arXiv subjects

Zihao Wu

Publications and source records attributed to Zihao Wu.

At least 37 records · Page 2Linked to original sources

arXiv2026

JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: Photometrically Selected Galaxy Candidates at z > 8

We present a sample of 2081 sources selected at photometric redshift $z_{\mathrm{phot}} > 8$ across the JADES DR5 data release in GOODS-S and GOODS-N over a total area of 469 square arcmin. These sources range from $M_{\mathrm{UV}} = -22$ to $M_{\mathrm{UV}} = -16$, with 19 objects at $z_{\mathrm{phot}} > 14$. We estimate the UV slopes for the full sample from fits to the photometry and find evidence for a steepening of the relationship between the UV continuum slope and $M_{\mathrm{UV}}$ to higher redshifts, a result that differs from prior analyses of brighter samples in the literature. We provide evidence that over one quarter of our sources have evidence for being morphologically extended, with many galaxies showing multiple bright knots or clumps even out to $z \sim 13 - 14$, an indication of how galaxies at Cosmic Dawn are growing and evolving. We discuss JADES-GN+189.15982+62.28899, a GOODS-N F200W dropout galaxy at $z_{\mathrm{phot}} \sim 15 - 18$ which has been observed spectroscopically with JWST/NIRSpec in prism mode, resulting in a very low signal-to-noise spectrum that is consistent with the photometry and rules out a number of low-redshift solutions for the source. Finally, we use a subsample of 123 objects in our sample with spectroscopic redshifts to explore the usage of alternate fitting templates and a prescription for Ly-$α$ damping wing absorption, finding that both produce significant improvements to the estimated photometric redshifts.

astro-ph.GA↗

arXiv2026

A World Model of Radiologist Reading for Medical Image Representation Learning

Radiologist eye-tracking data provide a rich record of how experts search, compare, and accumulate evidence during image reading; yet, existing methods exploit this signal only partially, either as a static spatial prior or as an auxiliary prediction target decoupled from diagnosis. We propose GazeWorld, a medical imaging world model that treats the image as the world and the radiologist's fixation sequence as a trajectory through it. GazeWorld autoregressively predicts the latent representation of the next fixated patch from all previously visited ones, while a spatial-completion branch covers unvisited regions. At inference, GazeWorld generates a sequence of patch representations from the image alone without requiring real gaze data. Frozen GazeWorld features achieve state-of-the-art diagnostic accuracy across all nine supervised settings on CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax, as well as the highest zero-shot accuracy on all three benchmarks. On the GazeSearch benchmark, a generic decoder trained on the same frozen features outperforms the purpose-built LogitGaze-Med by over 16\% in ScanMatch and 22\% in SED, despite not being explicitly trained to predict gaze. GazeWorld demonstrates that modeling how experts read, not just what they conclude, offers a promising pretraining paradigm for medical imaging AI.

cs.CV↗

arXiv2026

Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability

Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained and evaluated under a single, unreported preprocessing pipeline. We formalize preprocessing choices as a counterfactual intervention space and show that EEG predictions are surprisingly unstable under this space: across six datasets spanning four paradigms, up to 42% of trial-level predictions flip when only the preprocessing changes, a variability that standard uncertainty methods do not explicitly quantify because they condition on a fixed preprocessing pipeline. We provide three tools to make this instability measurable, decomposable, and reducible. First, a Walsh-Hadamard decomposition of the 2^7 pipeline space reveals that sensitivity is near-additive in practice under the binary intervention design, enabling efficient step-by-step optimization. Second, we introduce Preprocessing Uncertainty (PU), a per-trial diagnostic that captures a dimension of instability complementary to model-based confidence. Third, we study Normalized Adaptive PGI (NA-PGI), a graph-structured regularizer that exploits the compositional structure of preprocessing interventions as one mitigation strategy with clear scope conditions.

