better than a traditional transfer learning method, with $\sim2.5\times$ less bias and $\sim$2.2$\times$ less scatter. Retraining the model on a combined dataset yields a model that generalizes better than LoRA but at a cost of greater computation time. Our work shows that LoRA is useful for fine-tuning regression models in astrophysics by providing a middle ground between full retraining and no retraining. LoRA shows potential in allowing us to leverage existing pretrained astrophysical models, especially for data sparse tasks.

astro-ph.IM↗

arXiv2025

Photometric Constraints on Intermediate-mass Black Holes in the Galactic Centre

JWST/MIRI observations can place photometric limits on the presence of an intermediate-mass black hole (IMBH) near the Galactic Centre. The stellar complex IRS 13E, a co-moving conglomerate of young and massive stars, is a prime location to study because it has been speculated to be bound by an IMBH. Assuming a standard radiatively inefficient accretion flow (RIAF) and a minimum fractional variability of 10% of intrinsic luminosity, the wavelength of peak emission in the spectral energy distribution for an IMBH would lie in the mid-infrared ($\sim$ 5-25 $\mu$m), and variability would be detectable in MIRI time-series observations. Monitoring fails to detect such variable emission (other than from Sgr A*) in and around the IRS 13E complex, and upper limits on a putative IMBH's intrinsic variability on timescales of minutes to about 1 hour are $\lesssim$1 mJy at 12 $\mu$m and $\lesssim$2 mJy at 19 $\mu$m. These translate to luminosities $\lesssim 25 \times 10^{32}$ erg/s. The resulting limits on the IMBH mass and accretion rate rule out any IMBH with mass $\gtrsim 10^3$ M$_\odot$ accreting at $\gtrsim 10^{-6}$ times Eddington rate at the location of IRS 13E. Further, the observations rule out an IMBH anywhere in the central 6" $\times$ 6" region that is more massive than $\approx$ 2 $\times 10^3$ M$_\odot$ and accreting at $\gtrsim 10^{-6}$ of the Eddington rate. Assuming Bondi accretion scaled to typical RIAF-accretion efficiencies, albeit somewhat uncertain, also allows us to rule out IMBHs moving with typical velocities of about 200 km/s and masses $\gtrsim 2 \times 10^3$ M$_\odot$. These methods showcase the effectiveness of photometric variability measurements in constraining the presence of accreting black holes in Galactic centre-like environments.

astro-ph.GA↗

arXiv2025

Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology

Upcoming surveys will produce billions of galaxy images but comparatively few spectra, motivating models that learn cross-modal representations. We build a dataset of 134,533 galaxy images (HSC-PDR2) and spectra (DESI-DR1) and adapt a Multi-Modal Masked Autoencoder (MMAE) to embed both images and spectra in a shared representation. The MMAE is a transformer-based architecture, which we train by masking 75% of the data and reconstructing missing image and spectral tokens. We use this model to test three applications: spectral and image reconstruction from heavily masked data and redshift regression from images alone. It recovers key physical features, such as galaxy shapes, atomic emission line peaks, and broad continuum slopes, though it struggles with fine image details and line strengths. For redshift regression, the MMAE performs comparably or better than prior multi-modal models in terms of prediction scatter even when missing spectra in testing. These results highlight both the potential and limitations of masked autoencoders in astrophysics and motivate extensions to additional modalities, such as text, for foundation models.

astro-ph.IM↗

arXiv2025

AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups

Research groups face persistent challenges in capturing, storing, and retrieving knowledge that is distributed across team members. Although structured data intended for analysis and publication is often well managed, much of a group's collective knowledge remains informal, fragmented, or undocumented--often passed down orally through meetings, mentoring, and day-to-day collaboration. This includes private resources such as emails, meeting notes, training materials, and ad hoc documentation. Together, these reflect the group's tacit knowledge--the informal, experience-based expertise that underlies much of their work. Accessing this knowledge can be difficult, requiring significant time and insider understanding. Retrieval-augmented generation (RAG) systems offer promising solutions by enabling users to query and generate responses grounded in relevant source material. However, most current RAG-LLM systems are oriented toward public documents and overlook the privacy concerns of internal research materials. We introduce AquiLLM (pronounced ah-quill-em), a lightweight, modular RAG system designed to meet the needs of research groups. AquiLLM supports varied document types and configurable privacy settings, enabling more effective access to both formal and informal knowledge within scholarly groups.

