• Presents the first comprehensive survey of astronomical multimodal data fusion. • Summarizes necessity, data sources, modalities, DL models, and fusion strategies. • Analyzes the general process of developing astronomical multimodal models. • Reviews 58 multimodal studies and 6 datasets published before October 2025. • Discusses main findings, challenges, and future research directions. With the rapid advancements in observational technologies and the widespread implementation of large-scale sky surveys, diverse electromagnetic wave data (e.g., optical and infrared) and non-electromagnetic wave data (e.g., gravitational waves) have become increasingly accessible. Astronomy has thus entered an unprecedented era of data abundance and complexity. Astronomers have long relied on unimodal data analysis to perceive the universe, but these efforts often provide only limited insights when confronted with the current massive and heterogeneous astronomical data. In this context, multimodal data fusion (MDF), as an emerging method, provides new opportunities to enhance the value of astronomical data and deepening the understanding of the universe by integrating information from different modalities. Recent progress in artificial intelligence (AI), particularly in deep learning (DL), has greatly accelerated the development of multimodal research in astronomy. Therefore, a timely review of this field is essential. This paper begins by discussing the motivation and necessity of astronomical MDF, followed by an overview of astronomical data sources and major data modalities. It then introduces representative DL models commonly used in astronomical multimodal studies, the general fusion process as well as various fusion strategies, emphasizing their characteristics, applicability, advantages, and limitations. Subsequently, the paper surveys existing astronomical multimodal studies and datasets. Finally, the discussion section synthesizes key findings, identifies potential challenges, and suggests promising directions for future research. By offering a structured overview and critical analysis, this review aims to inspire and guide researchers engaged in DL-based MDF in astronomy.

The Dependence of the Mean Spectral Energy Distributions on the Accretion Rate for Quasars with z < 0.75 from the Sloan Digital Sky Survey

Green valley (GV) galaxies are thought to represent a transitional population between star-forming and quiescent systems. However, their spatial distribution relative to galaxy systems, such as galaxy groups and clusters, remains unclear, particularly in relation to the large-scale environmental influence on galaxy quenching. We aim to determine whether GV galaxies preferentially inhabit specific environments within galaxy systems, and to explore the physical nature of their location on the outskirts of massive haloes. We analysed the spatial distribution of GV galaxies using the cluster–galaxy cross-correlation function, based on two datasets: the hydrodynamical simulation Illustris TNG300-1 and observational data from the Sloan Digital Sky Survey (SDSS DR18), cross-matched with the MPA–JHU DR7 catalogue. Galaxy systems with łog(M_ 200 /M_⊙) ≥ 13.5 are used as cluster centres, while galaxies classified as blue, green, or red, based on their location in the $(u-r)$ versus stellar mass diagram, serve as tracers for the correlation analysis. In TNG, GV galaxies show an increasing relative fraction with cluster-centric distance, peaking on the outskirts, particularly for low-mass galaxies and haloes. In some cases, the GV fraction exceeds that of red galaxies. SDSS data reveal qualitatively similar, but weaker trends, with the GV fraction remaining below that of red galaxies at all scales. Most GV galaxies on the outskirts are satellites bound to the central FoF group, consistent with a backsplash or infall origin. Mock catalogues built from TNG and matched to SDSS selection functions reproduce the observational signal, indicating that projection effects drive the differences between datasets. GV galaxies preferentially reside on the outskirts of galaxy systems as satellites bound to the central halo, supporting a scenario in which they are transitioning objects influenced by environmental quenching.

