arXiv subjects

Zhiqiang Yan

Publications and source records attributed to Zhiqiang Yan.

At least 37 records · Page 2Linked to original sources

arXiv2025

Massive Star Formation at Supersolar Metallicities: Constraints on the Initial Mass Function

Metals enhance the cooling efficiency of molecular clouds, promoting fragmentation. Consequently, increasing the metallicity may boost the formation of low-mass stars. Within the integrated galaxy initial mass function (IGIMF) theory, this effect is empirically captured by a linear relation between the slope of the low-mass stellar IMF, $α_1$, and the metal mass fraction, $Z$. This linear $α_1$-$Z$ relation has been calibrated up to $\approx 2 \, Z_{\odot}$, though higher metallicity environments are known to exist. We show that if the linear $α_1$-$Z$ relation extends to higher metallicities ($[Z] \gtrsim 0.5$), massive star formation is suppressed entirely. Alternatively, fragmentation efficiency may saturate beyond some metallicity threshold if gravitational collapse cascades rapidly enough. To model this behavior, we propose a logistic function describing the transition from metallicity-sensitive to metallicity-insensitive fragmentation regimes. We provide a user-friendly public code, pyIGIMF, which enables the instantaneous computation of the IGIMF theory with the logistic $α_1$-$Z$ relation.

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arXiv2025

Cosmic Threads: Interlinking the Stellar Initial Mass Function from Star-Birth to Galaxies

The stellar initial mass function (sIMF) describes the distribution of stellar masses formed in a single star formation event in a molecular cloud clump. It is fundamental to astrophysics and cosmology, shaping our understanding of unresolved stellar populations, galactic chemical enrichment and habitable zones, and black hole growth. This White Paper reviews studies on the core mass function, stellar multiplicity, and dynamical processes affecting sIMF determinations, as well as the link between star-forming clumps and the galaxy-wide IMF (gIMF). The evidence gleaned from observed systems for the dependency of the sIMF on the metallicity and density of the clump is portrayed. We examine evidence from gravitational lensing, stellar and gas kinematics, and spectral diagnostics to assess environmental dependencies of the gIMF. Theoretical perspectives provide further insights into the sIMF's variability. Beyond summarizing current knowledge, this work aims to establish a shared framework and define strategies for studying a variable IMF in the era of near-infrared integral-field spectroscopy, 30m-class telescopes and major space-based observatories.

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arXiv2025

DuCos: Duality Constrained Depth Super-Resolution via Foundation Model

We introduce DuCos, a novel depth super-resolution framework grounded in Lagrangian duality theory, offering a flexible integration of multiple constraints and reconstruction objectives to enhance accuracy and robustness. Our DuCos is the first to significantly improve generalization across diverse scenarios with foundation models as prompts. The prompt design consists of two key components: Correlative Fusion (CF) and Gradient Regulation (GR). CF facilitates precise geometric alignment and effective fusion between prompt and depth features, while GR refines depth predictions by enforcing consistency with sharp-edged depth maps derived from foundation models. Crucially, these prompts are seamlessly embedded into the Lagrangian constraint term, forming a synergistic and principled framework. Extensive experiments demonstrate that DuCos outperforms existing state-of-the-art methods, achieving superior accuracy, robustness, and generalization.

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arXiv2025

The effect of the environment-dependent stellar initial mass function on the baryonic Tully Fisher relation

We investigate the impact of an environment-dependent galaxy-wide stellar initial mass function (gwIMF) on the baryonic Tully-Fisher relation (BTFR). The integrated galaxy-wide IMF (IGIMF) theory, which incorporates variations in stellar populations due to star formation history (SFH) and metallicity, provides a more accurate framework for understanding systematic deviations in galaxy scaling relations than that given by an invariant gwIMF. By considering how the mass-to-light ratio of the stellar population is influenced by metallicity and SFH, we show that high-mass galaxies have their masses in stars and remnants underestimated under the assumption of a constant mass-to-light ratio. In contrast, low-mass, gas-dominated galaxies are less affected. Our results suggest that the discrepancies between the true and observed BTFR are primarily driven by the evolving nature of the stellar IMF, particularly in galaxies with slowly declining SFHs. The IGIMF theory offers a solution to the observed offsets in the BTFR, especially for high-mass galaxies, where the rotational velocities are higher than predicted by MOND. We conclude that incorporating the IGIMF provides a more accurate description of galaxy dynamics, revealing the importance of stellar population characteristics in refining our understanding of the baryonic mass-velocity relationship. This study underscores the necessity of accounting for the variation of the gwIMF when interpreting the BTFR, particularly in the context of alternative gravitational theories like MOND.