cs.IR↗

arXiv2025

Probing Fundamental Constant Oscillation in the Galactic Center with S-Star Spectroscopy

Astrophysical spectroscopy provides a powerful probe of spacetime variations of fundamental constants, as atomic and ionic emission and absorption lines depend sensitively on the fine-structure constant. In particular, coherent temporal oscillations induced by an ultralight scalar background produce characteristic, time-resolved signatures that can be robustly disentangled from intrinsic variability. In the Galactic Center, such scalar backgrounds can be substantially enhanced, either through the formation of dense scalar clouds powered by black hole rotational energy extraction or as ultralight scalar dark matter forming a soliton-like core. These scalar configurations generically induce oscillations of the fine-structure constant, with periods set by the scalar mass and spatial profiles determined by the scalar wavefunction and its coupling to the electromagnetic sector. We show that precise, time-resolved spectroscopy of S-stars orbiting the supermassive black hole Sgr A$^*$ provides a sensitive test of these effects, enabling constraints on quadratic scalar-photon couplings in the exceptionally high boson-density environment of the Galactic Center.

hep-ph↗

arXiv2025

MOSDEF-3D: Keck/OSIRIS Maps of the Ionized ISM in $z \sim 2$ Galaxies

We present spatially-resolved rest-frame optical emission line maps of four galaxies at $z \sim 2$ observed with Keck/OSIRIS to study the physical conditions of the ISM at Cosmic Noon. Our analysis of strong emission line ratios in these galaxies reveals an offset from the local star-forming locus on the BPT diagram, but agrees with other star-forming galaxies at similar redshifts. Despite the offset towards higher [O III]$\lambda5008$/H$\beta$ and [N II]$\lambda6585$/H$\alpha$, these strong-line ratios remain consistent with or below the maximum starburst threshold even in the inner $\sim 1$ kpc region of the galaxies, providing no compelling evidence for central AGN activity. The galaxies also exhibit flat radial gas-phase metallicity gradients, consistent with previous studies of $z \sim 2$ galaxies and suggesting efficient radial mixing possibly driven by strong outflows from intense star formation. Overall, our results reveal the highly star-forming nature of these galaxies, with the potential to launch outflows that flatten metallicity gradients through significant radial gas mixing. Future observations with JWST/NIRSpec are crucial to detect fainter emission lines at higher spatial resolution to further constrain the physical processes and ionization mechanisms that shape the ISM during Cosmic Noon.

astro-ph.GA↗

arXiv2025

The HST-Gaia Near-Infrared Astrometric Reference Frame near the Milky Way Galactic Center

We present the first high-precision proper motion catalog, tied to the International Celestial Reference System (ICRS), of infrared astrometric reference stars within R $\leq$ 25" (1 pc) of the central supermassive black hole at the Galactic center (GC). This catalog contains $\sim$2,900 sources in a highly extinguished region that is inaccessible via Gaia. New astrometric measurements are extracted from HST observations (14 epochs, 2010 - 2023) and transformed into the ICRS using 40 stars in common with Gaia-DR3. We implement a new method for modeling proper motions via Gaussian Processes that accounts for systematic errors, greatly improving measurement accuracy. Proper motion and position measurements reach precisions of $\sim$0.03 mas/yr and $\sim$0.11 mas, respectively, representing a factor of $\sim$20x improvement over previous ICRS proper motion catalogs in the region. These measurements define a novel HST-Gaia reference frame that is consistent with Gaia-CRF3 to within 0.025 mas/yr in proper motion and 0.044 mas in position, making it the first ICRS-based reference frame precise enough to probe the distribution of extended mass within the orbits of stars near SgrA*. In addition, HST-Gaia provides an independent test of the radio measurements of stellar masers that form the basis of current GC reference frames. We find that the HST-Gaia and radio measurements are consistent to within 0.041 mas/yr in proper motion and 0.54 mas in position at 99.7% confidence. Gaia-DR4 is expected to reduce the HST-Gaia reference frame uncertainties by another factor of $\sim$2x, further improving the reference frame for dynamical studies.