We present our methodology for identifying known clusters as counterparts to objects in the Euclid Catalogue of Galaxy Clusters (ECGC). Euclid is expected to detect a large number of optically selected galaxy clusters over the approximately square degrees of its extragalactic sky survey. Extending out well beyond redshift unity, the catalogue will contain many new high-redshift clusters, while at lower redshifts a fraction of the clusters will have been observed in other surveys. Identifying these known clusters as counterparts to the Euclid-detected clusters is an important step in the validation and construction of the ECGC to augment information with external observables. We present a set of catalogues and meta-catalogues of known clusters that we have assembled for this step, and we illustrate their application and our methodology using the Dark Energy Survey Year 1 cluster catalogue in lieu of the future ECGC. In the process of this work, we have constructed and delivered an updated catalogue with multi-wavelength counterparts. 14000 RedMaPPer EC-RedMaPPer

Abstract We report the discovery of PaRasMoMi-1, a large diffuse emission nebula in Monoceros at Galactic coordinates PN-G 206.2+00.6 (R.A. 06 h 41 m 30 s , decl. +06°16′30″, J2000). Identified in 2021 April through deep narrowband astrophotography and confirmed via archival Digitized Sky Survey, Wide-field Infrared Survey Explorer (WISE), and GALEX near-ultraviolet data, the nebula exhibits [O iii ] λ 5007 emission across an angular diameter of ∼26′ with no detectable H α or [S ii ]. At an estimated distance of ∼1800 pc, the projected physical diameter is ∼45 pc. PaRasMoMi-1 is now registered in the HASH Planetary Nebula database (PN-G 206.2+00.6) as a candidate planetary nebula. Three physical interpretations are considered: an evolved planetary nebula, an [O iii ]-dominant Strömgren sphere, and a bow-shock re-ionization structure associated with the Monoceros Supernova Remnant and the O-type binary HD 48099. Optical spectroscopy is required to discriminate among these scenarios.

We present a framework for relating gravitational wave (GW) sources to the astrophysical properties of spectroscopic galaxy samples. We show how this can enable using clustering measurements of GW sources to infer the relationship between the GW sources and the astrophysical properties of their host galaxies. We accomplish this by creating mock GW catalogs from the spectroscopic Sloan Digital Sky Survey (SDSS) DR7 galaxy survey. We populate the GWs using a joint host-galaxy probability function defined over stellar mass, star formation rate (SFR), and metallicity. This probability is modeled as the product of three broken power-law distributions, each with a turnover pointmotivated by astrophysical processes governing the relation between current-day galaxy properties and binary black hole (BBH) mergers, such as galaxy quenching and BBH delay time. Given that our analysis is anchored in the specific properties and selection characteristics of the adopted galaxy sample, as well as assumptions regarding the host-galaxy probability functions and BBH merger rate prescriptions, the resulting trends should be regarded as model-dependent.Within this framework, our results show that GW bias is most sensitive to host-galaxy probability dependence on stellar mass, with increases of up to ∼𝒪(10)% relative to galaxy bias as the stellar mass pivot scale rises. We also find a notable relationship between GW bias and SFR: when the host-galaxy probability favors low-SFR galaxies, the GW bias significantly increases. In contrast, we observe no strong correlation between GW bias and metallicity. These findings suggest that the spatial clustering of GW sources is primarily driven by the stellar mass and SFR of their host galaxies and shows how GW bias measurements can inform models of the host-galaxy probability function.

Abstract Asteroid light curves play a crucial role in asteroid science, providing valuable insights into the physical properties and internal structures of asteroids. However, traditional approaches to asteroid light curve analysis rely on high-cadence, dense photometric observations, which are fundamentally at odds with the sparse sampling strategies adopted by current large-scale sky surveys. This study aims to use sparse observations to fit light curves and preliminarily identify candidates for fast-rotating asteroids which are worthy of subsequent follow-up observations. We applied this approach to the WFST observations collected between June 2024 and March 2025, and derived light curves for $\sim160$ asteroids from $\sim3800$, among which 18 were identified as fast-rotating candidates. Notably, four of these asteroids exhibit rotation periods shorter than 2.2 hours, the critical limit for structural stability. The number of objects requiring follow-up confirmation was reduced from ~ $\sim3800$ to 18, greatly improving the efficiency of the telescope. This work highlights the potential of sparse photometry in asteroid light curve analysis and establishes a novel route for efficiently identifying ``time-critical'' Solar System Objects (SSOs) within large survey datasets, marking a paradigm shift from detailed studies of individual objects toward efficient screening of large samples.