cs.LG↗

arXiv2026

YOTOnet: Zero-Shot Cross-Domain Fault Diagnosis via Domain-Conditioned Mixture of Experts

Mechanical equipment forms the critical backbone of modern industrial production, yet domain shift severely limits the generalization of deep learning based fault diagnosis models across different equipment and operating conditions.Inspired by the success of foundation models in achieving zero-shotgeneralization, we propose YOTOnet (You Only Train Once), a novel architecture specifically designed for cross-domain fault diagnosis in mechanical equipment.YOTOnet comprises three core components: (1) a physics-aware Invariant Feature Distiller that extracts domain-agnostic representations using multi-scale dilated convolutions and FFT-based time-frequency fusion,(2) Domain-Conditioned Sparse Experts (DC-MoE) that adaptively route inputs to specialized processors via learned gating without external meta-data, and (3) a dual-head classification system with auxiliary supervision.Extensive validation on five public bearing datasets (CWRU, MFPT, XJTU,OTTAWA, HUST) through 30 cross-dataset protocols demonstrates the superiority of YOTOnet compared with other state-of-the-art methods. Critically, we observe a clear scaling effect-average test F1 improves from 0.5339(1 training dataset) to 0.705 (4 datasets), with a clear gain when moving from 3 to 4 datasets. These findings provide empirical evidence that foundation model principles can enable robust, train-once deployment for industrial fault diagnosis.

cs.LG↗

arXiv2026

Vibe Medicine: Redefining Biomedical Research Through Human-AI Co-Work

With the emergence of large language models (LLMs) and AI agent frameworks, the human-AI co-work paradigm known as Vibe Coding is changing how people code, making it more accessible and productive. In scientific research, where workflows are more complex and the burden of specialized labor limits independent researchers and those in low-resource areas, the potential impact is even greater, particularly in biomedicine, which involves heterogeneous data modalities and multi-step analytical pipelines. In this paper, we introduce Vibe Medicine, a co-work paradigm in which clinicians and researchers direct skill-augmented AI agents through natural language to execute complex, multi-step biomedical workflows, while retaining the role of research director who specifies objectives, reviews intermediate results, and makes domain-informed decisions. The enabling infrastructure consists of three layers: capable LLMs, agent frameworks such as OpenClaw and Hermes Agent, and the OpenClaw medical skills collection, which includes more than 1,000 curated skills from multiple open-source repositories. We analyze the architecture and skill categories of this collection across ten biomedical domains, and present case studies covering rare disease diagnosis, drug repurposing, and clinical trial design that demonstrate end-to-end workflows in practice. We also identify the principal risks, such as hallucination, data privacy, and over-reliance, and outline directions toward more reliable, trustworthy, and clinically integrated agent-assisted research that advances research and technological equity and reduces health care resource disparities.

cs.AI↗

arXiv2026

Intense and extended CIII] emission suggests a strong outflow in JADES-GS-z14-0

JWST has revealed an overabundance of very bright, blue galaxies at z>10, raising fundamental questions about how star formation and feedback operate at Cosmic Dawn. We present new JWST/NIRSpec MSA PRISM/CLEAR spectroscopy of JADES-GS-z14-0 (z=14.18) obtained with the JADES and OASIS programmes. While the rest-frame UV continuum flux level and shape are consistent between the two datasets, the OASIS spectrum shows a 10$σ$ detection of the CIII]$λ\lambda1907,1909$ emission line, with a luminosity three times higher than that measured in the JADES data. This difference is naturally explained by the offset in shutter placement between OASIS and JADES, implying that the CIII] emission is spatially displaced by $\sim400$ pc from the stellar continuum. The non-detection of CIII] in NIRCam medium-band imaging indicates that the emitting region is extended on scales $\gtrsim165$ pc, with a surface brightness below the detection threshold. Interpreting this diffuse, carbon-enriched gas as the result of ongoing or past outflows, we infer a mass outflow rate of $\dot{M}_{\rm out}\sim160~{\rm M_\odot\,yr^{-1}}$. We compare it with the star-formation rate (SFR) and derive a mass-loading factor of $η= \dot{M}_{\rm out}/{\rm SFR} = 4-15$, suggesting highly efficient feedback at very early times. Finally, we show that, if outflows are one of the mechanisms regulating star formation in JADES-GS-z14-0, the instantaneous star-formation efficiency in massive haloes is constrained to $ε_\star\lesssim0.08$. These results support a scenario in which outflows play a crucial role during the earliest phases of galaxy formation. Comparing our results with the current theoretical galaxy formation model, we conclude that a combination of moderate star-formation efficiency and reduced dust attenuation can account for the emergence of luminous galaxies at the highest redshifts.

