arXiv subjects
Publications and source records attributed to Jiang Chang.
We compute Polak's bound $A_4(19,6,8)$ for binary constant weight codes, obtaining the numerical value $673.04001$. An exact rational certificate gives $A_4(19,6,8)\le 673.0400143064802474039375132665301315202504$, and hence $A(19,6,8)\le 673$. This improves the upper bound $693$ listed in Brouwer's table. The certificate is verified by a separate program using exact rational arithmetic.
Unveiling the properties of the progenitors that formed the tidal structures of the Milky Way (MW) is essential for exploring the MW's assembly history. Such studies further afford a unique opportunity to dissect the formation and evolution of dwarf galaxies by contrasting surviving and disrupted systems. However, reconstructing the intrinsic structure of progenitors solely from the positions and velocities of their tidal debris remains a formidable challenge. Here, we decode the chemo-kinematic signatures embedded in the stellar streams of the Sagittarius (Sgr) dwarf galaxy through N-body simulations. Our simulation reproduces a vertical metallicity gradient in the Sgr streams. We show that this feature is naturally explained if the stream originated from a disk component with a $\wedge$-like metallicity profile. Remarkably, similar metallicity profiles are identified in MaNGA survey data for a few (3/217) field dwarf galaxies, suggesting this feature is rare but not unique to Sgr. The vertical metallicity gradient of stellar streams preserves extra detailed galactic fossil records, enabling quantitative reconstruction of the peculiar structure of the satellite galaxy. The similar metallicity profiles of Sgr and MW raise the question of a possible special coevolution.
We investigated disk reorientation and its relationship to mergers in a sample of 93 Milky Way-mass galaxies drawn from the TNG50 simulations. From this sample, we identified 117 individual disk-tilt events with clearly defined start and end times, and we divided them into two classes based on whether the disk evolves smoothly or undergoes rapid disruption and regrowth during the tilt duration. For each tilt event, we linked the most massive merger that occurs in close temporal association with it. We focus on those tilt events occurring during phases of smooth disk evolution, which typically show tilt rates below 10 degrees per Gyr, persist for 2-9 Gyr, and are generally connected to minor mergers. Roughly 40% of these events reach tilt angles exceeding 30 degrees, most often triggered by satellites whose masses are comparable to or less massive than the galaxy's total stellar mass, merging on tangential orbits with orbital angular momentum that is retrograde or nearly perpendicular to the disk spin. The satellite consistently drives the disk tilt toward alignment with the satellite's orbital spin; satellites that came in on retrograde orbits relative to the disk spin can thus induce large tilt angles and can produce an almost orthogonal configuration between the stellar disk and the satellite's orbital plane as an intermediate phase. Owing to the comparatively slow tilt rates, the stellar disk can keep stable and continue to grow throughout the reorientation process. Consequently, galaxies that experience strong disk tilts can still maintain stellar disks that are as thin as those in systems without significant reorientation.
We examine whether the unusual spatial flattening and orbital-plane coherence of the Milky Way's classical satellites are connected to the Galaxy's location in the cosmic web. We compare the 11 classical satellites with the 11 most massive satellites of 11,922 Milky Way-mass hosts in the TNG300-1 hydrodynamical simulation. We measure flattening with both the simple inertia tensor (SIT), which retains radial information, and the reduced inertia tensor (RIT), which isolates angular anisotropy. Orbital-plane coherence is characterized by the minimum opening angle containing the axes of the eight most coplanar satellite orbits. For the Milky Way, we find $(c/a,b/a)=(0.183,0.558)$ for SIT and $(0.364,0.736)$ for RIT, together with an eight-orbit opening angle of $21.9^\circ$. The corresponding fractions of simulated systems at least as extreme are $4.94\%$, $12.28\%$, and $5.04\%$, respectively. We describe the environment using the velocity-shear tensor smoothed on $2\,h^{-1}\mathrm{Mpc}$ and compare the full sample with Local Group-like pairs, systems whose shear eigenvalues match those reconstructed for the Local Group, and hosts in knots, filaments, sheets, and voids. Among the well-sampled environmental subsets, the shape and opening-angle distributions differ only weakly. Although the cosmic web imprints preferred orientations on host spins and satellite orbital-plane axes, the Milky Way combines orientations that are uncommon in the simulation. We conclude that the present-day cosmic-web environment alone does not substantially increase the occurrence of Milky Way-like satellite configurations. This result does not rule out an origin in anisotropic accretion, since neither the present-day web class nor its shear eigenvalues fully encodes the assembly history of an individual satellite system.