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arXiv2025

Event-Driven Dynamic Scene Depth Completion

Depth completion in dynamic scenes poses significant challenges due to rapid ego-motion and object motion, which can severely degrade the quality of input modalities such as RGB images and LiDAR measurements. Conventional RGB-D sensors often struggle to align precisely and capture reliable depth under such conditions. In contrast, event cameras with their high temporal resolution and sensitivity to motion at the pixel level provide complementary cues that are %particularly beneficial in dynamic environments.To this end, we propose EventDC, the first event-driven depth completion framework. It consists of two key components: Event-Modulated Alignment (EMA) and Local Depth Filtering (LDF). Both modules adaptively learn the two fundamental components of convolution operations: offsets and weights conditioned on motion-sensitive event streams. In the encoder, EMA leverages events to modulate the sampling positions of RGB-D features to achieve pixel redistribution for improved alignment and fusion. In the decoder, LDF refines depth estimations around moving objects by learning motion-aware masks from events. Additionally, EventDC incorporates two loss terms to further benefit global alignment and enhance local depth recovery. Moreover, we establish the first benchmark for event-based depth completion comprising one real-world and two synthetic datasets to facilitate future research. Extensive experiments on this benchmark demonstrate the superiority of our EventDC.

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arXiv2025

Why is the Star Formation Rate Proportional to Dense Gas Mass?

One of the most profound empirical laws of star formation is the Gao-Solomon relation, a linear correlation between the star formation rate (SFR) and the dense molecular gas mass. It is puzzling how the complicated physics in star-formation results in this surprisingly simple proportionality. Using archival Herschel and Atacama Large Millimeter/submillimeter Array Observations, we derived the masses of the most massive cores ($M^{\rm max}_{\rm core}$) and masses of the gravitationally bound gas ($ M_{\rm gas}^{\rm bound}$) in the parent molecular clouds for a sample of low-mass and high-mass star-forming regions. We discovered a significant correlation $\log(M^{\rm max}_{\rm core}/M_{\odot}) = 0.506 \log(M_{\rm gas}^{\rm bound}/M_{\odot})-0.32$. Our discovered $M^{\rm max}_{\rm core}$-$M_{\rm gas}^{\rm bound}$ correlation can be approximately converted to the Gao-Solomon relation if there is (1) a constant 30% efficiency of converting $M^{\rm max}_{\rm core}$ to the mass of the most massive star ($m^{\rm max}_{\rm star}$), and (2) if SFR and $m^{\rm max}_{\rm star}$ are tightly related through $\log({\rm SFR}/(M_{\odot} {\rm yr}^{-1})) = 2.04 \log(m^{\rm max}_{\rm star}/M_{\odot})-5.80$. Intriguingly, both requirements have been suggested by previous theoretical studies (c.f. Yan et al. 2017). Based on this result, we hypothesize that the Gao-Solomon relation is a consequence of combining the following three non-trivial relations (i) SFR vs. $m^{\rm max}_{\rm star}$, (ii) $m^{\rm max}_{\rm star}$ vs. $M^{\rm max}_{\rm core}$, and (iii) $M^{\rm max}_{\rm core}$ vs. $M_{\rm gas}^{\rm bound}$. This finding may open a new possibility to understand the Gao-Solomon relation in an analytic sense.

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arXiv2025

DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution

Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e.g., bicubic downsampling). However, in real-world scenarios, captured depth data often suffer from unconventional and unknown degradation due to sensor limitations and complex imaging environments (e.g., low reflective surfaces, varying illumination). Consequently, the performance of these methods significantly declines when real-world degradation deviate from their assumptions. In this paper, we propose the Degradation Oriented and Regularized Network (DORNet), a novel framework designed to adaptively address unknown degradation in real-world scenes through implicit degradation representations. Our approach begins with the development of a self-supervised degradation learning strategy, which models the degradation representations of low-resolution depth data using routing selection-based degradation regularization. To facilitate effective RGB-D fusion, we further introduce a degradation-oriented feature transformation module that selectively propagates RGB content into the depth data based on the learned degradation priors. Extensive experimental results on both real and synthetic datasets demonstrate the superiority of our DORNet in handling unknown degradation, outperforming existing methods. The code is available at https://github.com/yanzq95/DORNet.