astro-ph.GA↗

arXiv2026

The Way We Tally Becomes the Tale: the Impact of Selection Strategies on the Inferred Evolution of Little Red Dots Across Cosmic Time

Little Red Dots (LRDs) have emerged as a key population linked to early black hole growth, yet photometric selections have predominantly targeted only the most extreme red systems, thereby shaping our current understanding of this new population of objects. In this work, we deliberately explore a broad range of optical redness while enforcing stringent compactness and visual inspection to ensure robustness and minimize contamination. Leveraging the depth and multiwavelength coverage of the JWST Advanced Deep Extragalactic Survey (JADES) data in the GOODS-North and GOODS-South fields, we construct the largest photometric census of LRDs to date in these fields, comprising 412 sources over $z\approx2\text{--}11$ across $\approx349.6$ arcmin$^2$. We show that classic extreme color cuts isolate only a minor fraction of this population ($\lesssim25\%$), while the majority of LRDs span a broader, largely unexplored parameter space. We quantify how selection strategies impact UV and optical luminosity functions and number density evolution, finding that current demographic trends of LRDs are strongly driven by selection biases and further limited by incomplete identification at both high and low redshift. Spectroscopically confirmed LRDs reveal a continuous range of spectral shapes, consistent with varying Active Galactic Nucleus (AGN) and host contributions in agreement with recent findings. Our results demonstrate that commonly adopted, purity-driven selections bias current demographic constraints toward the most extreme systems, potentially misrepresenting the diversity and evolution of the LRD population. Accounting for these selection effects is essential for interpreting LRDs and their role in early black hole growth.

astro-ph.GA↗

arXiv2026

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems

This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that potentially combine both human and machine intelligences. The review traces this evolution from early connectionist models to state-of-the-art large language models, demonstrating how key innovations like transformer attention, foundation-model pre-training, and multi-agent architectures mirror neurobiological processes like cortical mechanisms, working memory, and episodic consolidation. We then discuss emerging physical substrates capable of breaking the von Neumann bottleneck to achieve brain-scale efficiency in silicon: memristive crossbars, in-memory compute arrays, and emerging quantum and photonic devices. There are four critical challenges at this intersection: 1) integrating spiking dynamics with foundation models, 2) maintaining lifelong plasticity without catastrophic forgetting, 3) unifying language with sensorimotor learning in embodied agents, and 4) enforcing ethical safeguards in advanced neuromorphic autonomous systems. This combined perspective across neuroscience, computation, and hardware offers an integrative agenda for in each of these fields.

q-bio.NC↗

arXiv2026

JADES Dark Horse: demonstrating high-multiplex observations with JWST/NIRSpec dense-shutter spectroscopy in the JADES Origins Field

