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Jianjun Zhou

Publications and source records attributed to Jianjun Zhou.

At least 19 recordsLinked to original sources
arXiv2026

ALMA Reveals an Explosive Outflow Candidate in IRAS 16119--5048

We present a multiwavelength study of the massive star formation region IRAS 16119-5048 (I16119) using ALMA ATOMS Band 3 and QUARKS Band 6 observations, complemented by archival ATCA radio continuum and Spitzer mid-infrared data. The CO (2-1) emission reveals a system of high-velocity streamer-like structures around the central region. Using dendrogram analysis of velocity-channel maps followed by linking in position-position-velocity space, we identify 16 approximately radially distributed streamers whose projected trajectories converge toward a common central region. The kinetic energy of the outflows is at least an order of magnitude lower than those of most known explosive outflows, while their mass entrainment and momentum rates are high compared with typical protostellar outflows, suggesting that I16119 may represent a low-energy explosive outflow candidate. Dense-gas and photodissociation-region tracers reveal shell-like structures associated with the 8 $\mu$m emission, indicating that feedback from the H II region may influence the streamer morphology. The 1.3 mm continuum resolves 27 dense cores along a fragmented filamentary structure. Their separations are consistent with thermal Jeans or cylindrical fragmentation, while the collect-and-collapse scenario is unsupported. The dense cores also show evidence of mass segregation, with the most massive cores concentrated near the inferred explosive centre. We suggest that I16119 is a plausible low-energy explosive outflow candidate, possibly triggered by dynamical interactions among centrally concentrated massive cores. However, the complex velocity structure and possible contamination from individual core-driven outflows prevent a definitive classification. More sensitive, higher angular-resolution observations are required to confirm the nature of the outflow.

astro-ph.GA↗
arXiv2026

ALOHA IRDCs Molecular Line Follow-up: I. Gas properties and kinematics

Infrared Dark Clouds are ideal sites for investigating the initial conditions of massive star and cluster formation. The A Lei Of the Habitat and Assembly of Infrared Dark Clouds (ALOHA IRDCs), a James Clerk Maxwell Telescope (JCMT) Large Program, has mapped nearby IRDCs with SCUBA-2. Complementary molecular line observations are needed to characterise the physical, kinematic, and chemical properties of the dense gas. We aim to determine the thermal, kinematic, and chemical properties of clumps identified in the ALOHA IRDCs, and to assess their evolutionary status and level of star-forming activity. We performed single-pointing K-band and W-band observations towards 56 ALOHA IRDCs clumps using the Effelsberg 100-m and Yebes 40-m telescopes, respectively. We derived NH3 kinetic temperatures using the hyperfine group ratio (HFGR) method and identified infall and shock signatures from HCO+, H13CO+, SiO, and HNCO profiles. Water masers and NH2D emission were used as complementary tracers of chemical evolution and star formation. The clumps exhibit kinetic temperatures of 15-29 K. We detect NH2D emission towards 18 sources, with NH2D centroid velocities consistent with NH3, indicating both species trace the same dense gas component. More than half of the clumps display blue-asymmetric HCO+ profiles, identifying them as infall candidates. Water masers are detected in 22 sources, with prominent velocity ranges and variability. Broad SiO emission (>~20 km/s) indicates strong shocks, while narrower extents (<~6km/s) likely trace large-scale interactions or low-velocity shocks. The widespread infall signatures, shock tracers, masers, and NH2D emission suggest that relatively quiescent, chemically young material can coexist with dynamically active gas affected by early protostellar feedback, providing insight into the coupled physical and chemical evolution of massive IRDC clumps.

