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Yue Shen

Publications and source records attributed to Yue Shen.

At least 19 recordsLinked to original sources
arXiv2026

The Roman eXtreme Deep Field (RXDF)

The Roman eXtreme Deep Field (RXDF) program is one of the five General Astrophysics Survey (GAS) programs approved for observing time with the Nancy Grace Roman Space Telescope in Cycles 1 and 2. It has been allocated 386.41 hours to carry out an imaging survey to AB = 30 mag (5-sigma) over ~140x larger area than the Hubble eXtreme Deep Field (HXDF) full-depth area (ACS+WFC3/IR). The RXDF will cover the full Roman wavelength range with 7 bands, reaching AB = 30 mag in RZYJH, 29 mag in F, and 28 mag in K, over a full-depth area of 678.75 arcmin^2 embedded in a total area of 1,243 arcmin^2, and far exceeding the depths of the Roman Core Community Surveys (CCS). The RXDF is within the Euclid Ultra Deep Field (EUDF) near the North Ecliptic Pole (NEP), a strategic long-term field for generational space facilities, with a wealth of multi-wavelength data including extensive coverage from the James Webb Space Telescope (JWST) NEXUS Treasury program. The observations will cover 3 epochs at a 1-year cadence, each epoch divided into 3 sub-epochs ~10 days apart, enabling time-domain studies on time baselines from ~10 days to over ~2 years. The RXDF is uniquely positioned to address critical questions in reionization, large scale structure (LSS), growth of supermassive black holes (SMBHs), little red dots (LRDs), and high-z supernovae (SNe); the volumes probed by HST+JWST are too small at these extreme depths, and even the deepest CCS tiers are too shallow. In addition to our key objectives, a wealth of additional science will be enabled by engaging the community with our rapidly released datasets, revolutionizing a wide range of science for a lasting legacy. This short document, which is converted from the approved RXDF proposal, aims to provide the community with a summary of the program.

astro-ph.GA↗
arXiv2026

NEXUS: Transient Searches and First Results from Year One Observations

We describe ongoing efforts of high-redshift transient searches using multi-epoch NIRCam imaging (F200W+F444W) and NIRSpec MSA/PRISM spectroscopy from the NEXUS JWST multi-cycle Treasury program targeting the north ecliptic pole region. The transient search area covers $\sim 61.5\,{\rm arcmin^2}$ between the reference epoch and each subsequent NEXUS-Deep epoch at a cadence of $\sim2$~months. In the first year of observations from NEXUS, we detect 68 robust transients, with the host photometric redshift distribution declining rapidly at $z>2$ but extending to $z_{\rm phot}\approx 6$. In addition, we obtained secure spectroscopic redshifts for 37 transients ($\sim54\%$) from NIRSpec/PRISM and NIRCam/WFSS, with seventeen at $1 < z < 2$, eight at $2 < z < 3$, two at $3 < z < 4$, and one tentative host association at z = 6.151. Overall, the NEXUS program recovers observed supernova (SN) rates broadly consistent with other SN search programs with JWST. While NEXUS achieves the highest transient detection efficiency, 6.6 SNe per imaging hr ($2.6\times$ COSMOS-SN and $26\times$ JADES-SN), the limited filter coverage (F200W+F444W only) limits robust identification and classification of high-$z$ SNe for deep spectroscopic follow-up. We describe the details of the data reduction and transient detection pipeline, and report the Year-1 transient sample along with their light curves, available MSA spectroscopy, host association, and the raw detection rate. We also describe and release a new PSF photometry package that properly accounts for correlated pixel noise from combining drizzled images.