We present JWST/NIRSpec dense-shutter spectroscopy (DSS). This novel observing strategy with the NIRSpec/MSA deliberately permits a high number of controlled spectral overlaps to reach extreme multiplex while retaining the low background of slit spectroscopy. In a single configuration over the JADES Origins Field, we opened shutters on all faint (mF444W$<$30 mag) z$_\mathrm{phot}>$3 candidates, prioritising emission-line science and rejecting only bright continuum sources. Using 33.6 and 35.8 ks on-source in G235M and G395M, we observed a single mask with $\sim$850 sources, obtaining spectroscopic redshifts for $\sim$540 galaxies over 2.5$<$z$<$8.9. The per-configuration target density in DSS mode is 4-5x higher than standard no- and low-overlap MSA strategies ($<$200 sources), with no loss in redshift precision or accuracy. Line-flux sensitivities are 30 percent lower at fixed exposure time, matching the expected increase in background noise, but the gain in survey speed is 5x in our setup, more than justifying the penalty. The measured line sensitivity exceeds NIRCam/WFSS by at least $\sim$5x ($\sim$25x in exposure time) at $λ\sim4\,μ$m, demonstrating that DSS is a compelling method to gain deep, wide-band spectra for large samples. Crucially, NIRSpec/MSA could deliver even higher target allocation densities than those used here. We derive H$α$-based SFRs, gas-phase metallicities (including a large sample suitable for strong-line calibrations), and identify rare mini-quenched galaxies and broadline AGN. DSS is immediately applicable wherever deep imaging enables robust pre-selection and astrometry, providing an efficient method to obtain large samples of faint emission-line galaxies, a compelling middle ground between the completeness of slitless surveys and the sensitivity and bandwidth of NIRSpec/MSA.

astro-ph.GA↗

arXiv2026

Rethinking Token Prediction: Tree-Structured Diffusion Language Model

Discrete diffusion language models have emerged as a competitive alternative to auto-regressive language models, but training them efficiently under limited parameter and memory budgets remains challenging. Modern architectures are predominantly based on a full-vocabulary token prediction layer, which accounts for a substantial fraction of model parameters (e.g., more than 20% in small scale DiT-style designs) and often dominates peak GPU memory usage. This leads to inefficient use of both parameters and memory under constrained training resources. To address this issue, we revisit the necessity of explicit full-vocabulary prediction, and instead exploit the inherent structure among tokens to build a tree-structured diffusion language model. Specifically, we model the diffusion process with intermediate latent states corresponding to a token's ancestor nodes in a pre-constructed vocabulary tree. This tree-structured factorization exponentially reduces the classification dimensionality, makes the prediction head negligible in size, and enables reallocation of parameters to deepen the attention blocks. Empirically, under the same parameter budget, our method reduces peak GPU memory usage by half while matching the perplexity performance of state-of-the-art discrete diffusion language models.

cs.CL↗

arXiv2026

The Rank and Gradient Lost in Non-stationarity: Sample Weight Decay for Mitigating Plasticity Loss in Reinforcement Learning

Deep reinforcement learning (RL) suffers from plasticity loss severely due to the nature of non-stationarity, which impairs the ability to adapt to new data and learn continually. Unfortunately, our understanding of how plasticity loss arises, dissipates, and can be dissolved remains limited to empirical findings, leaving the theoretical end underexplored.To address this gap, we study the plasticity loss problem from the theoretical perspective of network optimization. By formally characterizing the two culprit factors in online RL process: the non-stationarity of data distributions and the non-stationarity of targets induced by bootstrapping, our theory attributes the loss of plasticity to two mechanisms: the rank collapse of the Neural Tangent Kernel (NTK) Gram matrix and the $Θ(\frac{1}{k})$ decay of gradient magnitude. The first mechanism echoes prior empirical findings from the theoretical perspective and sheds light on the effects of existing methods, e.g., network reset, neuron recycle, and noise injection. Against this backdrop, we focus primarily on the second mechanism and aim to alleviate plasticity loss by addressing the gradient attenuation issue, which is orthogonal to existing methods. We propose Sample Weight Decay -- a lightweight method to restore gradient magnitude, as a general remedy to plasticity loss for deep RL methods based on experience replay. In experiments, we evaluate the efficacy of \methodName upon TD3, \myadded{Double DQN} and SAC with SimBa architecture in MuJoCo, \myadded{ALE} and DeepMind Control Suite tasks. The results demonstrate that \methodName effectively alleviates plasticity loss and consistently improves learning performance across various configurations of deep RL algorithms, UTD, network architectures, and environments, achieving SOTA performance on challenging DMC Humanoid tasks.