astro-ph.GA↗
arXiv2026

The TOP-SCOPE Survey of Planck Galactic Cold Clumps: Molecular gas properties

We surveyed 2008 Planck Galactic Cold Clumps (PGCCs) in $^{12}\mathrm{CO}$ and $^{13}\mathrm{CO}$ $J=1$--0 lines using the Taeduk Radio Astronomy Observatory (TRAO) 14 m telescope's multi-beam receiver. We detected 2784 ($^{12}\mathrm{CO}$) and 2291 ($^{13}\mathrm{CO}$) velocity components, their closely correlated centroid velocities suggest that $^{12}$CO and $^{13}$CO generally trace kinematically associated gas. PGCCs have low excitation temperatures (mean $\sim$10 K), mean $^{13}\mathrm{CO}$ optical depth $\sim$0.5, and mean $^{13}\mathrm{CO}$-derived H$_2$ column density $4.3\times10^{21}$~cm$^{-2}$. Gas--dust correlations are moderate, with $N_{^{13}\mathrm{CO}}$ more tightly correlated with the dust-derived H$_2$ column density from the PGCC catalog than $I_{^{12}\mathrm{CO}}$. Colder PGCCs tend to have higher CO-to-H$_2$ conversion factor ($X_{\mathrm{CO}}$) and $[\mathrm{H_{2}}]/[^{13}\mathrm{CO}]$ ratio. $X_{\mathrm{CO}}$ increases clearly with the dust-derived H$_2$ column density, consistent with enhanced CO freeze-out in high-column-density gas. Supersonic non-thermal motions are widespread: the Mach number derived from $^{13}\mathrm{CO}$ has a mean of 4.3 and a median of 3.6, increasing slightly with dust-derived H$_2$ column density. Overall, PGCCs are cold but dynamically active, serving as a valuable laboratory for studying the initial conditions of star formation.

astro-ph.GA↗
arXiv2026

Local well-posedness of general mean field game master equations

This paper presents a generic approach for establishing mean field game master equations, applicable whenever the mean field equilibrium can be characterized by a McKean-Vlasov forward-backward stochastic differential equation system. The core of our approach is a representation formula for the first-order Lions derivative of the decoupling field of this forward-backward SDE system. We then employ a bootstrap argument to recursively compute its higher-order derivatives. To demonstrate the method's versatility, we establish the local well-posedness for master equations in three distinct models: extended mean field games, mean field games with volatility control, and mean field games with a major player.

math.PR↗
arXiv2026

A 1.3 cm spectral line study of the W33 region

At a distance of 2.4kpc, W33 is one of the most prolific sources of molecular line emission, and it is an excellent research target for a centimeter spectral line search. We carried out a 1.3cm spectral line survey in the frequency range 18-26GHz. The lines we identified include 44 radio recombination lines (RRLs) and 24 molecular lines, excluding transitions from the main isotopolog of NH3. The RRLs are associated with the ionized gas from W33Main. Intensity ratios between RRL pairs with varying differences in the principal quantum number $n$ (i.e., $\Delta n$) from the same element at adjacent frequencies agree with ratios expected under conditions of local thermodynamical equilibrium. In spite of a resulting helium-to-hydrogen abundance ratio (equal emitting volumes assumed) of (10.7$\pm$1.8)\%, which is consistent with expectations, helium shows broader turbulent line widths than hydrogen. The difference amounts to a few kilometers per second, hinting that the spatial distributions are slightly different. The molecular lines are attributed to nine different species (CH3OH, HC3N, SiS, c-C3H2, CH3CN, NH2D, HNCO, H2O and CCS). Rotation temperatures and column densities were derived from CH3OH transitions using rotational temperature diagram analysis. Maser emission produced by water vapor and methanol have been observed in W33Main, W33A, and W33B. Our survey discovered a CH3OH(10$_{2,8}$-10$_{1,9}$E) maser in W33Main. Toward W33B1, the fractionated deuterium-to-hydrogen ratio (D/H) deduced from para-NH2D/NH3 is estimated to be $\lesssim$(1.0$\pm$0.2)$\times$10$^{-3}$. For the other molecular W33-hotspots, 3$\sigma$ upper limits are (5.0$\pm$0.4)$\times$10$^{-3}$. At linear scales of (0.5pc), fractional abundances and excitation temperatures do not reach values close to those in well-established hot cores, but higher-resolution measurements may alter this picture.

astro-ph.GA↗
arXiv2026

ALMA-QUARKS view of W49N: Multipolar episodic outflow associated with the most energetic Galactic water maser