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arXiv2025

Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion

In this paper, we introduce the Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first time. Moving beyond direct completion using convolutional neural networks (CNNs), SigNet initially densifies sparse depth data through non-CNN densification tools to obtain coarse yet dense depth. This approach eliminates the mismatch and ambiguity caused by direct convolution over irregularly sampled sparse data. Subsequently, SigNet redefines completion as enhancement, establishing a self-supervised degradation bridge between the coarse depth and the targeted dense depth for effective RGB-D fusion. To achieve this, SigNet leverages the implicit degradation to adaptively select high-frequency components (e.g., edges) of RGB data to compensate for the coarse depth. This degradation is further integrated into a multi-modal conditional Mamba, dynamically generating the state parameters to enable efficient global high-frequency information interaction. We conduct extensive experiments on the NYUv2, DIML, SUN RGBD, and TOFDC datasets, demonstrating the state-of-the-art (SOTA) performance of SigNet.

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arXiv2025

Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction

The task of vision-based 3D occupancy prediction aims to reconstruct 3D geometry and estimate its semantic classes from 2D color images, where the 2D-to-3D view transformation is an indispensable step. Most previous methods conduct forward projection, such as BEVPooling and VoxelPooling, both of which map the 2D image features into 3D grids. However, the current grid representing features within a certain height range usually introduces many confusing features that belong to other height ranges. To address this challenge, we present Deep Height Decoupling (DHD), a novel framework that incorporates explicit height prior to filter out the confusing features. Specifically, DHD first predicts height maps via explicit supervision. Based on the height distribution statistics, DHD designs Mask Guided Height Sampling (MGHS) to adaptively decouple the height map into multiple binary masks. MGHS projects the 2D image features into multiple subspaces, where each grid contains features within reasonable height ranges. Finally, a Synergistic Feature Aggregation (SFA) module is deployed to enhance the feature representation through channel and spatial affinities, enabling further occupancy refinement. On the popular Occ3D-nuScenes benchmark, our method achieves state-of-the-art performance even with minimal input frames. Source code is released at https://github.com/yanzq95/DHD.

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arXiv2025

Variable Radiance Field for Real-World Category-Specific Reconstruction from Single Image

Reconstructing category-specific objects using Neural Radiance Field (NeRF) from a single image is a promising yet challenging task. Existing approaches predominantly rely on projection-based feature retrieval to associate 3D points in the radiance field with local image features from the reference image. However, this process is computationally expensive, dependent on known camera intrinsics, and susceptible to occlusions. To address these limitations, we propose Variable Radiance Field (VRF), a novel framework capable of efficiently reconstructing category-specific objects without requiring known camera intrinsics and demonstrating robustness against occlusions. First, we replace the local feature retrieval with global latent representations, generated through a single feed-forward pass, which improves efficiency and eliminates reliance on camera intrinsics. Second, to tackle coordinate inconsistencies inherent in real-world dataset, we define a canonical space by introducing a learnable, category-specific shape template and explicitly aligning each training object to this template using a learnable 3D transformation. This approach also reduces the complexity of geometry prediction to modeling deformations from the template to individual instances. Finally, we employ a hyper-network-based method for efficient NeRF creation and enhance the reconstruction performance through a contrastive learning-based pretraining strategy. Evaluations on the CO3D dataset demonstrate that VRF achieves state-of-the-art performance in both reconstruction quality and computational efficiency.