astro-ph.HE↗
arXiv2026

Can You Say This for Me? Speaking Up by Proxy in Co-Located Discussion

Equal participation in co-located discussion is important for effective collaboration, yet people often hold back when they anticipate negative interpersonal or professional consequences, especially when raising a point requires voicing it themselves. We present SecondVoice, a mixed-reality system that enables people to speak up through an embodied virtual proxy. By separating what is said from who says it, SecondVoice brings hesitant points into the live spoken discussion without putting the speaker on the spot. Using a private overlay, users specify their intent through a structured specification process rather than composing a full utterance. The system reformulates the input and voices it into the conversation through the proxy. We characterize a design space of participation channels under social risk. In a preliminary within-subject study (N = 16), we compare the complete SecondVoice system with an anonymous text-board channel across two group discussion tasks. Half of participants reported using SecondVoice for a point they did not say aloud, compared with 18.8% for the text board. Proxy-delivered points entered the spoken floor and were followed by multi-turn group engagement, which we did not observe after text-board posts. Participants described the channel as situationally valuable but identified tradeoffs around timing, ownership, and trust in reformulation.

cs.AI↗
arXiv2026

Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal

Adverse weather causes diverse and complex image degradations, severely compromising the reliability of computer vision systems. Existing all-in-one restoration models attempt to address multiple degradation types within a unified framework, but often lack explicit spatial and semantic modeling of degradation characteristics, limiting their adaptability to diverse weather conditions. To address this limitation, we propose a Degradation-Aware Cross-Modal Prompt Compensation Network (DCMPC-Net) that leverages cross-modal degradation cues from a pretrained vision-language model to condition restoration features within a unified backbone. Specifically, our DCMPC-Net mainly consists of the Cross-Modal Prompt Generator (CMPG), Prompt-Guided Attention Alignment Module (PGAAM), and Dual Feature Compensation Module (DFCM). The CMPG integrates textual embeddings with visual features to produce degradation-aware prompts that encode degradation-related semantic and contextual cues. These prompts are injected into the decoder via a PGAAM, which adaptively aligns semantic information with degraded regions to facilitate context-aware restoration. To further enhance structural fidelity, DFCM is introduced that disentangles degradation artifacts from scene structures, thereby improving the reconstruction of fine textures and detailed content. By integrating cross-modal semantic guidance with spatial alignment and structural enhancement, DCMPC-Net achieves robust and perceptually consistent restoration across diverse weather conditions. Extensive experiments show that DCMPC-Net outperforms state-of-the-art methods in both task-specific and unified settings, achieving superior accuracy and visual fidelity.

cs.CV↗
arXiv2026

NEXUS: Spectral Variability of Little Red Dots and Blue Active Galactic Nuclei at $2 \lesssim z \lesssim 6$

We present spectral measurements for 17 Little Red Dots (LRDs) and 14 blue broad-line active galactic nuclei (AGNs) at $2\lesssim z \lesssim 6$ using multi-epoch JWST NIRSpec MSA spectra from the NEXUS program, sampling rest-frame timescales of $\sim 1-3$ months. Overall, the LRD population shows significantly enhanced Balmer decrement compared with both blue JWST AGNs at similar redshifts and 56 low-redshift broad-line AGNs matched in H$\rm\alpha$ luminosity. The rest-optical continua of LRDs show little ensemble variability (rms $\lesssim 3\%$), and the total H$\rm\alpha$ emission also shows weaker ensemble variability compared with low-redshift AGNs matched in H$\rm\alpha$ luminosity and rest-frame timescales. Based on the flux uncertainties, we constrain the intrinsic H$\rm\alpha$ rms variability to be $\lesssim 4\%$ for the LRD population over these timescales. Combining our results with recent broad-line variability measurements of LRDs over yearly to decade timescales reveals a low-level white-noise pattern across all timescales, in stark contrast to the variability amplitude ($\sim 6\%$ over monthly timescales) and red-noise pattern observed in normal AGNs. These results add to the growing observational studies that suggest population-wise, LRDs have weak variability both in optical continuum and broad-line emission. Furthermore, the distinct white-noise broad-line variability pattern suggests different production mechanisms of broad-line emission in LRDs as opposed to normal AGNs, and/or different properties of the driving ionizing flux from the central engine.

