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At least 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Microbial Community Structure–Function Relationships in Yaquina Bay Estuary Reveal Spatially Distinct Carbon and Nitrogen Cycling Capacities

Linking microbial community structure to ecological processes requires understanding of the functional roles among individual populations and the factors that influence their distributions. These structure–function relationships are particularly difficult to disentangle in estuaries, due to highly variable physico-chemical conditions. Yet, examining microbe-mediated turnover of resources in these “bioreactor” ecosystems is critical for understanding estuarine ecology. In this study, a combined metagenomics and metaproteomics approach was used to show that the unequal distribution of microbial populations across the Yaquina Bay estuary led to a habitat-specific taxonomic and functional structure and a clear spatial distribution in microbe-mediated capacities for cycling of carbon and nitrogen. For example, size-fractionation revealed that communities inhabiting suspended particulate material encoded more diverse types of metabolisms (e.g., fermentation and denitrification) than those with a planktonic lifestyle, suggesting that the metabolic reactions can differ between size fractions of the same parcel of an estuarine water column. Similarly, communities inhabiting oligotrophic conditions in the lower estuary were enriched in genes involved in central carbon metabolism (e.g., TCA cycle), while communities in the upper estuary were enriched in genes typical of copiotrophic populations (e.g., cell growth, cell division). Integrating gene and protein data revealed that abundant populations of Flavobacteriales and Rhodobacterales encoded similar genomic functions, yet differed significantly in protein expression, dedicating a large proportion of their respective proteomes to rapid growth and division versus metabolic versatility and resource acquisition. This suggested potentially distinct life-strategies between these two co-occurring lineages and was concomitant with differing patterns of positive evolutionary selection on their encoded genes. Microbial communities and their functions across Yaquina Bay appear to be structured by population-level habitat preferences, resulting in spatially distinct elemental cycling, while within each community, forces such as competitive exclusion and evolutionary selection influence species life-strategies and may help maintain microbial diversity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models

In the event of an accidental or intentional hazardous material release in the atmosphere, researchers often run physics-based atmospheric transport and dispersion models to predict the extent and variation of the contaminant spread. These predictions are imperfect due to propagated uncertainty from atmospheric model physics (or parameterizations) and weather data initial conditions. Ensembles of simulations can be used to estimate uncertainty, but running large ensembles is often very time consuming and resource intensive, even using large supercomputers. In this paper, we present a machine-learning-based method which can be used to quickly emulate spatial deposition patterns from a multi-physics ensemble of dispersion simulations. We use a hybrid linear and logistic regression method that can predict deposition in more than 100,000 grid cells with as few as fifty training examples. Logistic regression provides probabilistic predictions of the presence or absence of hazardous materials, while linear regression predicts the quantity of hazardous materials. The coefficients of the linear regressions also open avenues of exploration regarding interpretability—the presented model can be used to find which physics schemes are most important over different spatial areas. A single regression prediction is on the order of 10,000 times faster than running a weather and dispersion simulation. However, considering the number of weather and dispersion simulations needed to train the regressions, the speed-up achieved when considering the whole ensemble is about 24 times. Ultimately, this work will allow atmospheric researchers to produce potential contamination scenarios with uncertainty estimates faster than previously possible, aiding public servants and first responders.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Spatial Environmental Assessment Tool (SEAT): A Modeling Tool to Evaluate Potential Environmental Risks Associated with Wave Energy Converter Deployments

Wave energy converter (WEC) arrays deployed in coastal regions may create physical disturbances, potentially resulting in environmental stresses. Presently, limited information is available on the nature of these physical disturbance or the resultant effects. A quantitative Spatial Environmental Assessment Tool (SEAT) for evaluating the potential effects of wave energy converter (WEC) arrays on nearshore hydrodynamics and sediment transport is presented for the central Oregon coast (USA) through coupled numerical model simulations of an array of WECs. Derived climatological wave conditions were used as inputs to the model to allow for the calculation of risk metrics associated with various hydrodynamic and sediment transport variables such as maximum shear stress, bottom velocity, and change in bed elevation. The risk maps provided simple, quantitative, and spatially-resolved means of evaluating physical changes in the vicinity of a hypothetical WEC array in response to varying wave conditions. The near-field risk of sediment mobility was determined to be moderate in the lee of the densely spaced array, where the potential for increased sediment deposition could result in benthic habitat alteration. Modifications to the nearshore sediment deposition and erosion patterns were observed near headlands and topographic features, which could have implications for littoral sediment transport. The results illustrate the benefits of a risk evaluation tool for facilitating coastal resource management at early market marine renewable energy sites.