We present a detailed investigation of a multipolar episodic molecular outflow in the mini-starburst region W49N, which hosts the most luminous water maser in the Galaxy. Using high-resolution ($\sim$0.3 arcsec) Atacama Large Millimeter/submillimeter Array (ALMA) observations of the $\mathrm{^{12}CO}$ emission as part of the ALMA-QUARKS survey, we analyze the morphology and kinematics of the outflow. Our observations reveal four newly identified outflow lobes in addition to the previously known central bipolar jet. These lobes appear more jet-like rather than exhibiting wide opening angles. Based on the $\mathrm{^{12}CO}$ (2-1) and $\mathrm{^{13}CO}$ (2-1) emission, we provide a more reliable estimate of the outflow's physical parameters, confirming it as one of the most energetic outflows in the Galaxy. Notably, these newly discovered lobes exhibit chains of knots, a characteristic signature of episodic ejection. Furthermore, two of the lobes display prominent S-shaped wiggles, suggestive of a precessing jet. The discovery of these features -- commonly observed in outflows from low-mass protostars -- in such an extreme massive star-forming environment provides compelling evidence that some underlying physical mechanisms for launching outflows are conserved across a wide range of stellar masses.

astro-ph.GA↗
arXiv2026

The ALMA-QUARKS survey: Investigating Thermal Feedback of Massive Protostars in Hot Molecular Cores

We identify a sample of 83 spatially resolved hot molecular cores (HMCs) in the QUARKS survey, aiming at investigating thermal feedback from massive stars. Using CH$_3$CN\,(12--11) line emission together with 1.3\,mm continuum data we derive the radial temperature, volume density and \ch3cn{} abundance profiles for the 83 HMCs. Based on the envelope temperature and density profiles, we compute the luminosities of the embedded massive protostars with \radmc{} radiation transfer model. The derived luminosities are comparable (within $\sim1$ dex) to the bolometric luminosities of their natal clumps and show strong correlations with several core-scale properties, including the HMC mass ($Log[ M_\mathrm{env}] = 1.01\,Log [L_\star] - 4.80$), the inner core radius (the flat radius of Plummer-like volume density profile) ($Log[a] = 0.46\,Log[L_\star] + 0.52$) and the central density $ (Log[n_c] = -0.55 Log[L_\star] +10.47) $. These empirical relations provide useful observational constraints for physical models of protostellar objects. Importantly, we find a strong positive correlation between the massive protostellar luminosity and the local thermal Jeans mass. The derived Jeans masses, $M_\mathrm{Jeans}$, exceed the HMC masses $M_\mathrm{env}$, with the average $M_\mathrm{Jeans}$ being two times larger than the average $M_\mathrm{env}$. This provides observational evidence that thermal feedback from massive protostars can effectively suppress further fragmentation of HMCs, thereby promoting massive star formation. In addition, the positive correlation between massive protostellar luminosity and natal clump mass suggests that more massive clumps preferentially host more luminous protostars, leading to stronger thermal feedback.

astro-ph.GA↗
arXiv2026

The ALMA-QUARKS Survey: Discovery of Dusty Fibrils inside Massive Star-forming Clumps

We report the discovery of more than 323 superfine dusty filamentary structures (fibrils) inside 121 massive star forming clumps that are located in widely different Galactic environments (Galactocentric distances of $\sim$0.5-12.7 kpc). These fibrils are identified from the 1.3~mm continuum emission in the ALMA-QUARKS survey, which has a linear resolution of $\sim900$ AU for a source at $\sim$3 kpc, using the \textit{FilFinder} software. Using \textit{RadFil} software, we find that the typical width of these fibrils is $\sim$0.01 pc, which is about ten times narrower than that of dusty filaments in nearby clouds identified by the \textit{Herschel} Space Observatory. The mass ($M$) versus length ($L$) relation for these fibrils follows $M\propto L^{2}$, similar to that of Galactic filaments identified in space (e.g., \textit{Herschel}) and ground-based single-dish (e.g., \textit{APEX}) surveys. However, these fibrils are significantly denser ($\mathrm{N_{H_2} = 10^{23}-10^{24}\ cm^{-2}}$) than the filaments found in previous \textit{Herschel} surveys ($\mathrm{N_{H_2} = 10^{20}-10^{23}\ cm^{-2}}$). This work contributes a large sample of superfine fibrils in massive clumps, following the identification of large 0.1-pc wide filaments and associated internal velocity coherent fibers in nearby molecular clouds, further emphasizing the crucial role played by filamentary structures in star formation at various physical scales.