cs.LG↗

arXiv2026

Clumps in High-Redshift Galaxies: Mass Scaling and Radial Trends from JADES

Massive star-forming clumps are a prominent feature of high-redshift galaxies and are thought to trace gravitational fragmentation, feedback, and bulge growth in gas-rich disks. We present a statistical analysis of clumps in $\sim$3600 galaxies spanning $2 \lesssim z \lesssim 8$ from deep JWST/NIRCam imaging in the JADES GOODS--South field. Clumps are identified as residual features after subtracting smooth Sérsic profiles, enabling a uniform, rest-frame optical census of sub-galactic structure. We characterize their physical properties, size--mass relations, and spatial distributions to constrain models of sub-galactic structure formation and evolution. We find that clumps in our sample are typically low-mass ($10^{\sim7-8}M_\odot$), actively star-forming, and show diverse gas-phase metallicity, dust attenuation, and stellar population properties. Their sizes and average pairwise separations increase with cosmic time (toward lower redshift), consistent with inside-out disk growth. The clump mass function follows a power law with slope $α= -1.50_{-0.17}^{+0.19}$, consistent with fragmentation in turbulent disks. We find a deficit of relatively young clumps near galaxy centers and a radial transition in the size--mass relation: outer clumps exhibit steeper, near-virial slopes ($R_{\rm e}\propto M_*^{\sim 0.3}$), while inner clumps follow flatter trends ($R_{\rm e}\propto M_*^{\sim 0.2}$), consistent with structural evolution via migration or disruption. These results provide new constraints on the formation, survival, and dynamical evolution of clumps, highlighting their role in shaping galaxy morphology during the peak of cosmic star formation.

astro-ph.GA↗

arXiv2026

An OASIS of Lyman-$α$ within a neutral intergalactic desert: reaffirmed line and blue continuum reveal efficient ionising agents at $z = 13$

$\require{mediawiki-texvc}$Galaxy assembly was already well underway in the first 400 Myr of cosmic time, as recently revealed by JWST. However, the contribution of these early galaxies to cosmic reionisation remains uncertain. Here we present new JWST/NIRSpec observations of GS-z13-1-LA obtained as part of the OASIS and JADES programmes, whose combined deep (56 h) NIRSpec/PRISM spectrum confirms the Lyman-$\mathrmα$ line detection and blue UV continuum at redshift $z = 13.1$ presented in a previous work. The measured Lyman-$\mathrmα$ emission (rest-frame equivalent width of $66_{-9}^{+10}\,Å$) and steep continuum slope ($β_\text{UV} \approx -3$) point towards GS-z13-1-LA hosting a remarkably hot and powerful ionising source, and allow at most a modest contribution from the nebular continuum. The steep turnover of the continuum is still present, but less pronounced in the new OASIS spectrum. Combined, this implies that ionising photons may escape GS-z13-1-LA at a sufficient rate to weaken the other, still undetected UV lines, and to lead the formation of a small ionised bubble ($R_\text{ion} \approx 0.2\,\mathrm{pMpc}$). A yet larger bubble could alleviate the required ionising production efficiency of GS-z13-1-LA from $ξ_\mathrm{ion} \approx 10^{26.4}\,\mathrm{Hz\,erg^{-1}}$ down to $\approx 10^{25.9}\,\mathrm{Hz\,erg^{-1}}$, still extremely high but more readily reconcilable with stellar models. In turn, this would require a notable overdensity of galaxies with highly efficient ionising capabilities, a scenario for which tentative evidence is found in the form of 16 nearby photometric candidates and one spectroscopically confirmed source, JADES-GS-z13-0. The new OASIS observations therefore confirm the overall picture of GS-z13-1-LA as an early beacon of reionisation, providing compelling evidence for its start only 330 Myr after the Big Bang.

