arXiv subjects

Zewei Wu

Publications and source records attributed to Zewei Wu.

6 recordsLinked to original sources

arXiv2025

Effect of ionizing photon escape fraction in faint galaxies on modeling reionization history of the universe

We present model calculations of the reionization history of hydrogen using star formation histories, computed with a galaxy formation model which reproduces properties of local dwarf galaxies and UV luminosity functions of galaxies at $z=5-16$. We use the ionizing photon density functions predicted by the model along with different models for the escape fraction of ionizing photons, $f_{\rm esc}$, to study the effects of ionizing photons from faint galaxies and different assumptions about $f_{\rm esc}$ on the evolution of hydrogen ionized fraction with redshift, $Q_{\rm HII}(z)$. We show that accounting for the contribution of faint galaxies with UV luminosities $M_{1500}>-13$, and with a constant ionizing photon escape fraction of $f_{\rm esc}=0.1$ results in the hydrogen reionization history consistent with all current observational constraints. Comparing results of the $f_{\rm esc}=0.1$ model and two alternative models shows that the model with a strong luminosity dependence of $f_{\rm esc}$, which assigns high $f_{\rm esc}$ to faint galaxies, results in early reionization inconsistent with observational constraints. However, the model in which $f_{\rm esc}$ follows a universal redshift-independent correlation with the recent maximum specific star formation rate, motivated by the results of the SPHINX galaxy formation simulation, results in the reionization history in good agreement with existing observational constraints, even though this model produces a sizeable ionized hydrogen fraction of $Q_{\rm HII}\approx 0.15-0.2$ at redshifts $z=8-12$. Our results show that the relative contribution of faint dwarf galaxies to reionization depends sensitively on assumptions about the escape fraction for galaxies of different luminosities, and that this is the main source of uncertainty in modeling hydrogen reionization.

astro-ph.GA↗

arXiv2025

Multi-tracklet Tracking for Generic Targets with Adaptive Detection Clustering

Tracking specific targets, such as pedestrians and vehicles, has been the focus of recent vision-based multitarget tracking studies. However, in some real-world scenarios, unseen categories often challenge existing methods due to low-confidence detections, weak motion and appearance constraints, and long-term occlusions. To address these issues, this article proposes a tracklet-enhanced tracker called Multi-Tracklet Tracking (MTT) that integrates flexible tracklet generation into a multi-tracklet association framework. This framework first adaptively clusters the detection results according to their short-term spatio-temporal correlation into robust tracklets and then estimates the best tracklet partitions using multiple clues, such as location and appearance over time to mitigate error propagation in long-term association. Finally, extensive experiments on the benchmark for generic multiple object tracking demonstrate the competitiveness of the proposed framework.

cs.CV↗

arXiv2025

Head Anchor Enhanced Detection and Association for Crowded Pedestrian Tracking

Visual pedestrian tracking represents a promising research field, with extensive applications in intelligent surveillance, behavior analysis, and human-computer interaction. However, real-world applications face significant occlusion challenges. When multiple pedestrians interact or overlap, the loss of target features severely compromises the tracker's ability to maintain stable trajectories. Traditional tracking methods, which typically rely on full-body bounding box features extracted from {Re-ID} models and linear constant-velocity motion assumptions, often struggle in severe occlusion scenarios. To address these limitations, this work proposes an enhanced tracking framework that leverages richer feature representations and a more robust motion model. Specifically, the proposed method incorporates detection features from both the regression and classification branches of an object detector, embedding spatial and positional information directly into the feature representations. To further mitigate occlusion challenges, a head keypoint detection model is introduced, as the head is less prone to occlusion compared to the full body. In terms of motion modeling, we propose an iterative Kalman filtering approach designed to align with modern detector assumptions, integrating 3D priors to better complete motion trajectories in complex scenes. By combining these advancements in appearance and motion modeling, the proposed method offers a more robust solution for multi-object tracking in crowded environments where occlusions are prevalent.