Numerical simulations of hierarchical structure formation predict that galaxy clusters retain significant dark matter substructure, a signature of their ongoing assembly. This substructure is traced by both the spatial distribution of member galaxies and perturbations in the hot intracluster medium. Merging events significantly impact the thermodynamic state of clusters, introducing scatter in observable mass scaling relations and thereby affecting their use as precision cosmological probes. We statistically quantified the prevalence and properties of substructure in optical galaxy clusters and directly investigated its impact on X-ray morphology and scaling relations, leveraging new data from the DECaLS Legacy Survey and the SRG/eROSITA all-sky survey. We applied the hierarchical density-based clustering algorithm HDBSCAN to the redMaPPer galaxy cluster catalog to identify and characterize substructure from the probabilistic membership assignments. This provides a refined membership catalog and a classification of each cluster as containing substructure or not. We then cross-matched this sample with the eROSITA X-ray morphology catalog to correlate optical substructure with a comprehensive set of X-ray morphological parameters. Finally, we analyzed the scaling relation between X-ray luminosity and optical richness for clusters with and without substructure. Substructure is a common feature, present in approximately 40% of clusters; a quarter of the full sample exhibits a fractional contribution to richness in excess of 35%. We find a highly significant correlation between optical substructure and disturbed X-ray morphologies, a trend that is strongest for high-mass clusters. The clusters with substructure also drive a stronger redshift evolution in the scatter of the L_X-łambda relation. At low redshifts (z &lt; 0.2), they display a systematically higher X-ray luminosity at fixed richness compared to relaxed systems. We demonstrate that substructure identification with redMaPPer is viable and essential for enhancing the precision of cluster cosmology. We attribute the enhanced effect of mergers on X-ray properties at low redshifts to the increased density contrast of low-redshift cool cores and longer substructure survival times, which are possibly due to the suppression of disruptive mixing by effects such as magnetic draping. At lower cluster richness, a discordance between X-ray morphology and the merging state indicates a growing relative importance of active galactic nucleus feedback in governing X-ray morphology.

Context: Astronomical imaging aims to maximize signal capture while minimizing noise. It is difficult and expensive to enhance the signal-to-noise ratio directly on detectors, which has led to extensive research into advanced post-processing techniques. Aims: Removing background noise from images is a valuable preprocessing step for catalog-building tasks. We introduce BGRem, a machine-learning (ML)-based tool to remove background noise from astronomical images. Our aim is to improve image quality and enhance the performance of the subsequent analysis pipeline, from detecting faint sources to performing source characterization tasks. Methods: The BGRem tool uses a diffusion-based model with an attention U-Net as backbone, trained on simulated images for optical and gamma $(γ)$-ray data from the MeerLICHT and Fermi-LAT telescopes. The tool learns to denoise astronomical images in a supervised manner over several diffusion steps. We performed preprocessing and postprocessing techniques, including normalization and median subtraction, on these images to make them suitable for the analysis pipeline. Results: We compared the performance of BGRem with SourceExtractor (SExtractor), a widely used tool for cataloging astronomical sources. The number of true positive sources using SExtractor increased by about 7% for MeerLICHT data when we used BGRem as a preprocessing step. We also show the generalizability of BGRem by testing it with optical images from different telescopes and on simulated γ-ray data representative of the Fermi-LAT telescope. In both cases, BGRem improves the source detection efficiency. Conclusions: The BGRem tool improves the source detection accuracy of traditional pixel-based methods by removing complex background noise. Using zero-shot approach, BGRem generalizes well to a wide range of optical images. The successful application of BGRem to simulated γ-ray images, alongside optical data, demonstrates its adaptability to distinct noise characteristics and observational domains. This cross-wavelength performance highlights its potential as a general-purpose background removal framework for multiwavelength astronomical surveys.