astro-ph.GA↗
arXiv2026

An Exploratory Analysis of New Large Gaia-informed Quasar Samples in SDSS-V

Quasars are luminous objects that provide insights into the physics and evolution of supermassive black holes (SMBHs) and their accretion flows, galaxy evolution, and even cosmology. In this study, we present an exploratory study based on the ongoing fifth generation of the Sloan Digital Sky Survey (SDSS-V) and its unique dual-hemisphere, wide-field, and multi-object spectroscopic capabilities, with the aim of creating a comprehensive, all-sky quasar sample. The targets were selected through two novel methods, GUA and Skewt-QSO, that rely primarily on data from WISE and Gaia, aiming to address gaps in previous large quasar samples. Our sample includes over 250,000 spectroscopically confirmed quasars reaching z~5, with tens of thousands of newly identified quasars in the southern hemisphere. The selection methods are highly pure, with well over 80% of the spectra collected being genuine quasars; the main contaminants are M-type stars. The detailed spectral decomposition procedure we employed shows that the quasars in the sample span a wide range of luminosities (Lbol~$10^{44}-10^{48} erg s^{-1}$), SMBH masses (MBH~$10^6-10^{10}$ Msun), and accretion rates (L/LEdd~0.01-1). The distributions of these properties are consistent with those of previous quasar catalogs, which are based on past generations of SDSS, once we account for potential selection biases related to the various survey depths. Our findings confirm that novel selection methods based on optical+IR colors and/or astrometry can yield a large, high-purity quasar sample over wide sky areas, including in cases where more nuanced multi-band photometry and/or multi-wavelength data in the X-ray or radio is not available. This SDSS-V sample, which will continue to grow, establishes a robust reference for future southern (time-domain) surveys, while enhancing and complementing our understanding of quasar demographics and SMBH evolution.

astro-ph.GA↗
arXiv2026

Stress-Sharing for Decentralized Fault Repair in Modular Spacecraft

Structural damage in modular spacecraft can disrupt mechanical and communication connectivity, reducing system capability. Existing approaches rely on redundancy or preplanned reconfiguration and do not enable autonomous repair under local information and physical constraints. We model the spacecraft as a lattice-constrained graph and introduce a fully decentralized, asynchronous stress-sharing repair policy inspired by biological wound healing: local distress signals guide surviving modules toward damaged regions to close fragmented gaps, after which each displaced module locally retraces its own motions to recover the pre-damage shape, using only local information and no absolute position sensing. We evaluate the policy in PyBullet rigid-body simulation across structures of up to 160 modules, three fault densities (10, 20, 30%), and random and localized damage. The policy consolidates the surviving modules into a single connected body: even in the most severe case tested, where 30% of modules fail at random, it gathers roughly 80% or more of the surviving modules into one connected component, and this fraction improves with assembly size, making the approach well suited as a swarm-scale repair policy for large modular spacecraft.

cs.RO↗
arXiv2026

Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations

Accurate extreme precipitation forecasting is critical for disaster mitigation but remains challenging for numerical weather prediction (NWP) models due to systemic intensity underestimation and spatial displacement. Traditional precipitation multi-model blending algorithms perform pixel-by-pixel blending on the forecast field based on weights, which may lead to the expansion of precipitation areas and the smoothing of extreme values. This study proposes an U-Net based two-stage framework: probability classification followed by value reconstruction, to blend forecasts from six major NWP models. A novel station-grid joint supervision mechanism is introduced by integrating observations from 2411 national meteorological stations in China into the loss function, simultaneously constraining spatial structures and peak intensities. Evaluations using independent samples from the 2025 flood season demonstrate that our model significantly outperforms both individual NWPs and current operational products. For rainstorms (>=50 mm), the Threat Score (TS) improved by 38.4% compared to the best NWP. Notably, for extreme events (>=100 mm) driven by extratropical cyclones and the subtropical high, the model successfully elevated the TS to above 0.1, transforming forecasts from having negligible reference value into those with certain operational utility. Furthermore, the model exhibits data-driven spatial correction capabilities, effectively realigning systematic rainbelt displacements with actual precipitation centers. The inclusion of station observations specifically enhanced the TS for rainstorms by 10.4% and effectively balanced the Bias. These results highlight the efficacy of multi-source joint supervision in enhancing the capture of extreme precipitation events.