cs.CV↗

arXiv2024

On the contribution of dwarf galaxies to reionization of the Universe

We present estimates of the ultraviolet (UV) and Lyman continuum flux density contributed by galaxies of luminosities from $M_{\rm UV}\approx -25$ to $M_{\rm UV}=-4$ at redshifts $5\leq z\leq 10$ using a galaxy formation model that reproduces properties of local dwarf galaxies down to the luminosities of the ultra-faint satellites. We characterize the UV luminosity function (LF) of galaxies and their abundance as a function of the ionizing photon emission rate predicted by our model and present accurate fitting functions describing them. Although the slope of the LF becomes gradually shallower with decreasing luminosity due to feedback-driven outflows, the UV LF predicted by the model remains quite steep at the luminosities $M_{\rm UV}\lesssim -14$. After reionization, the UV LF flattens at $M_{\rm UV}\gtrsim -12$ due to UV heating of intergalactic gas. However, before reionization, the slope of the LF remains steep and approximately constant from $M_{\rm UV}\approx -14$ to $M_{\rm UV}=-4$. We show that for a constant ionizing photon escape fraction the contribution of faint galaxies with $M_{\rm UV}>-14$ to the UV flux and ionizing photon budget is $\approx 40-60\%$ at $z>7$ and decreases to $\approx 20\%$ at $z=6$. Before reionization, even ultra-faint galaxies of $M_{\rm UV}>-10$ contribute $\approx 10-25\%$ of ionizing photons. If the escape fraction increases strongly for fainter galaxies, the contribution of $M_{\rm UV}>-14$ galaxies before reionization increases to $\approx 60-75\%$. Our results imply that dwarf galaxies fainter than $M_{\rm UV}=-14$, beyond the James Webb Space Telescope limit, contribute significantly to the UV flux density and ionizing photon budget before reionization alleviating requirements on the escape fraction of Lyman continuum photons.

astro-ph.GA↗

arXiv2023

Densities and mass assembly histories of the Milky Way satellites are not a challenge to $Λ$CDM

We use the \texttt{GRUMPY} galaxy formation model based on a suite of zoom-in, high-resolution, dissipationless $Λ$ Cold Dark Matter ($Λ$CDM) simulations of the Milky Way (MW) sized haloes to examine total matter density within the half-mass radius of stellar distribution, $ρ_{\rm tot}( 10$, at these early epochs halos that survive as satellites to $z=0$ are located many virial radii away from the MW progenitors and thus do not have a chance to merge with it. Our results show that neither the densities estimated in observed Milky Way satellites nor their mass assembly histories pose a challenge to the $Λ$CDM model. In fact, the broad agreement between density trends with the stellar mass of the observed and model galaxies can be considered as yet another success of the model.

astro-ph.GA↗

arXiv2022

Nonmonontone submodular maximization under routing constraints

In machine learning and big data, the optimization objectives based on set-cover, entropy, diversity, influence, feature selection, etc. are commonly modeled as submodular functions. Submodular (function) maximization is generally NP-hard, even in the absence of constraints. Recently, submodular maximization has been successfully investigated for the settings where the objective function is monotone or the constraint is computation-tractable. However, maximizing nonmonotone submodular function with complex constraints is not yet well-understood. In this paper, we consider the nonmonotone submodular maximization with a cost budget or feasibility constraint (especially from route planning) that is generally NP-hard to evaluate. Such a problem is common for machine learning, big data, and robotics. This problem is NP-hard, and on top of that, its constraint evaluation is also NP-hard, which adds an additional layer of complexity. So far, few studies have been devoted to proposing effective solutions, making this problem currently unclear. In this paper, we first propose an iterated greedy algorithm, which yields an approximation solution. Then we develop the proof machinery that shows our algorithm is a bi-criterion approximation algorithm: it can achieve a constant-factor approximation to the optimal algorithm, while keeping the over-budget tightly bounded. We also explore practical considerations of achieving a trade-off between time complexity and over-budget. Finally, we conduct numeric experiments on two concrete examples, and show our design's efficacy in practical settings.

cs.DS↗