astro-ph.GA↗

arXiv2025

First mid-infrared detection and modeling of a flare from Sgr A*

The time-variable emission from the accretion flow of Sgr A*, the supermassive black hole at the Galactic Center, has long been examined in the radio-to-mm, near-infrared (NIR), and X-ray regimes of the electromagnetic spectrum. However, until now, sensitivity and angular resolution have been insufficient in the crucial mid-infrared (MIR) regime. The MIRI instrument on JWST has changed that, and we report the first MIR detection of Sgr A*. The detection was during a flare that lasted about 40 minutes, a duration similar to NIR and X-ray flares, and the source's spectral index steepened as the flare ended. The steepening suggests synchrotron cooling is an important process for Sgr A*'s variability and implies magnetic field strengths $\sim$40--70 Gauss in the emission zone. Observations at $1.3~\mathrm{mm}$ with the Submillimeter Array revealed a counterpart flare lagging the MIR flare by $\approx$10 minutes. The observations can be self-consistently explained as synchrotron radiation from a single population of gradually cooling high-energy electrons accelerated through (a combination of) magnetic reconnection and/or magnetized turbulence.

astro-ph.HE↗

arXiv2024

Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation

In this work, we explore methods to improve galaxy redshift predictions by combining different ground truths. Traditional machine learning models rely on training sets with known spectroscopic redshifts, which are precise but only represent a limited sample of galaxies. To make redshift models more generalizable to the broader galaxy population, we investigate transfer learning and directly combining ground truth redshifts derived from photometry and spectroscopy. We use the COSMOS2020 survey to create a dataset, TransferZ, which includes photometric redshift estimates derived from up to 35 imaging filters using template fitting. This dataset spans a wider range of galaxy types and colors compared to spectroscopic samples, though its redshift estimates are less accurate. We first train a base neural network on TransferZ and then refine it using transfer learning on a dataset of galaxies with more precise spectroscopic redshifts (GalaxiesML). In addition, we train a neural network on a combined dataset of TransferZ and GalaxiesML. Both methods reduce bias by $\sim$ 5x, RMS error by $\sim$ 1.5x, and catastrophic outlier rates by 1.3x on GalaxiesML, compared to a baseline trained only on TransferZ. However, we also find a reduction in performance for RMS and bias when evaluated on TransferZ data. Overall, our results demonstrate these approaches can meet cosmological requirements.

astro-ph.IM↗

arXiv2024

Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models

Redshift measures the distance to galaxies and underlies our understanding of the origin of the Universe and galaxy evolution. Spectroscopic redshift is the gold-standard method for measuring redshift, but it requires about $1000$ times more telescope time than broad-band imaging. That extra cost limits sky coverage and sample size and puts large spectroscopic surveys out of reach. Photometric redshift methods rely on imaging in multiple color filters and template fitting, yet they ignore the wealth of information carried by galaxy shape and structure. We demonstrate that a diffusion model conditioned on continuous redshift learns this missing joint structure, reproduces known morphology-$z$ correlations. We verify on the HyperSuprime-Cam survey, that the model captures redshift-dependent trends in ellipticity, semi-major axis, S\'ersic index, and isophotal area that these generated images correlate closely with true redshifts on test data. To our knowledge this is the first study to establish a direct link between galaxy morphology and redshift. Our approach offers a simple and effective path to redshift estimation from imaging data and will help unlock the full potential of upcoming wide-field surveys.

astro-ph.GA↗