astro-ph.GA↗
arXiv2026

Optimal Control of Unbounded Stochastic Evolution Systems in Hilbert Spaces

Optimal control and the associated second-order Hamilton-Jacobi-Bellman (HJB) equation are studied for unbounded stochastic evolution systems in Hilbert spaces. A new notion of viscosity solution, featured by absence of B-continuity, is introduced for the second-order HJB equation in the sense of Crandall and Lions, and is shown to coincide with the classical solutions and to satisfy a stability property. The value functional is proved to be the unique continuous viscosity solution to the second-order HJB equation, with the coefficients being not necessarily B-continuous. Our result provides a new theory of viscosity solutions to the HJB equation for optimal control of stochastic evolutionary equations-driven by a linear unbounded operator-in a Hilbert space, and removes the B-continuity assumption on the coefficients which is used in the existing literature.

math.OC↗
arXiv2025

Error Bound Analysis of Physics-Informed Neural Networks-Driven T2 Quantification in Cardiac Magnetic Resonance Imaging

Physics-Informed Neural Networks (PINN) are emerging as a promising approach for quantitative parameter estimation of Magnetic Resonance Imaging (MRI). While existing deep learning methods can provide an accurate quantitative estimation of the T2 parameter, they still require large amounts of training data and lack theoretical support and a recognized gold standard. Thus, given the absence of PINN-based approaches for T2 estimation, we propose embedding the fundamental physics of MRI, the Bloch equation, in the loss of PINN, which is solely based on target scan data and does not require a pre-defined training database. Furthermore, by deriving rigorous upper bounds for both the T2 estimation error and the generalization error of the Bloch equation solution, we establish a theoretical foundation for evaluating the PINN's quantitative accuracy. Even without access to the ground truth or a gold standard, this theory enables us to estimate the error with respect to the real quantitative parameter T2. The accuracy of T2 mapping and the validity of the theoretical analysis are demonstrated on a numerical cardiac model and a water phantom, where our method exhibits excellent quantitative precision in the myocardial T2 range. Clinical applicability is confirmed in 94 acute myocardial infarction (AMI) patients, achieving low-error quantitative T2 estimation under the theoretical error bound, highlighting the robustness and potential of PINN.

physics.bio-ph↗
arXiv2025

The ALMA-QUARKS survey: Evidence of a candidate high-mass prestellar core aside a bright-rimmed cloud IRAS 18290-0924

Although frequently reported in observations, the definitive confirmation of high-mass prestellar cores has remained elusive, presenting a persistent challenge in star formation studies. Using two-band observational data from the 3mm ATOMS and 1.3mm QUARKS surveys, we report a high-mass prestellar core candidate, C2, located on the side of the bright-rimmed cloud IRAS 18290-0924. The C2 core identified from the 3mm continuum data of the ATOMS survey ($\sim$2 arcsecond, $\rm\sim 10000~au$ at 5.3 kpc) has a mass ranging from 27-68 $M_{\odot}$ for temperatures 10-22K within a radius of $\sim$2800 au. The highest-resolution ($\sim$0.3 arcsecond, $\rm\sim 1500 au$) observations of this source presented to date from the QUARKS survey reveal no evidence of further fragmentation. Further analysis of a total $\sim$10 GHz band width of molecular line survey does not find star-formation activity (e.g., outflows, ionized gas) associated with the core, with a few molecular lines of cold gas detected only. Additionally, virial analysis indicates the C2 core is gravitationally bound ($\alpha_{\rm vir} \sim0.1-0.3$) and thus could be undergoing collapse toward star formation. These results strongly establish a candidate for a high-mass prestellar core, contributing to the very limited number of such sources known to date.

astro-ph.GA↗
arXiv2025

The ALMA-QUARKS survey: Hot Molecular Cores are a long-standing phenomenon in the evolution of massive protostars