Jones, Craig↗
DOE OSTI2021

Spatially-Explicit Prediction of Capacity Density Advances Geographic Characterization of Wind Power Technical Potential

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios.

17 WIND ENERGY↗
DOE OSTI2025

Modeling and Analysis of Wind Turbine Wake Vortex Evolution Due to Time-Constant Spatial Variations in Atmospheric Flow

Modern utility-scale wind turbines are evolving toward larger, lighter, and more flexible designs to meet the growing demand for renewable energy while minimizing logistical costs. However, these advancements in lightweight design result in heightened aeroelastic sensitivity, leading to complex interactions which affect the rotor’s capacity to withstand aerodynamic loading and the cascading effects that manifest in the wake’s vortex-structure evolution under variable atmospheric conditions. In this paper, we analyze the influence of stream-wise fluctuating atmospheric flow conditions on wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex–wake interactions for a range of conditions with spatially variant atmospheric flow. CODEF solutions for turbine operation in wind conditions featuring only one parameter fluctuation are compared to wind conditions with several wind parameter variations in combination. By isolating individual inflow variations and comparing them to multi-parameter scenarios, we determine the contributions of each atmospheric factor to rotor dynamics, wake evolution, and downstream wind farm interactions. The purpose of this paper is to analyze the effects of spatial variations in atmospheric flow on the topological evolution of wind turbine vortex wakes, which constitutes a gap in the current understanding of wind turbine wake dynamics. The insights gained from this study are particularly valuable for the development of wind farm control strategies aimed at mitigating the adverse effects of wake interactions, enhancing energy capture, and improving the overall stability of wind farm operations. With these insights, we aim to contribute to the development of modeling and simulation tools to optimize utility-scale wind power plants operating in diverse atmospheric environments.

Farrell, Alayna (ORCID:000000023555720X)↗
DOE OSTI2017

A Multivariate Dynamic Spatial Factor Model for Speciated Pollutants and Adverse Birth Outcomes

Evidence suggests that exposure to elevated concentrations of air pollution during pregnancy is associated with increased risks of birth defects and other adverse birth outcomes. While current regulations put limits on total PM2.5 concentrations, there are many speciated pollutants within this size class that likely have distinct effects on perinatal health. However, due to correlations between these speciated pollutants, it can be difficult to decipher their effects in a model for birth outcomes. To combat this difficulty, we develop a multivariate spatio-temporal Bayesian model for speciated particulate matter using dynamic spatial factors. These spatial factors can then be interpolated to the pregnant women’s homes to be used to model birth defects. The birth defect model allows the impact of pollutants to vary across different weeks of the pregnancy in order to identify susceptible periods. Here, the proposed methodology is illustrated using pollutant monitoring data from the Environmental Protection Agency and birth records from the National Birth Defect Prevention Study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Collaborative Geodesign and Spatial Optimization for Fragmentation-Free Land Allocation

Demand for agricultural food production is projected to increase dramatically in the coming decades, putting at risk our clean water supply and prospects for sustainable development. Fragmentation-free land allocation (FF-LA) aims to improve returns on ecosystem services by determining both space partitioning of a study area and choice of land-use/land-cover management practice (LMP) for each partition under a budget constraint. In the context of large-scale industrialized food production, fragmentation (e.g., tiny LMP patches) discourages the use of modern farm equipment (e.g., 10- to 20-m-wide combine harvesters) and must be avoided in the allocation. FF-LA is a computationally challenging NP-hard problem. We introduce three frameworks for land allocation planning, namely collaborative geodesign, spatial optimization and a hybrid model of the two, to help stakeholders resolve the dilemma between increasing food production capacity and improving water quality. A detailed case study is carried out at the Seven Mile Creek watershed in the midwestern US. The results show the challenges of generating near-optimal solutions through collaborative geodesign, and the potential benefits of spatial optimization in assisting the decision-making process.

