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DOE OSTI2025

STEM Ptychographic Holography of Electric and Magnetic Potentials

The development of fast an efficient direct electron imaging detectors have enabled the advancement of phase-imaging techniques in STEM such as iterative ptychography. As beneficial as these techniques are to imaging phase objects, the information recorded in the raw data is due to phase gradients across the probe, so it can be challenging to reconstruct slowly varying phase at lower spatial frequencies such as those induced by electric or magnetic potentials within the specimen. STEM holography [1,2] is an interferometric 4D-STEM technique where electrons in the beam are coherently divided into a superposition of two or more spatially separated probes which are then scanned over the specimen. For example, in a two-beam superposition, the two probes form overlapping bright field discs at the detector which then interfere (left of Fig. 1). Furthermore, if one probe passes through vacuum while the other transmits through the specimen, the resulting relative phase shift can be measured by recording shifts in the interference pattern. STEM holography is thus directly sensitive to the phase of the probe relative to the reference beam, and this phase can be measured regardless of the convergence angle of the probe, unlike single beam ptychography.

Biological Sciences↗

DOE OSTI2021

Past agricultural land use affects multiple facets of ungulate antipredator behavior

Antipredator behavior affects prey fitness, prey demography, and the strength of ecological interactions. Although predator-prey interactions increasingly occur in habitats that experience multiple forms of human-generated disturbance, it is unclear how different forms of disturbance might affect antipredator behavior. Fire is a contemporary disturbance that has dramatic effects on terrestrial habitats. Such habitats may have also experienced past disturbances, like agricultural land use, that leave lasting legacies on habitat structure (e.g., overstory and understory composition). It is unclear how these past and present disturbances affect the use of different antipredator behaviors, like temporal avoidance and vigilance. We examined whether variation in disturbance regimes generates differences in ungulate antipredator behavior by using cameras to measure white-tailed deer vigilance and activity time across 24 longleaf pine woodlands that vary in past land use and contemporary fire regime. Regardless of land-use history, woodlands with high fire frequencies had 4 times less vegetation cover than low-fire woodlands, generating riskier habitats for deer; however, deer responded to fire with different antipredator strategies depending on land-use history. In nonagricultural woodlands, fire affected deer activity time such that activity was nocturnal in low-fire woodlands and crepuscular in high-fire woodlands. In post-agricultural woodlands, fire affected vigilance and not activity time such that deer were more vigilant in high-fire woodlands than in low-fire woodlands. Furthermore, these results suggest that ungulate antipredator behavior may vary spatially depending on past land use and contemporary fire regime, and such disturbances may generate “landscapes of fear” that persist for decades after agricultural use.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values d max were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Urbanization and malaria have a contextual relationship in endemic areas: A temporal and spatial study in Ghana

In West Africa, malaria is one of the leading causes of disease-induced deaths. Existing studies indicate that as urbanization increases, there is corresponding decrease in malaria prevalence. However, in malaria-endemic areas, the prevalence in some rural areas is sometimes lower than in some peri-urban and urban areas. Therefore, the relationship between the degree of urbanization, the impact of living in urban areas, and the prevalence of malaria remains unclear. This study explores this association in Ghana, using epidemiological data at the district level (2015–2018) and data on health, hygiene, and education. We applied a multilevel model and time series decomposition to understand the epidemiological pattern of malaria in Ghana. Then we classified the districts of Ghana into rural, peri-urban, and urban areas using administratively defined urbanization, total built areas, and built intensity. We converted the prevalence time series into cross-sectional data for each district by extracting features from the data. To predict the determinant most impacting according to the degree of urbanization, we used a cluster-specific random forest. We find that prevalence is impacted by seasonality, but the trend of the seasonal signature is not noticeable in urban and peri-urban areas. While urban districts have a slightly lower prevalence, there are still pockets with higher rates within these regions. These areas of high prevalence are linked to proximity to water bodies and waterways, but the rise in these same variables is not associated with the increase of prevalence in peri-urban areas. The increase in nightlight reflectance in rural areas is associated with an increased prevalence. We conclude that urbanization is not the main factor driving the decline in malaria. However, the data indicate that understanding and managing malaria prevalence in urbanization will necessitate a focus on these contextual factors. Finally, we design an interactive tool, ’malDecision’ that allows data-supported decision-making.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Establishing fluvial silicon regimes and their stability across the Northern Hemisphere

