arXiv subjects

Shouyi Wang

Publications and source records attributed to Shouyi Wang.

9 recordsLinked to original sources

arXiv2026

An emerging baryon cycle in a galaxy 500 million years after the Big Bang

The emergence of stellar feedback as a regulator of galaxy growth marks a fundamental transition in cosmic history. At early times, rapid gas accretion and collapse may induce intense star formation before feedback becomes effective, producing feedback-free starbursts. When and how such bursts subsequently develop into self-regulated baryon cycles remain observationally unknown. Here we show that Gz9p3, a merging galaxy at $z=9.311$, is caught in this transition only 500 million years after the Big Bang. Deep JWST spectroscopy reveals a substantial neutral-gas reservoir along its merger-driven tidal structure and a multiphase outflow. Fine-structure absorption provides the first direct measurement of the electron density of the cool outflowing gas at high redshift ($\approx\,17\,{\rm cm^{-3}}$), yielding a mass-loading factor among the highest yet measured for galaxies of comparable stellar mass. The emergence of such efficient feedback after an intense burst is consistent with the delayed onset of feedback expected in feedback-free starburst models. The cool outflowing gas is unlikely to escape the host halo, implying that much of this metal-enriched material may remain available for future recycling through the circumgalactic medium. Gz9p3 therefore provides an early view of a baryon cycle being established through the interplay of merger-driven gas redistribution, bursty star formation and stellar feedback, suggesting that feedback-regulated recycling was already shaping galaxy growth during the epoch of reionization.

astro-ph.GA↗

arXiv2026

Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis

Air pollution and climate-related stressors are increasingly important concerns for respiratory health, especially in settings with unequal environmental exposure and healthcare capacity. This study evaluates an interpretable machine learning framework for predicting respiratory disease rates and air-quality status using structured country-level weekly data. Two supervised learning tasks were considered: regression of respiratory disease rate per 100,000 population and binary classification of air-quality status. Nine regression models and nine classification models were compared using nested cross-validation. Model interpretation was conducted using SHAP values, and subgroup analysis was performed across income levels and geographic regions. The results showed that PM2.5 concentration was the dominant predictor of respiratory disease rate, with linear and regularized linear models achieving the strongest regression performance. For air-quality classification, models achieved high balanced accuracy when PM2.5 was included, but performance decreased substantially when PM2.5 was removed, indicating strong dependence on pollutant-related information. SHAP analysis showed that, without PM2.5, socioeconomic and meteorological variables such as GDP per capita, precipitation, and healthcare access became more influential. Subgroup analysis showed similar aggregate regression error across income groups, but PM2.5 contributed more strongly to predictions in lower-middle-income countries. These results show that model accuracy alone is not sufficient for climate-health prediction. Interpretable models can help identify dominant pollution-related signals, test whether results depend on key pollutant variables, and show whether prediction patterns differ across socioeconomic groups.

cs.LG↗

arXiv2026

A Rare Eddington-Limited, Heavily Obscured Low-Mass Active Galactic Nucleus Likely Triggered by a Galaxy Merger

We report a detailed analysis of GAMA 376183, a powerful, heavily obscured active galactic nucleus (AGN) hosted by a low-mass galaxy ($M_\star \approx 10^{10}~M_{\odot}$) likely experiencing a galaxy merger. The source was initially identified due to its remarkably strong [Ne v] $\lambda3426$ emission, exhibiting a rest-frame equivalent width (EW) of $\approx 48$ A. We present $\sim100$ ks Nuclear Spectroscopic Telescope Array follow-up observations, confirming its heavily obscured nature with a column density (in $\mathrm{cm^{-2}}$) of $\log N_\mathrm{H} = 23.3^{+0.4}_{-1.2}$ and an intrinsic $2$--$10$ keV luminosity (in $\mathrm{erg~s^{-1}}$) of $\log L_\mathrm{X,int} = 42.92^{+0.24}_{-0.20}$. GAMA 376183 thus represents one of the few known heavily obscured AGNs in low-mass galaxies. Its estimated Eddington ratio is $\lambda_\mathrm{Edd}\approx0.8$, indicative of rapid black-hole growth. High-resolution optical images reveal a disturbed, likely merging morphology, while its multiwavelength spectral energy distribution indicates a recent starburst in its host galaxy. These pieces of evidence suggest that the ongoing merger has triggered both the heavily obscured, Eddington-limited accretion and the starburst, making GAMA 376183 a rare observed case in low-mass galaxies. Overall, this unique source demonstrates that (i) [Ne v] can help identify heavily obscured low-mass AGNs, and (ii) the merger-driven coevolution framework established for massive galaxies may also extend to low-mass galaxies.