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arXiv2025

Learning Inverse Laplacian Pyramid for Progressive Depth Completion

Depth completion endeavors to reconstruct a dense depth map from sparse depth measurements, leveraging the information provided by a corresponding color image. Existing approaches mostly hinge on single-scale propagation strategies that iteratively ameliorate initial coarse depth estimates through pixel-level message passing. Despite their commendable outcomes, these techniques are frequently hampered by computational inefficiencies and a limited grasp of scene context. To circumvent these challenges, we introduce LP-Net, an innovative framework that implements a multi-scale, progressive prediction paradigm based on Laplacian Pyramid decomposition. Diverging from propagation-based approaches, LP-Net initiates with a rudimentary, low-resolution depth prediction to encapsulate the global scene context, subsequently refining this through successive upsampling and the reinstatement of high-frequency details at incremental scales. We have developed two novel modules to bolster this strategy: 1) the Multi-path Feature Pyramid module, which segregates feature maps into discrete pathways, employing multi-scale transformations to amalgamate comprehensive spatial information, and 2) the Selective Depth Filtering module, which dynamically learns to apply both smoothness and sharpness filters to judiciously mitigate noise while accentuating intricate details. By integrating these advancements, LP-Net not only secures state-of-the-art (SOTA) performance across both outdoor and indoor benchmarks such as KITTI, NYUv2, and TOFDC, but also demonstrates superior computational efficiency. At the time of submission, LP-Net ranks 1st among all peer-reviewed methods on the official KITTI leaderboard.

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arXiv2024

The initial mass function of stars

The initial mass function (IMF) is one of the most important functions in astrophysics because it is key to reconstructing the cosmological matter cycle, understanding the formation of super-massive black holes, and deciphering the light from high-redshift observations. The IMF's dependency on the physical conditions of the gas and its connection to the galaxy-wide IMF connects the molecular clumps to the cosmological scale. The extraction of the IMF from observational data requires a thorough understanding of stellar evolution, the time-dependent stellar multiplicity, the stellar-dynamical evolution of dense stellar populations, and the structures, star formation histories, and chemical enrichment histories of galaxies. The IMF in galaxies, referred to as the galaxy-wide IMF (gwIMF), and the IMF in individual star-forming regions (the stellar IMF) need not be the same, although the former must be related to the latter. Observational surveys inform on whether star-forming regions provide evidence for the stellar IMF being a probability density distribution function. They may also indicate star formation to optimally follow an IMF shaped by the physical conditions of the star-forming gas. Both theoretical and observational evidence suggest a relationship between the initial mass function of brown dwarfs and that of stars. Late-type stars may arise from feedback-regulated fragmentation of molecular cloud filaments, which build up embedded clusters. In contrast, early-type stars form under more violent accretion and feedback-regulated conditions near the centers of these clusters. The integration over all star-forming molecular cloud clumps and their stellar IMFs in a galaxy via the IGIMF theory yields its gwIMF which sensitively depends on the physical properties of the molecular cloud clumps and the range of their masses that depends on the SFR of the galaxy.

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arXiv2024

Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video

In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate depth estimation through the brightness consistency constraint (BCC). To tackle these intertwined tasks, we propose a novel depth-centric learning framework that integrates the ASM model with the BCC constraint. Our key idea is that both ASM and BCC rely on a shared depth estimation network. This network simultaneously exploits adjacent dehazed frames to enhance depth estimation via BCC and uses the refined depth cues to more effectively remove haze through ASM. Additionally, we leverage a non-aligned clear video and its estimated depth to independently regularize the dehazing and depth estimation networks. This is achieved by designing two discriminator networks: $D_{MFIR}$ enhances high-frequency details in dehazed videos, and $D_{MDR}$ reduces the occurrence of black holes in low-texture regions. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art techniques in both video dehazing and depth estimation tasks, especially in real-world hazy scenes. Project page: https://fanjunkai1.github.io/projectpage/DCL/index.html.

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arXiv2024

DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain

In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth.

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arXiv2024

Failed supernova explosions increase the duration of star formation in globular clusters

Context. The duration of star formation (SF) in globular clusters (GCs) is an essential aspect for understanding their formation. Contrary to previous presumptions that all stars above 8 M explode as core-collapse supernovae (CCSNe), recent evidence suggests a more complex scenario. Aims. We analyse iron spread observations from 55 GCs to estimate the number of CCSNe explosions before SF termination, thereby determining the SF duration. This work for the first time takes the possibility of failed CCSNe into account, when estimating the SF duration. Methods. Two scenarios are considered: one where all stars explode as CCSNe and another where only stars below 20 M lead to CCSNe, as most CCSN models predict that no failed CCSNe happen below 20 M . Results. This establishes a lower ($\approx$ 3.5 Myr) and an upper ($\approx$ 10.5 Myr) limit for the duration of SF. Extending the findings of our previous paper, this study indicates a significant difference in SF duration based on CCSN outcomes, with failed CCSNe extending SF by up to a factor of three. Additionally, a new code is introduced to compute the SF duration for a given CCSN model. Conclusions. The extended SF has important implications on GC formation, including enhanced pollution from stellar winds and increased binary star encounters. These results underscore the need for a refined understanding of CCSNe in estimating SF durations and the formation of multiple stellar populations in GCs.