Abstract Quasar samples remain severely incomplete at low Galactic latitudes because of strong extinction and source confusion. We conduct a systematic search for quasars behind the Galactic plane using X-ray sources from the Chandra Source Catalog (CSC), combined with optical data from Gaia Data Release 3 and mid-infrared data from CatWISE2020. Using spectroscopically confirmed quasars and stellar-type objects from datasets including DESI, the Sloan Digital Sky Survey, and LAMOST, we apply a random forest classifier to identify quasar candidates, with stellar contaminants suppressed using Gaia proper-motion constraints. Photometric redshifts are estimated for the candidates using a random forest regression model. Applying this framework to previously unclassified CSC sources, we identify 7570 quasar candidates, including 1060 Galactic plane quasar (GPQ) candidates at ∣ b ∣ &lt; 20°, of which 551 are high-confidence candidates. Relative to the previously known GPQ sample, our selected GPQs reach lower optical and X-ray fluxes, improving sensitivity to low-flux GPQs. In addition, both the GPQ candidates and known GPQs display harder X-ray spectra than the all-sky quasar sample, consistent with increased absorption through the Galactic plane. Pilot spectroscopy confirms two high-confidence GPQ candidates as quasars at spectroscopic redshifts of z = 1.2582 and z = 1.1313, and further spectroscopic follow-up of the GPQ sample is underway. This work substantially improves the census of GPQs and provides a valuable target sample for future spectroscopic follow-up, enabling the use of GPQs to refine the reference frames for astrometry and probe the Milky Way interstellar and circumgalactic media with the absorption features of GPQs.

Abstract The ultra-faint Milky Way satellite Willman 1 (W1; M V = −2.6; r half ∼ 27 pc) was the first stellar overdensity found via resolved stars in the Sloan Digital Sky Survey, yet its classification as a dwarf galaxy or star cluster remains ambiguous. Using new Keck/DEIMOS spectroscopy, Hubble Space Telescope photometry, and orbital modeling, we re-examine the nature of W1. From our updated member sample of 56 stars, we find that past analyses included four binaries and seven nonmembers, identified using Gaia proper motions and updated velocities. We continue to find a velocity dispersion consistent with previous analyses, measuring σ v = 4 . 7 − 1.3 + 1.5 km s −1 from 49 stars out to 3 r half . If W1 is in equilibrium, this suggests a dynamical mass of 5 . 9 − 3.4 + 3.7 × 1 0 5 M ⊙ and mass-to-light ratio of (M/L) V = 660 ± 590. Based on Ca II triplet measurements, we estimate an iron abundance of [Fe/H] = − 2.4 5 − 0.13 + 0.12 and metallicity dispersion of σ [ Fe / H ] = 0.3 0 − 0.11 + 0.15 dex. We confirm that W1 does not exhibit mass segregation inside ∼1 r half . Our best-fit orbital model predicts that W1 is at apocenter, implying that W1 has been closer to the Milky Way in the recent past with a pericentric passage ≲25 kpc from the Galactic center ∼0.3 Gyr ago. Given its internal kinematics, metallicity spread, and lack of mass segregation, we conclude that W1 is a galaxy. However, given its orbit and structural properties, which suggest that W1 might be tidally disrupted, as well as the difficulty in identifying a pure member sample, we caution that the measured internal velocity dispersion may not accurately reflect the dynamical mass of this system.