Xie, Yiqun↗
DOE OSTI2019

Remotely Sensed Spatial Structure as an Indicator of Internal Changes of Vegetation Communities in Desert Landscapes

Desert environments are sensitive to disturbances, and their functions and processes can take many years to recover. Detecting early signs of disturbance is critical, but developing such a capability for expansive remote desert regions is challenging. Using a variogram and 15-cm resolution Visible Atmospherically Resistant Index (VARI) imagery, we examined the usefulness of the spatial structure of desert lands for monitoring early signs of habitat changes using the Riverside East solar energy zone located within Riverside County, California. We tested the method on four habitat types in the region, Parkinsonia florida–Olneya tesota , Chorizanthe rigida–Geraea canescens , Larrea tridentata–Ambrosia dumosa , and Larrea tridentata–Encelia farinosa alliances. The results showed that the sill, range, form, and partial sill of the variogram generated from VARI strongly correlate with overall vegetation cover, average canopy size, canopy size variation, and spatial structure within a dryland habitat, respectively. Establishing a baseline of variogram parameters for each habitat and comparing to subsequent monitoring parameters would be most effective for detecting internal changes because values of variogram parameters would not match absolute values of landscape properties. When monitoring habitats across varying landscape characteristics, a single appropriate image resolution would likely be the resolution that could adequately characterize the habitat dominated by the smallest vegetation. For the variogram generated from VARI, which correlates to vegetation greenness, the sills may indicate the health of vegetation communities. However, further studies are warranted to determine the effectiveness of variograms for monitoring habitat health. Remotely sensed landscape structure obtained from variograms could provide complementary information to traditional methods for monitoring internal changes in dryland vegetation communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bayesian Spatial Models for Projecting Corn Yields

Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Spatial and Temporal Correlates of Greenhouse Gas Diffusion from a Hydropower Reservoir in the Southern United States

Emissions of CO 2 and CH 4 from freshwater reservoirs constitute a globally significant source of atmospheric greenhouse gases (GHGs), but knowledge gaps remain with regard to spatiotemporal drivers of emissions. We document the spatial and seasonal variation in surface diffusion of CO 2 and CH 4 from Douglas Lake, a hydropower reservoir in Tennessee, USA. Monthly estimates across 13 reservoir sites from January to November 2010 indicated that surface diffusions ranged from 236 to 18,806 mg m -2 day -1 for CO 2 and 0 to 0.95 mg m -2 day -1 for CH 4 . Next, we developed statistical models using spatial and physicochemical variables to predict surface diffusions of CO 2 and CH 4 . Models explained 22.7 and 20.9% of the variation in CO 2 and CH4 diffusions, respectively, and identified pH, temperature, dissolved oxygen, and Julian day as the most informative important predictors. These findings provide baseline estimates of GHG emissions from a reservoir in eastern temperate North America a region for which estimates of reservoir GHGs emissions are limited. Our statistical models effectively characterized non-linear and threshold relationships between physicochemical predictors and GHG emissions. Further refinement of such models will aid in predicting current GHG emissions in unsampled reservoirs and forecasting future GHG emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Spatial-dependent propagation of cosmic rays results in spectrum of proton, ratios of p-bar /p, B/C AND anisotropy of nuclei

Recent precise measurements of cosmic ray spectra revealed an anomalous hardening at ∼200 GV, observed by the ATIC, CREAM, PAMELA, and AMS02 experiments. Particularly, the latest observation of the p-bar /p ratio by AMS02 demonstrated a flat distribution, which further validated the spectral anomalies of secondary particles. All those new phenomena indicated that the conventional propagation model of cosmic rays meets a challenge. In this work, the spatial-dependent diffusion coefficient D(r,z,p) is employed by tracing the source distribution under the physical picture of the two-halo model in the DRAGON package. Under such a scenario, the model calculation will result in two-component spectra for primary nuclei. Due to the smaller rigidity dependence of D(r,z,p) in the galactic disk, the ratios secondary-to-primary will inevitably be flatter and the expected anisotropy of cosmic rays will be much more attenuated than in the conventional model. As a result, we can reproduce the spectral hardening of protons, the flat ratios of p-bar /p and B/C, and consistent anisotropy from ∼100 GeV to ∼100 TeV by only adopting one set of spatial-dependent diffusion coefficients D(r,z,p) in a galactic disk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