cs.LG↗
arXiv2026

EHR-Complex: Benchmarking Medical Agents for Complex Clinical Reasoning

Clinical agents promise to democratize access to electronic health records (EHRs), yet existing benchmarks fail to reflect the complexity of practical EHR analysis, e.g., often operating on idealized, clean EHRs via static SQL generation rather than interactive execution. In this work, we introduce EHR-Complex, a large-scale benchmark designed for interactive clinical database reasoning. Built on the large MIMIC-IV substrate (365K patients, 31 tables, 500M+ records), EHR-Complex comprises about 52K tasks spanning six clinical intents, supporting both patient-level and population-level queries, where each task requires an agent to interact with a sandboxed environment by executing SQL queries or Python code. Notably, EHR-Complex considers the real-world SQL task complexity for longitudinal multi-table aggregation and compositional reasoning, resulting in 31.93 SQL structural components per query on average. Evaluation results on EHR-Complex reveal the clinical difficulty of these EHR reasoning scenarios, with the top-performing model achieving only 62.3% exact-match accuracy. Pass^k consistency drops below 50% for nearly all evaluated models at k=4, exposing broad stochastic fragility. A fine-grained analysis of more than 3,800 failed trajectories for representative LLMs reveals three dominant failure modes: SQL logic errors, medical-code lookup failures, and semantic misunderstandings. EHR-Complex provides a rigorous testbed for clinical agents and highlights remaining gaps in robust reasoning for large-scale EHR analysis.

cs.AI↗
arXiv2026

NEXUS: Abundance, Environments, and Spectral Diversity of Little Red Dots from the NIRSpec MSA Sample

We present a comprehensive study of Little Red Dots (LRDs) at 2.3 < z < 7.4 using NIRCam photometry and NIRSpec MSA/PRISM spectra from the ongoing NEXUS program. Photometric selection combining several commonly adopted methods yields a high completeness of about 85% for LRD selection over this redshift range and for a flux limit of F444W < 26. The overall purity is about 60%, with contamination from emission-line galaxies and normal active galactic nuclei (AGNs), as well as dwarf stars. Most (>90%) of the spectroscopically confirmed LRDs have robust broad-line detection. Our spectroscopic sample of 36 LRDs displays the full range of spectral diversity of LRDs. It includes objects with extreme Balmer breaks similar to the LRD "Cliff", as well as objects with moderately reddened rest-optical continua that can be fit with low-temperature blackbody components in the recent BH* model framework. The broad H$\alpha$ emission is correlated with the continuum emission at 5100 Angstrom, suggesting common origins for these emission components; the narrow [O III] emission, however, is poorly correlated with the optical continuum. We do not find evidence of redshift evolution in these spectral properties. The space density of LRDs declines toward z about 2, opposite to the trend for normal AGNs, although low-luminosity LRDs at z about 2-4 may be more abundant than currently probed by ground-based searches. The clustering of LRDs suggests that they live in dark matter halos of several times $10^{11}\ h^{-1}$ solar masses, albeit with large uncertainties. Overall, these results are consistent with recent observations of LRDs and with the emerging picture of accreting SMBHs enshrouded in dense gas envelopes as the origin of LRDs.