We present an analysis of the QUARKS survey sample, focusing on protoclusters where Hot Molecular Cores (HMCs, traced by CH3CN(12--11)) and UC HII regions (traced by H30\alpha/H40\alpha) coexist. Using the high-resolution, high-sensitivity 1.3 mm data from the QUARKS survey, we identify 125 Hot Molecular Fragments (HMFs), which represent the substructures of HMCs at higher resolution. From line integrated intensity maps of CH3CN(12--11) and H30\alpha, we resolve the spatial distribution of HMFs and UC HII regions. By combining with observations of CO outflows and 1.3 mm continuum, we classify HMFs into four types: HMFs associated with jet-like outflow, with wide-angle outflow, with non-detectable outflow, and shell-like HMFs near UC HII regions. This diversity possibly indicates that the hot core could be polymorphic and long-standing phenomenon in the evolution of massive protostars. The separation between HMFs and H30\alpha/H40\alpha emission suggests that sequential high-mass star formation within young protoclusters is not likely related to feedback mechanisms.

astro-ph.GA↗
arXiv2025

Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning

Clinical adoption of multi-shot diffusion-weighted magnetic resonance imaging (multi-shot DWI) for body-wide tumor diagnostics is limited by severe motion-induced phase artifacts from respiration, peristalsis, and so on, compounded by multi-organ, multi-slice, multi-direction and multi-b-value complexities. Here, we introduce a reconstruction framework, LoSP-Prompt, that overcomes these challenges through physics-informed modeling and synthetic-data-driven prompt learning. We model inter-shot phase variations as a high-order Locally Smooth Phase (LoSP), integrated into a low-rank Hankel matrix reconstruction. Crucially, the algorithm's rank parameter is automatically set via prompt learning trained exclusively on synthetic abdominal DWI data emulating physiological motion. Validated across 10,000+ clinical images (43 subjects, 4 scanner models, 5 centers), LoSP-Prompt: (1) Achieved twice the spatial resolution of clinical single-shot DWI, enhancing liver lesion conspicuity; (2) Generalized to seven diverse anatomical regions (liver, kidney, sacroiliac, pelvis, knee, spinal cord, brain) with a single model; (3) Outperformed state-of-the-art methods in image quality, artifact suppression, and noise reduction (11 radiologists' evaluations on a 5-point scale, $p<0.05$), achieving 4-5 points (excellent) on kidney DWI, 4 points (good to excellent) on liver, sacroiliac and spinal cord DWI, and 3-4 points (good) on knee and tumor brain. The approach eliminates navigator signals and realistic data supervision, providing an interpretable, robust solution for high-resolution multi-organ multi-shot DWI. Its scanner-agnostic performance signifies transformative potential for precision oncology.

cs.CV↗
arXiv2025

Optimal Consumption-Investment with Epstein-Zin Utility under Leverage Constraint

We study optimal portfolio choice under Epstein-Zin recursive utility in the presence of general leverage constraints. We first establish that the optimal value function is the unique viscosity solution to the associated Hamilton-Jacobi-Bellman (HJB) equation, by developing a new dynamic programming principle under constraints. We further demonstrate that the value function admits smoothness and characterize the optimal consumption and investment strategies. In addition, we derive explicit solutions for the optimal strategy and explicitly delineate the constrained and unconstrained regions in several special cases of the leverage constraint. Finally, we conduct a comparative analysis, highlighting the differences relative to the classical time-separable preferences and to the setting without leverage constraints.