WEIBEL INSTABILITY DRIVEN BY SPATIALLY ANISOTROPIC DENSITY STRUCTURES

Observations of afterglows of gamma-ray bursts (GRBs) suggest that post-shock magnetic fields are strongly amplified to about 100 times the shock-compressed value. The Weibel instability appears to play an important role in generating the magnetic field. However, recent simulations of collisionless shocks in homogeneous plasmas show that the magnetic field generated by the Weibel instability rapidly decays. There must be some density fluctuations in interstellar and circumstellar media. The density fluctuations are anisotropically compressed in the downstream region of relativistic shocks. In this paper, we study the Weibel instability in electron–positron plasmas with spatially anisotropic density distributions by means of two-dimensional particle-in-cell simulations. We find that large magnetic fields are maintained for a longer time by the Weibel instability driven by spatially anisotropic density structure. Particles anisotropically escape from the high density region, so that a temperature anisotropy is generated and the Weibel instability becomes unstable. Our simulation results suggest that the Weibel instability driven by an anisotropic density structure can generate sufficiently large magnetic fields and they can cover sufficiently large regions to explain the afterglow emission of GRBs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A Wide View of the Galactic Globular Cluster NGC 2808: Red Giant and Horizontal Branch Star Spatial Distributions

Wide-field and deep DECam multiband photometry, combined with HST data for the core of the Galactic globular cluster NGC 2808, allowed us to study the distribution of various stellar subpopulations and stars in different evolutionary phases out to the cluster tidal radius. We used the C ugi = (u - g) - (g - i) index to identify three chemically distinct subpopulations along the red giant branch and compared their spatial distributions. The most light-element-enriched subpopulation (P3) is more centrally concentrated; however, it shows a more extended distribution in the external regions of the cluster compared to the primordial (P1) and intermediate (P2) composition populations. Furthermore, the P3 subpopulation centroid is off-center relative to those of the P1 and P2 groups. We also analyzed the spatial distribution of horizontal branch stars and found that the relative fraction of red horizontal branch stars increases for radial distances larger than ≈1$^{'}_{.}$5, while that of the blue and hotter stars decreases. These new observations, combined with literature spectroscopic measurements, suggest that the red horizontal branch stars are the progeny of all the stellar subpopulations in NGC 2808, i.e., primordial and light-element enhanced, while the blue stars are possibly the result of a combination of the "hot-flasher" and the "helium-enhanced" scenarios. A similar distribution of different red giant branch subpopulations and horizontal branch stars was also found for the most massive Galactic globular cluster, ω Cen, based on combined DECam and HST data, which suggests that the two may share a similar origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SPATIAL VARIATIONS OF PAH PROPERTIES IN M17SW REVEALED BY SPITZER /IRS SPECTRAL MAPPING

We present Spitzer /IRS mid-infrared spectral maps of the Galactic star-forming region M17 as well as IRSF/SIRIUS Br γ and Nobeyama 45 m/FOREST {sup 13}CO ( J = 1–0) maps. The spectra show prominent features due to polycyclic aromatic hydrocarbons (PAHs) at wavelengths of 6.2, 7.7, 8.6, 11.3, 12.0, 12.7, 13.5, and 14.2 μ m. We find that the PAH emission features are bright in the region between the H ii region traced by Br γ and the molecular cloud traced by {sup 13}CO, supporting that the PAH emission originates mostly from photo-dissociation regions. Based on the spatially resolved Spitzer /IRS maps, we examine spatial variations of the PAH properties in detail. As a result, we find that the interband ratio of PAH 7.7 μ m/PAH 11.3 μ m varies locally near M17SW, but rather independently of the distance from the OB stars in M17, suggesting that the degree of PAH ionization is mainly controlled by local conditions rather than the global UV environments determined by the OB stars in M17. We also find that the interband ratios of the PAH 12.0 μ m, 12.7 μ m, 13.5 μ m, and 14.2 μ m features to the PAH 11.3 μ m feature are high near the M17 center, which suggests structural changes of PAHs through processing due to intense UV radiation, producing abundant edgy irregular PAHs near the M17 center.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Transport of High-energy Charged Particles through Spatially Intermittent Turbulent Magnetic Fields

Identifying the sources of the highest energy cosmic rays requires understanding how they are deflected by the stochastic, spatially intermittent intergalactic magnetic field. Here we report measurements of energetic charged-particle propagation through a laser-produced magnetized plasma with these properties. We characterize the diffusive transport of the particles experimentally. The results show that the transport is diffusive and that, for the regime of interest for the highest-energy cosmic rays, the diffusion coefficient is unaffected by the spatial intermittency of the magnetic field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Toward a More Complete Optical Census of Active Galactic Nuclei via Spatially Resolved Spectroscopy