astro-ph.GA↗

arXiv2026

Revisiting the Claim for a Direct-Collapse Black Hole in UHZ1 at $z=10.05$

We reassess the direct collapse black hole (DCBH) interpretation of UHZ1 (UNCOVER-26185), a gravitationally lensed galaxy at $z_\mathrm{spec}=10.054$. That interpretation rests on a hard ($2-7$ keV) X-ray excess detected with Chandra, attributed to a Compton-thick AGN with an inferred $2-10$ keV luminosity of $L_\mathrm{X,int}\sim10^{46}~\mathrm{erg~s^{-1}}$ (Bogdan et al. 2024). The resulting extreme X-ray to rest-frame optical-IR ratio was taken as the hallmark signature of an "outsize black hole galaxy" at cosmic dawn. We analyse the full 2.2 Ms Chandra imaging dataset -- including 0.95 Ms of unpublished observations -- and present new JWST/MIRI photometry at $\lambda_\mathrm{obs}>5~\mu\mathrm{m}$. Across the full range of plausible Chandra data reductions, the $2-7$ keV excess at the position of UHZ1 reaches a significance of only $2.0-2.9\sigma$; the originally reported $4.2-4.4\sigma$ detection is sensitive to the specific astrometric alignment adopted and is not robustly reproducible. Moreover, the hard X-ray signal does not grow with the additional exposure, contrary to expectations for a steady source, indicating that any excess is not persistent. UHZ1 is also undetected in all nine MIRI imaging bands. Fitting red/obscured AGN SED templates to the tightest MIRI upper limit, we constrain the bolometric luminosity of any buried AGN to $L_\mathrm{bol}<1.3\times10^{45}~\mathrm{erg~s^{-1}}$. These conclusions are further supported by independent JWST spectroscopy (Alvarez-Marquez et al. 2026), which reveals no AGN signatures in the rest-frame UV or optical. Taken together, the multiwavelength data paint a consistent picture of UHZ1 as a low-mass, metal-poor, star-forming galaxy in the early Universe, with no compelling evidence for a luminous obscured AGN, regardless of its proposed formation channel.

astro-ph.GA↗

arXiv2025

A Roadmap for Climate-Relevant Robotics Research

Climate change is one of the defining challenges of the 21st century, and many in the robotics community are looking for ways to contribute. This paper presents a roadmap for climate-relevant robotics research, identifying high-impact opportunities for collaboration between roboticists and experts across climate domains such as energy, the built environment, transportation, industry, land use, and Earth sciences. These applications include problems such as energy systems optimization, construction, precision agriculture, building envelope retrofits, autonomous trucking, and large-scale environmental monitoring. Critically, we include opportunities to apply not only physical robots but also the broader robotics toolkit - including planning, perception, control, and estimation algorithms - to climate-relevant problems. A central goal of this roadmap is to inspire new research directions and collaboration by highlighting specific, actionable problems at the intersection of robotics and climate. This work represents a collaboration between robotics researchers and domain experts in various climate disciplines, and it serves as an invitation to the robotics community to bring their expertise to bear on urgent climate priorities.