q-fin.PM↗
arXiv2025

OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling

The field of 4D world modeling - aiming to jointly capture spatial geometry and temporal dynamics - has witnessed remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning. However, the development of truly general 4D world models remains fundamentally constrained by the availability of high-quality data. Existing datasets and benchmarks often lack the dynamic complexity, multi-domain diversity, and spatial-temporal annotations required to support key tasks such as 4D geometric reconstruction, future prediction, and camera-control video generation. To address this gap, we introduce OmniWorld, a large-scale, multi-domain, multi-modal dataset specifically designed for 4D world modeling. OmniWorld consists of a newly collected OmniWorld-Game dataset and several curated public datasets spanning diverse domains. Compared with existing synthetic datasets, OmniWorld-Game provides richer modality coverage, larger scale, and more realistic dynamic interactions. Based on this dataset, we establish a challenging benchmark that exposes the limitations of current state-of-the-art (SOTA) approaches in modeling complex 4D environments. Moreover, fine-tuning existing SOTA methods on OmniWorld leads to significant performance gains across 4D reconstruction and video generation tasks, strongly validating OmniWorld as a powerful resource for training and evaluation. We envision OmniWorld as a catalyst for accelerating the development of general-purpose 4D world models, ultimately advancing machines' holistic understanding of the physical world.

cs.CV↗
arXiv2025

WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool

We present WinT3R, a feed-forward reconstruction model capable of online prediction of precise camera poses and high-quality point maps. Previous methods suffer from a trade-off between reconstruction quality and real-time performance. To address this, we first introduce a sliding window mechanism that ensures sufficient information exchange among frames within the window, thereby improving the quality of geometric predictions without large computation. In addition, we leverage a compact representation of cameras and maintain a global camera token pool, which enhances the reliability of camera pose estimation without sacrificing efficiency. These designs enable WinT3R to achieve state-of-the-art performance in terms of online reconstruction quality, camera pose estimation, and reconstruction speed, as validated by extensive experiments on diverse datasets. Code and model are publicly available at https://github.com/LiZizun/WinT3R.

cs.CV↗
arXiv2025

The ALMA-QUARKS Survey: III. Clump-to-core fragmentation and search for high-mass starless cores

The Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures (QUARKS) survey observed 139 infrared-bright (IR-bright) massive protoclusters at 1.3 mm wavelength with ALMA. This study investigates clump-to-core fragmentation and searches for candidate high-mass starless cores within IR-bright clumps using combined ALMA 12-m (C-2) and Atacama Compact Array (ACA) 7-m data, providing $\sim$ 1 arcsec ($\sim\rm0.02~pc$ at 3.7 kpc) resolution and $\sim\rm0.6\,mJy\,beam^{-1}$ continuum sensitivity ($\sim 0.3~M_{\odot}$ at 30 K). We identified 1562 compact cores from 1.3 mm continuum emission using getsf. Observed linear core separations ($\lambda_{\rm obs}$) are significantly less than the thermal Jeans length ($\lambda_{\rm J}$), with the $\lambda_{\rm obs}/\lambda_{\rm J}$ ratios peaking at $\sim0.2$. This indicates that thermal Jeans fragmentation has taken place within the IR-bright protocluster clumps studied here. The observed low ratio of $\lambda_{\rm obs}/\lambda_{\rm J}\ll 1$ could be the result of evolving core separation or hierarchical fragmentation. Based on associated signatures of star formation (e.g., outflows and ionized gas), we classified cores into three categories: 127 starless, 971 warm, and 464 evolved cores. Two starless cores have mass exceeding 16$\,M_{\odot}$, and represent high-mass candidates. The scarcity of such candidates suggests that competitive accretion-type models could be more applicable than turbulent core accretion-type models in high-mass star formation within these IR-bright protocluster clumps.

astro-ph.GA↗
arXiv2025

$\pi^3$: Permutation-Equivariant Visual Geometry Learning

We introduce $\pi^3$, a feed-forward neural network that offers a novel approach to visual geometry reconstruction, breaking the reliance on a conventional fixed reference view. Previous methods often anchor their reconstructions to a designated viewpoint, an inductive bias that can lead to instability and failures if the reference is suboptimal. In contrast, $\pi^3$ employs a fully permutation-equivariant architecture to predict affine-invariant camera poses and scale-invariant local point maps without any reference frames. This design not only makes our model inherently robust to input ordering, but also leads to higher accuracy and performance. These advantages enable our simple and bias-free approach to achieve state-of-the-art performance on a wide range of tasks, including camera pose estimation, monocular/video depth estimation, and dense point map reconstruction. Code and models are available at https://github.com/yyfz/Pi3.

cs.CV↗