While emission line flux ratio diagnostics are the most common technique for identifying active galactic nuclei (AGNs) in optical spectra, applying this approach to single-fiber spectra of galaxies can omit entire subpopulations of AGNs. Here, we use spatially resolved spectroscopy from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey to construct a sample of 10 galaxies where Baldwin–Philips–Terlevich line flux ratio diagnostics classify each galaxy’s central 3" spectrum as LINER or star-forming, while >10% of the spaxels in the galaxy’s MaNGA footprint are classified as Seyfert. We obtain Chandra observations of these 10 galaxies with off-nuclear Seyfert regions to determine whether AGNs are actually present in them. Our main result is that 7–10 (depending on strictness of criteria) of the galaxies host one or more X-ray AGNs, even though none of them were classified as AGNs based on their single-fiber optical spectra. We find that these AGNs were not identified in the single-fiber spectra because they are AGNs in the nuclei of companion galaxies, low-luminosity AGNs, dust-obscured AGNs, and/or flickering AGNs. In summary, we find that off-nuclear AGN signatures may increase the number of known AGNs by a factor of two over what conventional single nuclear fiber spectra identify. Our results show that spatially resolved spectroscopy can be leveraged to reveal a more complete census of AGNs that are traditionally missed by single-fiber spectra.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Dissecting Nearby Galaxies with piXedfit. II. Spatially Resolved Scaling Relations among Stars, Dust, and Gas

We study spatially resolved scaling relations among stars, dust, and gas in ten nearby spiral galaxies. In a preceding paper, we have derived spatially resolved properties of the stellar population and dust by a panchromatic spectral energy distribution fitting using piXedfit. Now, we investigate resolved star formation (${{\rm{\Sigma }}}_{{{\rm{H}}}_{2}}$–ΣSFR–Σ * ) and dust scaling relations. While the relations with all subgalactic regions of the galaxies are reasonably tight (σ ≲ 0.3 dex), we find that most of the scaling relations exhibit galaxy-to-galaxy variations in normalization and shape. Only two relations of Σdust–Σgas and Σdust–${{\rm{\Sigma }}}_{{{\rm{H}}}_{2}}$ do not show noticeable galaxy-to-galaxy variations among our sample galaxies. We further investigate the correlations among the scaling relations. We find significant correlations among the normalization of the ${{\rm{\Sigma }}}_{{{\rm{H}}}_{2}}$–ΣSFR–Σ * relations, which suggest that the galaxies with higher levels of resolved H 2 fraction (${f}_{{{\rm{H}}}_{2}}$) tend to have higher levels of resolved star formation efficiency (SFE) and specific star formation rate (sSFR). We also observe that the galaxies with higher levels of resolved dust-to-stellar mass ratios tend to have higher levels of resolved sSFR, SFE, and ${f}_{{{\rm{H}}}_{2}}$. Moreover, we find that the galaxies with higher global sSFR and less compact morphology tend to have higher levels of the resolved sSFR, SFE, and ${f}_{{{\rm{H}}}_{2}}$, which can explain the variations in the normalization of the ${{\rm{\Sigma }}}_{{{\rm{H}}}_{2}}$–ΣSFR–Σ * relationships. Overall, we observe indications of the contributions of both global and local factors in governing the star formation process in galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Origin of Star Formation in Early-type Galaxies Inferred from Spatially Resolved Spectroscopy

Abstract We investigate the origin of star formation activity in early-type galaxies with current star formation using spatially resolved spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory in the Sloan Digital Sky Survey (SDSS). We first identify star-forming early-type galaxies from the SDSS sample, which are morphologically early-type but show current star formation activity in their optical spectra. We then construct comparison samples with different combinations of star formation activity and morphology, which include star-forming late-type galaxies, quiescent early-type galaxies, and quiescent late-type galaxies. Our analysis of the optical spectra reveals that the star-forming early-type galaxies have two distinctive episodes of star formation, which is similar to late-type galaxies but different from quiescent early-type galaxies with a single star formation episode. Star-forming early-type galaxies have properties in common with star-forming late-type galaxies, which include stellar population, gas and dust content, mass, and environment. However, the physical properties of star-forming early-type galaxies derived from spatially resolved spectroscopy differ from those of star-forming late-type galaxies in the sense that the gas in star-forming early-type galaxies is more concentrated than their stars, and is often kinematically misaligned with stars. The age gradient of star-forming early-type galaxies also differs from those of star-forming late-type galaxies. Our findings suggest that the current star formation in star-forming early-type galaxies has an external origin including galaxy mergers or accretion gas from the cosmic web.

Astronomy & Astrophysics↗