For four finite discrete random variables, we prove that the negative of the Ingleton expression, divided by the joint entropy, is at most $19/124 \approx 0.15323$, improving the previously reported upper bound $3/19$ by $11/2356$. The proof uses a copy program with four copy steps and six auxiliary variables, certified by exact rational linear-programming certificates. We also show that two published information inequalities, due to Dougherty-Freiling-Zeger and Csirmaz, imply the bound $5/32$ by an explicit nonnegative combination with Shannon inequalities, and that $5/32$ is the exact optimum both of the union of the three published inequality catalogues considered here and of the full copy-lemma extensions of all 213 distinct copy programs occurring in them. The new program is not among these programs, and the bound $19/124$ remains optimal when all of them and all catalogue inequalities are added. Ancillary files provide the parsed catalogues, exact primal and dual certificates, exact extension certificates, and verifiers that use only the Python standard library.
The extremely metal-poor nature of the C-19 stream indicates that its progenitor was a primordial stellar system born in the very early Universe. Current observations show that it has a small metallicity dispersion (0.18 at the 95% confidence level), which is the signature of a globular cluster origin, while at the same time displaying an unusually large velocity dispersion ($\sim10$ km/s) typical of dwarf galaxies. To reconcile this conflicting observational evidence, previous simulations have focused on potential interactions with dark matter subhalos, which can efficiently make a cluster stream dynamically hot. In this work, we explore internal dynamical processes in star cluster formation, focusing on initial conditions shaped by gas expulsion and a top-heavy initial mass function. We find that the large observed velocity dispersion and broad stream morphology can be reproduced by a cluster that underwent severe gas expulsion and expansion during its birth phase, which is potentially a typical formation scenario of extremely metal-poor star clusters. A top-heavy IMF and binaries can also increase the velocity dispersion. The formation of C-19 may involve a combination of these effects.
Understanding the formation and evolution of the Milky Way (MW) requires detailed knowledge of its satellite galaxies. In this study, we focus on the Sextans dwarf spheroidal (dSph) galaxy, a faint, dark matter (DM)-dominated satellite, to investigate the role of tidal and baryonic effects in shaping its observed properties. Using tailored $N$-body simulations, we explore possible orbits of Sextans in different MW models to reconstruct its progenitor's properties. Our simulations demonstrate the stars in Sextans are only mildly affected by galactic tides and the stellar kinematics provide robust constraints on its dynamical mass within the half-light radius, while the tidal mass loss of its DM component depends primarily on MW mass. The recovered infall mass of Sextans ranges from $1.22$ to $3.14\times10^9\rm\,M_\odot$ for MW masses from $0.8$ to $2\times10^{12}\rm\,M_\odot$. If the DM density remained as cuspy as NFW profile, the infall mass would be smaller by a factor of 2. Although with large ranges, the possible infall masses of Sextans recovered by our simulations are consistent with the stellar mass-halo mass relation in TNG50 and abundance matching results. We find some cases for the cuspy DM density profile where the infall mass is smaller than $10^9\rm\,M_\odot$, possibly indicating that star formation in Sextans is more efficient than in other satellites. The recovered DM halo structural parameters from our simulations provide valuable constraints for future studies on the DM content and formation history of Sextans.
Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundamentally compromises the identity mapping property intrinsic to the residual connection, which causes severe training instability and restricted scalability, and additionally incurs notable memory access overhead. To address these challenges, we propose Manifold-Constrained Hyper-Connections (mHC), a general framework that projects the residual connection space of HC onto a specific manifold to restore the identity mapping property, while incorporating rigorous infrastructure optimization to ensure efficiency. Empirical experiments demonstrate that mHC is effective for training at scale, offering tangible performance improvements and superior scalability. We anticipate that mHC, as a flexible and practical extension of HC, will contribute to a deeper understanding of topological architecture design and suggest promising directions for the evolution of foundational models.
We performed a systematic analysis of how the orbital configuration of a merger can influence the structural formation of remnant galaxies using 531 merger pairs selected from IllustrisTNG-100. We comprehensively described the merger orbital configuration, considering the relative orbital motion of the merger pair and their disk orientations. We quantified the galaxy structures by dynamically defining four components: bulge, disk, warm component, and hot inner stellar halo. For mergers on spiral-in orbits, the disk planes of the two merging galaxies tend to be aligned with the orbital plane, leading to higher fractions for the disk and warm components, as well as lower fractions for the bulge and hot inner stellar halo components in the remnant galaxy. For mergers on direct collision orbits, the disk planes of the two galaxies tend to be perpendicular to the orbital plane, leading to lower fractions for disk and warm components, as well as higher fractions of the bulge and hot inner stellar halo in the remnant. Mergers can lead to either an increase or decrease in the disk and bulge mass fraction in the remnant compared to the progenitor galaxy, depending on the merger orbital configurations; however, in 93% of cases, mergers cause an increase in the hot inner stellar halo. As a result, the luminosity fraction of the hot inner stellar halo (but not the bulge) in galaxies at $z=0$ is highly correlated with its total ex situ stellar mass. In addition, we find that merger on spiral-in orbits is one of the possible reasons for the formation of recently discovered red but HI-rich (RR) galaxies.
We carry out deep imaging of the Milky Way satellite galaxies, Bootes III and Draco, with WFST as one pilot observing program to demonstrate the capability of WFST. Combining catalogs with PS1 DR2 and Gaia DR3, we derive proper motions for candidate member stars in these two satellite galaxies over a 12-year time baseline, yielding uncertainties of ~1.8 mas/yr at 21 mag and ~3.0 mas/yr at 22 mag in the r band. The proper motions derived from bright and faint stars are consistent, indicating no significant variation in proper motion across stellar luminosity as these galaxies undergo tidal interactions with the MW. Meanwhile, we suggest that Bootes III represents the bound remnant of the progenitor galaxy that gave rise to the Styx stream, as evidenced by its elongated density profile and overdensity in both spatial and kinematic space. This is the first paper to use WFST to measure the proper motions of faint stars in Milky Way satellite galaxies. More detailed analyses will be presented in forthcoming papers from the wide field survey (WFS) program.
Identifying dwarf galaxies within the Local Volume is crucial for constraining the luminosity function of satellite galaxies in the nearby universe. We report the detection capabilities of dwarf galaxies within the Local Volume using the Chinese Space Station Telescope (CSST). Based on the simulated imaging data of CSST, we develop a detection and classification pipeline that combines traditional image-based search techniques with advanced machine learning classification models. The simulated Local Volume dwarf galaxies can be identified using a pre-processing method for "extended source detection", followed by classification with a pretrained ViT-Base model. This pipeline achieves a true positive rate (TPR) exceeding 85% with a false positive rate (FPR) of only 0.1%. We quantify the detection completeness of Local Volume dwarf galaxies across a three-dimensional parameter space defined by absolute magnitude ($M_V$), half-light radius ($R_h$), and heliocentric distance, based on simulated single-exposure CSST wide-field imaging survey data. For unresolved or semi-resolved dwarf galaxies, our method achieves a significantly deeper absolute magnitude detection limit compared to catalog-based approaches, reaching $M_V = -7$ within 10 \Mpc. By combining this image-based approach with traditional stellar catalog-based "matched filter" techniques, our automated framework established in this work can identify dwarf galaxies within 20 \Mpc for the CSST mission.