Abstract Changing-look active galactic nuclei (CL-AGNs) exhibit dramatic spectral variability on unexpectedly short timescales, challenging standard accretion flow models. Despite growing samples, the physical drivers of this extreme variability, and the potential link to host-galaxy properties, remain unknown. Regardless of the underlying mechanism, the transition between AGN-dominated and host-dominated spectra offers a unique opportunity to study relations between AGNs and their hosts within the same objects. We present intermediate-resolution spectroscopy of 23 CL-AGNs identified by the Sloan Digital Sky Survey V (SDSS-V), obtained with the Very Large Telescope/X-shooter and Gemini-N/GMOS. An analysis of the Mg ii λ 2798 emission line observed in the spectra demonstrates that the majority of these sources cannot be driven by variable obscuration. Our CL-AGNs roughly follow the M BH – σ * and M BH – M * relations of inactive galaxies, with a median black hole-to-stellar mass ratio of 0.38 %. We find no evidence that the stellar population properties of our CL-AGNs, including stellar mass, age, young stellar fraction, and star formation rate, differ from those of type 2 AGNs in SDSS. These results suggest that CL-AGNs reside in typical AGN host galaxies and that their extreme variability is likely unrelated to host-galaxy environment, supporting the idea that CL-AGNs are not a distinct population, but rather represent a phase of normal AGN activity. This result, in turn, implies that CL-AGNs can serve as useful probes of the AGN-host connection, providing access to both AGN-dominated and host-dominated spectra of the same systems.

Galaxy morphology is key to understanding cosmic evolution. While large astronomical surveys such as the Sloan Digital Sky Survey produce vast amounts of data, energy–efficient processing remains a challenge. Diffractive deep neural networks (D 2 NNs) offer a promising optical computing solution with high throughput and low power consumption. However, deep D 2 NNs suffer from optical attenuation, while shallow networks have limited representation capability. To address this trade–off, we extend the concept of knowledge distillation with optical–domain considerations and develop an optical model compression learning (OMCL) framework for D 2 NNs. Numerical simulations on SDSS galaxy images show that the compressed model achieves 69.25% classification accuracy, representing a 27.5% improvement over conventional training, and exhibits partial recognition ability for unseen categories. Further analysis reveals that the compressed model develops smoother and sparser phase patterns, leading to improved robustness to noise and stronger tolerance to low–precision phase quantization (e.g., 3–bit). These results indicate that transferring optical–domain features from deeper networks can effectively mitigate the limitations of shallow D 2 NNs, providing a practical pathway toward fabrication–friendly and high–performance optical neural networks for large–scale astronomical data analysis.

Abstract The spectra of type 1 active galactic nuclei (AGNs) often exhibit a broad component in [O iii ] λ 5007, which is typically blueshifted and associated with strong outflows. We systematically analyze the [O iii ] emission-line properties of type 1 AGNs with broad components to investigate how these kinematic features relate to the physical properties of the central engine. From a parent sample of 11,557 QSOs at z &lt; 0.3 in Data Release 16 of the Sloan Digital Sky Survey, we select 2290 type 1 AGNs exhibiting broad components in [O iii ]. Previous studies have reported a strong correlation between the blue emission, defined as the full extent of the broad component on the blue side, and black hole mass when the latter is estimated from the M BH – σ * relation using the line width σ of the [O iii ] core component as a surrogate for σ * . In the same way, the black hole mass also shows a strong correlation with the blue emission parameter in our sample. However, this correlation becomes negligible when virial black hole masses are adopted. Besides, the velocity shifts between the broad and core components of [O iii ] show a weak correlation with the Eddington ratio. This is consistent with the expectation that higher accretion rates enhance radiative pressure, thereby driving faster or more prominent outflows. In future work, we will compare the properties of the [O iii ] road component between typical type 1 AGNs and those with double-peaked [O iii ] to probe differences in narrow-line region kinematics and the impact of outflows or dual AGNs.