astro-ph.GA↗
arXiv2026

SlimSearcher: Training Efficiency-Aware Web Agents via Adaptive Reward Gating

Deep research agents have demonstrated remarkable capabilities in complex information-seeking tasks, yet this power comes at a steep computational cost. Driven by accuracy-focused training paradigms, current models adopt brute-force strategies characterized by blind tool dependency and performative reasoning-generating long, redundant trajectories that are far from necessary for resolving these tasks, leading to wasteful tool calls and excessive token consumption. To overcome this efficiency trap, we propose SlimSearcher, a principled framework that pushes the Pareto frontier between accuracy and computational cost across both Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). In the SFT stage, SlimSearcher employs Pareto-efficient filtration to distill trajectories that are both successful and economical, guiding the model toward inherently efficiency-aware search behaviors. During RL, we introduce Adaptive Reward Gating, a dynamic reward-shaping mechanism that evaluates relative tool and token efficiency within a sampled cohort. By cascading these adaptive efficiency metrics with a strict correctness gate, our approach effectively avoids the brevity bias associated with absolute penalties and mitigates reward hacking. Extensive experiments on long-horizon benchmarks, including GAIA, BrowseComp, and XBenchDeepSearch, demonstrate that SlimSearcher reduces average tool-call rounds by 17%-58% while maintaining or improving accuracy.

cs.LG↗
arXiv2026

A New Member of the Fast and Furious Family: A Relativistic and Time-Variable UV Outflow in a Luminous Quasar

We report the fastest quasar outflow first detected in the ultraviolet, via variable C IV and Si IV absorption at outflow velocities $-77,000$ km s$^{-1}$ to at least $-90,000$ km s$^{-1}$, in the radio-quiet quasar SDSS J231854.31+243954.2 (J2318). J2318 is a weak-lined quasar in the rest-frame ultraviolet, but Gemini GNIRS spectroscopy reveals an H$\alpha$ redshift of $z=2.6781\pm0.0004$. A twenty-year photometric time series shows peak-to-peak variability of 0.5 mag in the $g$ band. The C IV outflow strengthened monotonically over three epochs spanning $\sim$2.2 rest-frame years. The existence of such a high-velocity outflow implies that models of quasar outflows must be able to either accelerate gas to $0.3c$ while still preserving C IV and Si IV ions, or enable the formation of C IV and Si IV ions in gas which has been accelerated to $0.3c$. Virial estimates reveal a black-hole mass of $1.65\times10^9~M_\odot$, which leads to an Eddington luminosity and Eddington ratio of $2.4\times10^{47}$ erg s$^{-1}$ and $0.45$, respectively. Using very conservative assumptions, the UV-absorbing outflow alone has an estimated mass loss of $>0.82~M_\odot~{\rm yr}^{-1}$ and a kinetic luminosity ratio $L_{kin}/L_{bol}\geq0.75$%. The lower limit is just above the threshold usually cited for significant feedback on the host galaxy. Comparison to PDS 456, the only other known quasar with a UV-absorbing outflow at $0.3c$, suggests that the true $\dot{M}$ and $L_{kin}/L_{bol}$ could be up to two orders of magnitude larger.

astro-ph.GA↗
arXiv2026

Varstrometry for Off-nucleus and Dual sub-Kpc AGN (VODKA): Radio Classification of High-Redshift Dual AGN Candidates with the Very Large Array

Dual active galactic nuclei (dual AGNs) are pairs of simultaneously accreting supermassive black holes in merging galaxies. We investigate dual AGNs to understand whether merger-induced accretion is a significant growth mechanism for supermassive black holes. Searching for such systems is favorable at close separations and high redshift (Cosmic Noon, $z \sim 2$) due to the expected combination of high galaxy merger rate and peak AGN activity which characterize this era of the Universe. The sample of nine dual AGN candidates is selected based on resolved optical dual detections with Gaia, whose angular separations are less than 1$' '$ and redshifts range between 1.5 and 2.8. Each pair is spatially coincident with a Sloan Digital Sky Survey quasar primary target. We aim to classify the secondary targets and other components in the radio regime using 2-band Very Large Array imaging (C and Ku-bands) to test for dual AGN presence. We identify two dual AGNs and three quadruply imaged gravitational lens AGNs, two out of which show evidence of radio flux anomaly. The two new dual AGNs add to the limited census of confirmed kpc-scale pairs at Cosmic Noon, while the radio flux anomalies in J0911+0550 and J1118+0745 provide independent evidence for substructure in their lensing potentials, consistent with microlensing on compact AGN emission regions. Besides one confirmed AGN-star pair, three candidates remain unclassified due to lack of radio detection for one or two components.