cs.RO↗

arXiv2025

NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

This paper reviews the NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images. This challenge received a wide range of impressive solutions, which are developed and evaluated using our collected real-world Raindrop Clarity dataset. Unlike existing deraining datasets, our Raindrop Clarity dataset is more diverse and challenging in degradation types and contents, which includes day raindrop-focused, day background-focused, night raindrop-focused, and night background-focused degradations. This dataset is divided into three subsets for competition: 14,139 images for training, 240 images for validation, and 731 images for testing. The primary objective of this challenge is to establish a new and powerful benchmark for the task of removing raindrops under varying lighting and focus conditions. There are a total of 361 participants in the competition, and 32 teams submitting valid solutions and fact sheets for the final testing phase. These submissions achieved state-of-the-art (SOTA) performance on the Raindrop Clarity dataset. The project can be found at https://lixinustc.github.io/CVPR-NTIRE2025-RainDrop-Competition.github.io/.

cs.CV↗

arXiv2024

The Remarkable X-ray Spectra and Variability of the Ultraluminous Weak-Line Quasar SDSS J1521+5202

We present a focused X-ray and multiwavelength study of the ultraluminous weak-line quasar (WLQ) SDSS J1521+5202, one of the few X-ray weak WLQs that is amenable to basic X-ray spectral and variability investigations. J1521+5202 shows striking X-ray variability during 2006--2023, by up to a factor of $\approx 32$ in 0.5--2 keV flux, and our new 2023 Chandra observation caught it in its brightest X-ray flux state to date. Concurrent infrared/optical observations show only mild variability. The 2023 Chandra spectrum can be acceptably described by a power law with intrinsic X-ray absorption, and it reveals a nominal intrinsic level of X-ray emission relative to its optical/ultraviolet emission. In contrast, an earlier Chandra spectrum from 2013 shows apparent spectral complexity that is not well fit by a variety of models, including ionized-absorption or standard Compton-reflection models. Overall, the observations are consistent with the thick-disk plus outflow model previously advanced for WLQs, where a nominal level of underlying X-ray emission plus variable absorption lead to the remarkable observed X-ray variability. In the case of J1521+5202 it appears likely that the outflow, and not the thick disk itself, lies along our line-of-sight and causes the X-ray absorption.

astro-ph.HE↗

arXiv2021

Boost-S: Gradient Boosted Trees for Spatial Data and Its Application to FDG-PET Imaging Data

Boosting Trees are one of the most successful statistical learning approaches that involve sequentially growing an ensemble of simple regression trees (i.e., "weak learners"). However, gradient boosted trees are not yet available for spatially correlated data. This paper proposes a new gradient Boosted Trees algorithm for Spatial Data (Boost-S) with covariate information. Boost-S integrates the spatial correlation structure into the classical framework of gradient boosted trees. Each tree is grown by solving a regularized optimization problem, where the objective function involves two penalty terms on tree complexity and takes into account the underlying spatial correlation. A computationally-efficient algorithm is proposed to obtain the ensemble trees. The proposed Boost-S is applied to the spatially-correlated FDG-PET (fluorodeoxyglucose-positron emission tomography) imaging data collected during cancer chemoradiotherapy. Our numerical investigations successfully demonstrate the advantages of the proposed Boost-S over existing approaches for this particular application.

stat.AP↗

arXiv2021

A Statistician Teaches Deep Learning

Deep learning (DL) has gained much attention and become increasingly popular in modern data science. Computer scientists led the way in developing deep learning techniques, so the ideas and perspectives can seem alien to statisticians. Nonetheless, it is important that statisticians become involved -- many of our students need this expertise for their careers. In this paper, developed as part of a program on DL held at the Statistical and Applied Mathematical Sciences Institute, we address this culture gap and provide tips on how to teach deep learning to statistics graduate students. After some background, we list ways in which DL and statistical perspectives differ, provide a recommended syllabus that evolved from teaching two iterations of a DL graduate course, offer examples of suggested homework assignments, give an annotated list of teaching resources, and discuss DL in the context of two research areas.

stat.ML↗