Abstract We present the first statistical census of emission-line variable active galactic nuclei (EVA) at cosmic noon by combining untargeted and deep Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) spectroscopy with multiepoch spectra from the Sloan Digital Sky Survey, DESI, and LAMOST. Anchoring all candidates to a HETDEX spectroscopic epoch and requiring an active galactic nucleus (AGN) classification in either the HETDEX or external epoch(s), we identify a homogeneous sample of 100 EVA at z ∼ 1.5, including 98 that are newly identified. Emission-line variability is selected primarily through statistically significant line-flux changes, supplemented by extensive visual inspections using contemporaneous photometric light curves. The resulting incidence fraction is f EVA ∼ 0.9%. The rest-frame intervals between spectroscopic epochs span ∼1–10 yr, with brightening and dimming events exhibiting statistically indistinguishable characteristic timescales (Δ T ∼ 2.2 and ∼2.6 yr, respectively). A key result is the characterization of the Baldwin effect in the time domain: while many EVA follow the ensemble Baldwin effect (eBeff) between two epochs, a substantial fraction exhibit apparent anti-eBeff responses. Time-resolved spectroscopy of an individual source reveals that the intrinsic equivalent width–luminosity relation is nonstationary, with the line-to-continuum responsivity systematically evolving from stronger to weaker across successive variability cycles; sparse two-epoch sampling of this evolving intrinsic Baldwin evolution naturally produces both eBeff-like and anti-eBeff behaviors. Finally, EVA show no strong preference for extreme Eddington ratios but exhibit a mild tendency toward lower λ Edd values relative to matched control samples, driven primarily by sources observed in their dim states. Together, these results establish a coherent framework for interpreting emission-line variability in AGN at the peak epoch of cosmic black hole growth.

Abstract Unlocking all the physical information encoded in low-resolution spectra poses a significant challenge for astronomical survey analysis. Such a task demands modeling spectra and optimizing astrophysical parameters in high-dimensional space as a consequence of line blending. Here we present PhDLspec —a deep learning framework embedded with physical priors for stellar spectrum modeling and analysis. By imposing differential spectra derived from ab initio stellar atmospheric model calculation on a Transformer framework, PhDLspec can rigorously and precisely model stellar spectra by simultaneously taking into account more than 30 physical parameters at a computational speed hundreds of times faster than ab initio model calculation. With such a flexible stellar modeling approach, PhDLspec can effectively derive ∼30 stellar labels from a low-resolution spectrum using affordable optimization techniques. Application to LAMOST spectra ( R ≲ 1800) yields stellar elemental abundances in good agreement with high-resolution spectroscopic surveys, after essential calibrations to correct systematic biases in elemental abundance estimates using wide binaries and reference high-resolution data sets. We provide a catalog of 25 elemental abundances for 116,611 subgiant stars with precise age estimates. The successful application of PhDLspec to LAMOST spectra for high-dimensional parameter determination sheds light on similar challenges faced by other surveys and disciplines.

Abstract The Spectro Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) is conducting the first all-sky near-infrared spectral survey spanning 0.75–5.0 μ m with resolving power R ≈ 35–130. Linear variable filters mounted in front of six H2RG detectors produce a position-dependent spectral response across the focal plane. This paper presents the ground-based spectral calibration of SPHEREx, including the cryogenic apparatus, optical configuration, measurement strategy, analysis pipeline, and resulting calibration products. Monochromatic wavelength scans are used to derive the spectral response function, band center, and resolving power for every pixel. Band centers are measured to better than 1 nm for Bands 1 through 4 (0.75–3.82 μ m) and better than 10 nm for Bands 5 and 6 (3.82–5.0 μ m). Out-of-band leakage is negligible for detectors above 1.64 μ m and is present at the percent level below this wavelength. The resolving power is measured to within 5% and agrees with design expectations to within 10%. An on-sky spectrum of the Cat’s Eye Nebula (NGC 6543) constructed from repeated observations provides in-flight verification and shows agreement between ground-calibrated response and astrophysical emission features. Calibration products, including per-pixel band center and resolving power maps, are released through IPAC to support community use of SPHEREx data. The absolute spectral calibration will continue to improve through in-flight measurements, with further reductions in uncertainty expected for the longest-wavelength bands.