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arXiv2024

The Variation of the Galaxy-Wide IMF for Low-Mass Stars: Modeling and Observational Insights

The Stellar Initial Mass Function (IMF) characterizes the mass distribution of newly formed stars in various cosmic environments, serving as a fundamental assumption in astrophysical research. Recent findings challenge the prevalent notion of a universal and static IMF, proposing instead that the IMF's shape is contingent upon the star formation environment. In this study, we analyze the galaxy-wide variation of the IMF for low-mass stars in both dwarf and massive galaxies with diverse observational methods. Despite systematic discrepancies between different approaches, an IMF model with a metallicity-dependent slope for the low-mass stars aligns with the majority of observations, indicating a high degree of uniformity in the star formation processes across the universe. We also emphasize the need for a more comprehensive understanding of the variation of the low-mass IMF, considering measurement biases and factors beyond metallicity.

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arXiv2024

MambaLLIE: Implicit Retinex-Aware Low Light Enhancement with Global-then-Local State Space

Recent advances in low light image enhancement have been dominated by Retinex-based learning framework, leveraging convolutional neural networks (CNNs) and Transformers. However, the vanilla Retinex theory primarily addresses global illumination degradation and neglects local issues such as noise and blur in dark conditions. Moreover, CNNs and Transformers struggle to capture global degradation due to their limited receptive fields. While state space models (SSMs) have shown promise in the long-sequence modeling, they face challenges in combining local invariants and global context in visual data. In this paper, we introduce MambaLLIE, an implicit Retinex-aware low light enhancer featuring a global-then-local state space design. We first propose a Local-Enhanced State Space Module (LESSM) that incorporates an augmented local bias within a 2D selective scan mechanism, enhancing the original SSMs by preserving local 2D dependency. Additionally, an Implicit Retinex-aware Selective Kernel module (IRSK) dynamically selects features using spatially-varying operations, adapting to varying inputs through an adaptive kernel selection process. Our Global-then-Local State Space Block (GLSSB) integrates LESSM and IRSK with LayerNorm as its core. This design enables MambaLLIE to achieve comprehensive global long-range modeling and flexible local feature aggregation. Extensive experiments demonstrate that MambaLLIE significantly outperforms state-of-the-art CNN and Transformer-based methods. Project Page: https://mamballie.github.io/anon/

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arXiv2024

First detection of CO isotopologues in a high-redshift main-sequence galaxy: evidence of a top-heavy stellar initial mass function

Recent observations and theories have presented a strong challenge to the universality of the stellar initial mass function (IMF) in extreme environments. A notable example has been found for starburst conditions, where evidence favours a top-heavy IMF, i.e. there is a bias toward massive stars compared to the IMF that is responsible for the stellar mass function and elemental abundances observed in the Milky Way. Local starburst galaxies have star-formation rates similar to those in high-redshift main-sequence galaxies, which appear to dominate the stellar mass budget at early epochs. However, the IMF of high-redshift main-sequence galaxies is yet to be probed. Since $^{13}$CO and C$^{18}$O isotopologues are sensitive to the IMF, we have observed these lines towards four strongly-lensed high-redshift main-sequence galaxies using the Atacama Large Millimeter/sub-millimeter Array. Of our four targets, SDSS J0901+1814, at $z \approx 2.26$, is seen clearly in $^{13}$CO and C$^{18}$O, the first detection of CO isotopologues in the high-redshift main-sequence galaxy population. The observed $^{13}$C/$^{18}$O ratio, $2.4 \pm 0.8$, is significantly lower than that of local main-sequence galaxies. We estimate the isotope ratio, oxygen abundance and stellar mass using a series of chemical evolution models with varying star-formation histories and IMFs. All models favour an IMF that is more top-heavy than that of the Milky Way. Thus, as with starburst galaxies, main-sequence galaxies in the high-redshift Universe have a greater fraction of massive stars than a Milky-Way IMF would imply.

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