astro-ph.GA↗
arXiv2026

Understanding the Broad-line Region of Active Galactic Nuclei with Photoionization. II. Slim disks, Self-shadowing, and BLR sizes

Reverberation-mapping (RM) measurements have revealed that high-accretion-rate active galactic nuclei (AGNs) systematically lie below the canonical broad-line region (BLR) radius - optical continuum luminosity (R-L) relation, exhibiting shorter lags than predicted for fixed 5100\AA luminosity. The physical origin of these offsets remains debated. We investigate how accretion-flow structure and BLR cloud properties affect the emissivity-weighted BLR radius using analytic slim-disk SEDs and photoionization calculations on a two-dimensional axisymmetric grid. As the accretion rate approaches and exceeds the Eddington limit, geometric thickening of the inner disk produces anisotropic illumination and self-shadowing, reducing ionizing flux seen by low-latitude BLR clouds and flattening the R-L relation at high L/LEdd. Self-shadowing at high accretion rates reproduces the observed R-L trend in the RM AGN sample reasonably well, but this effect alone is insufficient to explain the observed lag offset in low-mass ($\sim10^{7}M_\odot$) systems with high accretion rates. Motivated by accretion-disk density scalings, we further explore models in which the BLR gas density increases toward lower black hole mass or higher accretion rate. We find that an accretion-rate-dependent BLR density enhancement further improves agreement with observed RM data, where the BLR gas density increases by a factor of 3-5 for one dex increase in $\dot{m}$. Variations in BLR opening angles produce a less important effect on BLR sizes. These results demonstrate that self-consistent modeling of accretion disk SED, BLR illumination and photoionization, and gas density variations can fully explain the observed distribution of AGNs in the BLR size - optical luminosity plane. This framework provides a physically motivated link between accretion-flow structure and BLR observables across a broad range of black-hole properties.

astro-ph.GA↗
arXiv2026

LATTE: Forecasting Peer Anchored Preference Trajectories for Personalized LLM Generation

Personalized generation with frozen large language models requires a conditioning signal that is both compact and current. Existing personalization methods typically retrieve or summarize user histories in text, or compress them into static latent profiles and soft prompts. These approaches are efficient, but they treat a user's past behavior as an aggregate profile and therefore mix stable identity, recent drift, and item content in the same representation. We propose LAtent Trajectory Tracking and Extrapolation (LATTE), a framework that represents personalization as forecasting a peer anchored relative preference state. For each historical session, LATTE subtracts a time masked baseline formed from comparable users who responded to the same item, producing a state that measures how the target user differs from peers under a shared item context. A lightweight sequence predictor then forecasts the next state in this trajectory, and a State to Token Bridge injects the forecast into a frozen instruction tuned LLM through a single anchored soft token. We provide a latent factor analysis showing when peer anchoring cancels shared item variation and why temporal forecasting trades off stale averages against noisy recent states. Experiments on Amazon Reviews 2023 and MemoryCD show that LATTE consistently outperforms retrieval, summary memory, static latent profiles, difference aware latent profiles, and soft prompt compression baselines. On Amazon Reviews 2023, LATTE improves average ROUGE-L from 0.219 for a static latent profile and 0.245 for the strongest added latent compression baseline to 0.259. Additional pairwise comparisons and diagnostic analyses suggest that the improvement is mainly due to forecasting user-specific trajectory information, rather than merely adding a soft prompt interface.

cs.CL↗
arXiv2026

SLSREC: Self-Supervised Contrastive Learning for Adaptive Fusion of Long- and Short-Term User Interests