astro-ph.GA↗

arXiv2026

Out of oxygen: Extremely metal-poor galaxy candidates identified at $2.5 < z < 6.5$ with deep JADES medium-band imaging

JWST is beginning to uncover a population of extremely metal-poor galaxies (EMPGs, $Z < 1\%~\mathrm{Z}_\odot$) at $z > 3$, mostly through serendipitous NIRSpec discoveries and blind slitless spectroscopy. To accelerate our understanding of pristine star formation, we further develop a methodology to identify EMPG candidates from photometry, using the extensive deep medium-band imaging from JADES. Our EMPG candidates at $2.5 < z < 6.5$ exhibit strong photometric boosts by H$α$, yet correspondingly weak boosts by [O III] + H$β$, likely indicating extremely low metallicity to explain their lack of [O III] emission. We further demand our EMPG candidates to have strong Balmer jumps, as revealed by medium-band imaging, to ensure that they are young starbursts, as opposed to broad-line AGN/LRDs, though contamination by dusty/dense-gas starbursts and highly-obscured AGN remains a concern. SED-fitting with near-pristine models (${\sim}0.1$-$1\%~\mathrm{Z}_\odot$) indicates that our 22 EMPG candidates are low-mass (median $M_* \approx 10^{6.7}~\mathrm{M}_\odot$), faint dwarf galaxies ($M_\mathrm{UV} \approx -16.6$), with high ionizing photon production efficiencies ($\log\, (ξ_\mathrm{ion, obs}/\mathrm{(Hz\ erg^{-1})}) \approx 26.0$). Hence these are plausible sites of near-pristine star formation, comprising ${\sim}0.04$-$0.6\%$ of $2.5 < z < 6.5$ galaxies at $-19 < M_\mathrm{UV} < -16$. We discuss this extremely metal-poor extension to the mass-metallicity relation. We forecast that deep (${\sim}28$ h) NIRCam slitless spectroscopy can identify bright EMPGs through strong H$β$ but lack of [O III] emission, or secure the redshifts of fainter systems through H$α$ detections. Highly-multiplexed NIRSpec spectroscopy offers an alternate route to discovering the faintest pristine galaxies out to $z=10$, without requiring deep medium-band/MIRI imaging to identify secure candidates.

astro-ph.GA↗

arXiv2026

Filling the Gap in Cluster Evolution: JWST's Glimpse into a Young, Star-Forming Cluster at Cosmic Noon

We present a detailed study of HUDFJ0332.4-2746.6 (HUDF46), a $z \approx 1.84$ overdensity in the Hubble Ultra Deep Field, previously identified with HST as a proto-cluster. JWST/NIRISS spectroscopy expands its confirmed membership from 18 to 37 galaxies, while deep HST/ACS, JWST/NIRCam, and JWST/MIRI imaging provide a comprehensive multiwavelength view from the rest-frame UV to the mid-infrared. This dataset probes the population across three dex in stellar mass ($M_\bigstar \approx 10^{7.5\text{--}10.5}\,M_\odot$), delivering the first direct view of a young cluster down to such low-$M_\bigstar$ at $z\gtrsim1$. Assuming virialization, we derive a velocity dispersion of $σ\approx 670\pm 91\,\mathrm{km\,s^{-1}}$ and a halo mass of $M_{200} \approx (1.2\pm0.2) \times 10^{14}\,M_\odot$, in agreement with X-ray constraints from deep {\it Chandra} data. Despite residing in a massive halo likely in the hot-halo regime, the population is overwhelmingly star-forming, with no established red sequence and no extended X-ray emission from a hot intracluster medium. HUDF46 members have stellar and structural properties nearly indistinguishable from coeval field galaxies, and the structure hosts only one AGN candidate, found in its brightest galaxy, which lies at the cluster center. Overall, HUDF46 appears to be in a transitional phase prior to the onset of environmental quenching, making its galaxy population a key benchmark for tracing the processes that will later build a passive population and shape the assembly of massive clusters at later cosmic times.