Abstract We investigate the stellar population properties of pseudo-bulges in barred galaxies drawn from the Sloan Digital Sky Survey (SDSS DR7) to assess how bars regulate central star formation and secular evolution. Our sample comprises barred spiral and barred lenticular (S0) galaxies with reliable spectroscopic indices obtained from multicomponent structural decompositions. Stellar ages and recent star formation are traced using the 4000Å break strength (Dn(4000)) and the Balmer absorption index (HδA), complemented by bulge, bar, and disc colours. Barred spirals show a clear bimodality in Dn(4000), with peaks at Dn(4000) ∼ 1.3 and ∼1.8. Low-Dn(4000) pseudo-bulges exhibit strong HδA absorption, blue colours, and high specific star-formation rates, indicating young, actively growing centres. High-Dn(4000) systems instead show weak HδA, red colours, and low sSFR, consistent with older, quenched pseudo-bulges. Barred S0s display an old-bulge-dominated distribution, suggesting that gas-poor barred spirals transition into S0s following disc-wide quenching. We also find elevated AGN incidence among old pseudo-bulges. These trends support a scenario in which bars funnel gas inward to build pseudo-bulges and later suppress central star formation by depleting or stabilising the inflow. IFU observations show that bars assemble cold nuclear discs that age and quench over time, while high-redshift imaging confirms that bars are already present at z ∼ 4, implying that this evolutionary cycle operates across cosmic time. The strong correspondence between stellar age, colour, and structure indicates that bar-driven secular evolution governs both the growth and quenching of central components, linking blue barred spirals to red S0 galaxies.

Abstract The abundance discrepancy problem refers to the systematic differences observed between chemical abundances derived from collisionally excited lines (CELs) and recombination lines (RLs) of heavy ions. It remains a major unsolved problem in the study of ionized nebulae and is quantified by the abundance discrepancy factor (ADF). In this work, we present a deep integral field spectroscopic data set of the entire Lagoon Nebula (M8), obtained by the Sloan Digital Sky Survey V Local Volume Mapper project, at a spatial resolution of 0.21 pc spaxel −1 . This unique data set allows us, for the first time, to investigate spatially resolved maps of oxygen RL intensities (O ii V1), together with maps of H i RLs, heavy-ion CELs, and dust attenuation across a whole H ii region. We map the electron temperature using CELs and RLs of O 2+ and CELs of N + , and we map the electron density using CELs of S + . We derive CEL-based ionic and elemental oxygen abundances and, for the first time, a spatially resolved map of the RL-based O 2+ abundance in an H ii region. These measurements enable construction of the first spatially resolved ADF(O 2+ ) map of an H ii region and yield a global mean ADF of ∼0.47 ± 0.02 dex. Focusing on the central region of M8, where ionization is dominated by the O-type star Her 36, we find radial variations in the ADF, ranging between ∼0.35 and 0.50 dex. Our findings provide novel constraints on the spatial behavior and origin of the abundance discrepancy in the H ii regions.

Abstract The chemical abundance of host stars plays a pivotal role in shaping the formation history of planetary systems, yet the influence of elements beyond iron remains poorly understood. Here, we investigate the relationship between the carbon-to-oxygen (C/O) ratio of host stars and the orbital periods of giant planets. By analyzing high-resolution spectroscopic data from 598 planet-hosting stars (hosting 929 planets) across the Sloan Digital Sky Survey, Keck, and HARPS surveys, we identify a correlation; stars with higher C/O ratios are more likely to host longer-period giant planets. Theoretical models of pebble-driven planet formation and migration further support this observation, demonstrating that elevated C/O ratios enhance solid material availability at outer disk regions, promoting giant planet formation at larger distances and subsequent moderate inward migration. Our findings establish stellar C/O as a critical factor in shaping the orbital architecture of giant planets, bridging disk chemistry to planetary system evolution.