Designing a 6G-oriented universal model capable of processing multi-modal data and executing diverse air interface tasks has emerged as a common goal in future wireless systems. Building on our prior work in communication multi-modal alignment and telecom large language model (LLM), we propose a scalable, task-aware artificial intelligence-air interface multi-modal universal model (AI2MMUM), which flexibility and effectively perform various physical layer tasks according to subtle task instructions. The LLM backbone provides robust contextual comprehension and generalization capabilities, while a fine-tuning approach is adopted to incorporate domain-specific knowledge. To enhance task adaptability, task instructions consist of fixed task keywords and learnable, implicit prefix prompts. Frozen radio modality encoders extract universal representations and adapter layers subsequently bridge radio and language modalities. Moreover, lightweight task-specific heads are designed to directly output task objectives. Comprehensive evaluations demonstrate that AI2MMUM achieves SOTA performance across five representative physical environment/wireless channel-based downstream tasks using the WAIR-D and DeepMIMO datasets.
We explore the density profile, shape, and virial mass of the Milky Way's dark matter halo using K giants (KG) from LAMOST and SDSS/SEGUE, as well as blue horizontal branch (BHB) stars from SDSS. Incorporating Gaia DR3 proper motions, we first investigate the velocity ellipsoid distribution within the $(R, |z|)$ space. The ellipsoids projected onto the $(v_R, v_z)$ plane exhibit near-spherical alignment. We then probe the underlying dark matter distribution using the axisymmetric Jeans equations with multi-Gaussian expansion (MGE) and the spherically aligned Jeans anisotropic modelling (JAM${\rm sph}$), allowing for different flattened dark matter density models. For each model, we apply two fitting approaches: fitting the KGs and BHBs separately or fit them simultaneously as two dynamical tracers in one gravitational potential. We find consistent results on the dark matter density profiles, $r_{200}$, and $M_{200}$ within a 1-$σ$ confidence region for models constrained by KGs, BHBs, and both. We find the strongest consistency between KGs and BHBs in constraining dark matter profiles for models incorporating radially varying halo flattening ($q(r_{\rm gc})$), which suggests the Milky Way's dark matter halo shape evolves with Galactocentric distance ($r_{\rm gc}$). Specifically, the halo flattening parameter $q_h$ decreases within $r_{\rm gc} < 20$ kpc and increases for $r_{\rm gc} > 20$ kpc. In this model, $M_{\rm tot} (< 60~{\rm kpc}) = 0.533^{+0.061}_{-0.054} \times 10^{12}$ $M_{\odot}$, $r_{200}$ is $188\pm15$ kpc, with $M_{200}$ estimated at $0.820^{+0.210}_{-0.186} \times 10^{12} M_{\odot}$.
Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm...
Cosmological simulations indicate that nearly half of the baryons in the nearby Universe are in the warm-hot intergalactic medium (WHIM) phase, and about a half them reside in cosmic filaments connecting galaxy clusters. Recent observational studies using stacked survey data and deep exposures of galaxy cluster outskirts have detected soft X-ray excesses associated with optically identified filaments. However, the physical characteristics of WHIM in filaments remain largely undetermined due to a lack of direct spectral diagnostics of individual targets, which are limited by the spectral resolution of current instruments in the soft X-ray band. We aim to select appropriate targets for WHIM characterization through pointing observations with the future Hot Universe Baryon Surveyor (HUBS) mission, which is designed with eV-level energy resolution in the 0.1-2.0 keV band and a one-square-degree field of view, thus complementing other planned microcalorimetry missions such as Athena. We built a sample of 1577 inter-cluster filaments based on the eRASS1 supercluster catalog and estimated their soft X-ray emission. Their modeled emission and geometrical properties were used to select candidate targets for HUBS observations. Four inter-cluster filaments were selected as the most appropriate candidates. By simulating and analyzing their mock observations, we demonstrated that with 200 ks HUBS exposure for each candidate, the gas properties of individual filaments can be accurately determined, with the temperature constrained to $\pm0.01$ keV, metallicity constrained to $\leq\pm0.03$ solar, and density constrained to $<\pm10\%$. Elemental abundances of O, Ne, Mg, and Fe can be measured separately, providing unprecedented insights into the chemical history of the filament gas. We also show that direct mapping of the WHIM distribution is promising with narrowband imaging of the Oviii line.
Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the confidence intervals for the predicted values remains a challenge, due to the inherent complexities in material modeling. This study introduces a novel approach for uncertainty quantification in fatigue life prediction of metal materials based on integrating knowledge from physics-based fatigue life models and machine learning models. The proposed approach employs physics-based input features estimated using the Basquin fatigue model to augment the experimentally collected data of fatigue life. Furthermore, a physics-informed loss function that enforces boundary constraints for the estimated fatigue life of considered materials is introduced for the neural network models. Experimental validation on datasets comprising collected data from fatigue life tests for Titanium alloys and Carbon steel alloys demonstrates the effectiveness of the proposed approach. The synergy between physics-based models and data-driven models enhances the consistency in predicted values and improves uncertainty interval estimates.
The gravitational interactions between the LMC and the Milky Way cause dynamical perturbations in the MW halo, leading to biased distributions of stellar density and kinematics. We run 50 high-resolution N-body simulations exploring varying masses and halo shapes of the MW and LMC to study the evolution of LMC-induced perturbations. By measuring mean velocities of simulated halo stars, we identify a discontinuity between the first-infall and second-passage scenarios of the LMC's orbital history. In the first infall, the Galactocentric latitudinal velocity hovers around 16 km/s for stars at 50-100 kpc, while it subsides to about 8 km/s in the second-passage scenario. We demonstrate that this reduced perturbation magnitude in the second-passage scenario is mainly due to the short dynamical times of the Galactic inner halo and the lower velocity of the LMC during its second infall into the MW. Using a subset of $\sim 1100$ RR Lyrae stars located in the outer halo ($50 \leq R_{\mathrm{GC}} < 100$ kpc) with precise distance estimates from Gaia, we find the mean latitudinal velocity ($v_{b}$) in the Galactocentric frame to be $\langle v_{b} \rangle = 18.1 \pm 4.1$ km/s. The observation supports the first-infall scenario with a massive LMC ($\sim 2.1 \times 10^{11} \mathrm{M}_{\odot}$) at infall, an oblate MW halo with a virial mass $M_{200} < 1.4 \times 10^{12} \mathrm{M}_{\odot}$ and a flattening parameter $q > 0.7$. Our study indicates that LMC-induced kinematic disturbances can reveal its orbital history and key characteristics, as well as those of the MW. This approach shows promise in helping determine fundamental parameters of both galaxies.
The rapid advancement in generative pre-training models is propelling a paradigm shift in technological progression from basic applications such as chatbots towards more sophisticated agent-based systems. It is with huge potential and necessity that the 6G system be combined with the copilot of large language model (LLM) agents and digital twins (DT) to manage the highly complicated communication system with new emerging features such as native AI service and sensing. With the 6G-oriented agent, the base station could understand the transmission requirements of various dynamic upper-layer tasks, automatically orchestrate the optimal system workflow. Through continuously get feedback from the 6G DT for reinforcement, the agents can finally raise the performance of practical system accordingly. Differing from existing LLM agents designed for general application, the 6G-oriented agent aims to make highly rigorous and precise planning with a vast amount of extra expert knowledge, which inevitably requires a specific system design from model training to implementation. This paper proposes a novel comprehensive approach for building task-oriented 6G LLM agents. We first propose a two-stage continual pre-training and fine-tuning scheme to build the field basic model and diversities of specialized expert models for meeting the requirements of various application scenarios. Further, a novel inference framework based on semantic retrieval for leveraging the existing communication-related functions is proposed. Experiment results of exemplary tasks, such as physical-layer task decomposition, show the proposed paradigm's feasibility and effectiveness.