User interests typically encompass both long-term preferences and short-term intentions, reflecting the dynamic nature of user behaviors across different timeframes. The uneven temporal distribution of user interactions highlights the evolving patterns of interests, making it challenging to accurately capture shifts in interests using comprehensive historical behaviors. To address this, we propose SLSRec, a novel Session-based model with the fusion of Long- and Short-term Recommendations that effectively captures the temporal dynamics of user interests by segmenting historical behaviors over time. Unlike conventional models that combine long- and short-term user interests into a single representation, compromising recommendation accuracy, SLSRec utilizes a self-supervised learning framework to disentangle these two types of interests. A contrastive learning strategy is introduced to ensure accurate calibration of long- and short-term interest representations. Additionally, an attention-based fusion network is designed to adaptively aggregate interest representations, optimizing their integration to enhance recommendation performance. Extensive experiments on three public benchmark datasets demonstrate that SLSRec consistently outperforms state-of-the-art models while exhibiting superior robustness across various scenarios.We will release all source code upon acceptance.

cs.IR↗
arXiv2026

DeepDISC-Euclid: Source Classification and Photometric Redshifts in Euclid Deep Field North With a Pixel-Level Deep Learning Approach

The first Euclid Quick Data Release (Q1) provides extensive imaging and spectroscopic data for hundreds of millions of photometric objects across several deep fields. Accurate classifications and photometric redshifts (photo-z) for these sources are crucial to maximizing the value of these data. In this work, we perform source classification and photo-z estimation for the Euclid Deep Field North (EDF-N) around the North Ecliptic Pole, using a deep learning framework (DeepDISC) that learns and infers using 9-band images simultaneously. We train three dedicated models for (1) source detection and classification, (2) galaxy photo-z, and (3) quasar photo-z. The Euclid Q1 input source catalog, and classifications and spectroscopic redshifts (spec-z) from the Dark Energy Spectroscopic Instrument Data Release 1 are adopted as our training data. DeepDISC source detection achieves overall completeness of ~93% and purity of ~80% if using the Euclid source catalog as the ground truth. Using a JWST source catalog within EDF-N as the reference, we estimate a true purity of ~ 90% for DeepDISC sources. About 99.2%, 99.0%, and 84.8% of stars, galaxies, and quasars, respectively, are correctly recovered with their spectroscopic classifications. The DeepDISC photo-zs show good agreement with spectroscopic redshifts, for both galaxies and quasars. Comparisons with other Euclid Q1 products demonstrate that DeepDISC provides comparable or improved performance in source detection/deblending, classification and photo-z, especially for quasars. These results demonstrate the potential of pixel-level deep learning approaches for large-scale sky surveys such as Euclid and Roman, which will continue to improve with better training labels. We release the full DeepDISC source catalog (~13 million objects) for EDF-N with classifications and photo-zs, including photo-z probability distributions.

astro-ph.GA↗
arXiv2026

A large population of over-massive black hole quasars at z=0.3-0.8 revealed by eROSITA

In most galaxies, the central black hole accounts for no more than a percent of the total mass in stars. Recently, however, extremely over-massive black holes with ratios of 10% have been reported in dwarf galaxies at z<1 and at cosmic dawn (z>5.5) by JWST. Both findings have been interpreted as signatures of the still mysterious origins of super-massive black holes, such that most of the black hole mass was built at birth rather than through black hole accretion. Here we show that among evolved galaxies over-massive black holes are also present, indicating that overmassive BHs are not a signature unique to black hole formation channels. The first large-area sky survey of the eROSITA X-ray telescope on board SpectrRG identified 200 quasars by their luminous hard X-ray radiation. These signpost rapidly growing black holes. Complementary optical spectroscopy from the Sloan Digital Sky Survey and archival UV to IR photometric data combined with galaxy-quasar decomposition techniques allow us unbiased estimates of cosmological distances, black hole masses and host galaxy stellar masses. We securely identify a sample of over-massive black holes with BH-to-host ratios of more than 5%, which may have undergone exponential accretion spurts lasting about a billion years. Our survey identified a high space density of at least 4/Gpc^3 of overmassive black holes near cosmic noon. This indicates an accretion channel disconnected from the stellar population that cause strong deviations from galaxy scaling relations. This channel is currently not part of galaxy evolution models. The identified channel, if applicable also for the first billion years of cosmic time, can explain JWST AGN without requiring them to signify imprints of black hole seeding mechanism.

astro-ph.HE↗