astro-ph.GA↗

arXiv2026

Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning

Deep reinforcement learning (RL) agents frequently suffer from neuronal activity loss, which impairs their ability to adapt to new data and learn continually. A common method to quantify and address this issue is the tau-dormant neuron ratio, which uses activation statistics to measure the expressive ability of neurons. While effective for simple MLP-based agents, this approach loses statistical power in more complex architectures. To address this, we argue that in advanced RL agents, maintaining a neuron's learning capacity, its ability to adapt via gradient updates, is more critical than preserving its expressive ability. Based on this insight, we shift the statistical objective from activations to gradients, and introduce GraMa (Gradient Magnitude Neural Activity Metric), a lightweight, architecture-agnostic metric for quantifying neuron-level learning capacity. We show that GraMa effectively reveals persistent neuron inactivity across diverse architectures, including residual networks, diffusion models, and agents with varied activation functions. Moreover, resetting neurons guided by GraMa (ReGraMa) consistently improves learning performance across multiple deep RL algorithms and benchmarks, such as MuJoCo and the DeepMind Control Suite.

cs.LG↗

arXiv2026

JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: NIRCam Imaging in GOODS-S and GOODS-N

We present the Near Infrared Camera (NIRCam) imaging products of the fifth data release (DR5) of the James Webb Space Telescope (JWST) Advanced Deep Extragalactic Survey (JADES). The JADES survey is one of the most ambitious programs yet conducted on JWST, producing deep infrared imaging and multiobject spectroscopy on the GOODS-S and GOODS-N extragalactic deep fields in order to explore galaxies to the earliest epoch. Here we describe the NIRCam data reduction procedures that result in deep and well-characterized mosaics in up to 18 filters covering 469 arcmin$^2$, with 250 arcmin$^2$ having at least 8 filters of coverage. This release contains the full NIRCam imaging of JADES, over 800 JWST mission hours, as well as co-reductions of 19 other programs in these two premier deep fields. We perform detailed tests on the final data products, thereby characterizing the photometric properties, point-spread function, and astrometric alignment. We release mosaics for individual programs (or epochs, depending on scheduling) and the mosaics combining data from all programs in order to facilitate photometric variability studies and the deepest possible photometry.

astro-ph.GA↗

arXiv2026

JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: MIRI Coordinated Parallels in GOODS-S and GOODS-N

Medium to ultra-deep mid-infrared imaging surveys with the James Webb Space Telescope (JWST)'s Mid-Infrared Instrument (MIRI) are reframing our view of the early Universe, from the emergence of ultra-red dusty and quiescent galaxies to the epoch of reionization to the first galaxies. Here we present the MIRI coordinated parallels component of the JADES program, which obtained ultra-deep (155 ks) imaging at $7.7 μ$m over $\sim10$ arcmin$^2$ as well as medium depth ($\sim5-15$ ks) imaging at $7.7, 12.8$, and $15 μ$m over $\sim36$, 25, and 22 arcmin$^2$, respectively, in the GOODS-S and GOODS-N fields. This paper describes the data reduction, which combines the official JWST Calibration Pipeline with custom steps to optimize flagging of warm/hot pixels and optimize background subtraction. We further introduce a new step to address artifacts caused by persistence from saturating sources. The final, fully reduced JADES/MIRI mosaics are being released as part of JADES Data Release 5, along with prior-based forced photometry using NIRCam detection images, providing critical rest-frame near-infrared and optical constraints on early galaxy populations.

astro-ph.GA↗