Abstract Fluvial silicon (Si) plays a critical role in controlling primary production, water quality, and carbon sequestration through supporting freshwater and marine diatom communities. Geological, biogeochemical, and hydrological processes, as well as climate and land use, dictate the amount of Si exported by streams. Understanding Si regimes—the seasonal patterns of Si concentrations—can help identify processes driving Si export. We analyzed Si concentrations from over 200 stream sites across the Northern Hemisphere to establish distinct Si regimes and evaluated how often sites moved among regimes over their period of record. We observed five distinct regimes across diverse stream sites, with nearly 60% of sites exhibiting multiple regime types over time. Our results indicate greater spatial and interannual variability in Si seasonality than previously recognized and highlight the need to characterize the watershed and climate variables that affect Si cycling across diverse ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Classification of Intensity Distributions of Transmission Eigenchannels of Disordered Nanophotonic Structures Using Machine Learning

Light-matter interaction optimization in complex nanophotonic structures is a critical step towards the tailored performance of photonic devices. The increasing complexity of such systems requires new optimization strategies beyond intuitive methods. For example, in disordered photonic structures, the spatial distribution of energy densities has large random fluctuations due to the interference of multiply scattered electromagnetic waves, even though the statistically averaged spatial profiles of the transmission eigenchannels are universal. Classification of these eigenchannels for a single configuration based on visualization of intensity distributions is difficult. However, successful classification could provide vital information about disordered nanophotonic structures. Emerging methods in machine learning have enabled new investigations into optimized photonic structures. In this work, we combine intensity distributions of the transmission eigenchannels and the transmitted speckle-like intensity patterns to classify the eigenchannels of a single configuration of disordered photonic structures using machine learning techniques. Specifically, we leverage supervised learning methods, such as decision trees and fully connected neural networks, to achieve classification of these transmission eigenchannels based on their intensity distributions with an accuracy greater than 99%, even with a dataset including photonic devices of various disorder strengths. Simultaneous classification of the transmission eigenchannels and the relative disorder strength of the nanophotonic structure is also possible. Our results open new directions for machine learning assisted speckle-based metrology and demonstrate a novel approach to classifying nanophotonic structures based on their electromagnetic field distributions. These insights can be of paramount importance for optimizing light-matter interactions at the nanoscale.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data.

Yang, Yi↗

DOE OSTI2025

Semiconductor-compatible topological digital alloys

Recently, GeSn alloys have attracted much interest for direct-gap infrared photonics and as potential topological materials which are compatible with the semiconductor industry. However, for photonics, the high-Sn content required leads to low detectivity, associated with poor material quality, and the (>35 %) Sn required for topological properties have been out of reach experimentally. Here, we demonstrate that by patterning the Sn distribution within Ge, the electronic properties have a far greater tunability than is possible with the random alloy. For the GeSn δ-digital alloy (DA) formed by confining Sn atoms in atomic layer(s) along the [111] direction of Ge, we show that ∼ 10 % Sn can lead to a triple-point semimetal. These findings are understood in terms of Sn ordering causing spatial separation of Sn and Ge band edges, leading to band inversion. This mechanism can also lead to a weak topological insulator, Weyl semimetal, and enables tunable direct bandgaps down to 2 meV, covering the entire infrared range. This DA induced topological properties are also identified in compound semiconductors, such as InAs 1-x Sb x , showing the general applicability of the DA design for realizing topological properties on conventional semiconductor platforms. Furthermore, our findings not only point to a new class of currently unexplored topological systems accessible by epitaxy, but also establish the promise of low-Sn GeSn DAs for application as infrared laser diodes and photodetectors in Si photonic integrated circuits and infrared image sensors.

3D topological semimetal↗

DOE OSTI2026

Protonic nickelate device networks for spatiotemporal neuromorphic computing

Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions. Here we introduce an integrated neuromorphic computing platform in which both nonlinear spatiotemporal processing and programmable memory are realized within a single perovskite nickelate material system. By engineering symmetric and asymmetric hydrogenated NdNiO 3 junction devices on the same wafer, we combine ultrafast, proton-mediated transient dynamics with stable multilevel resistance states. Networks of symmetric NdNiO 3 junctions exhibit emergent spatial interactions mediated by proton redistribution, while each node simultaneously provides short-term temporal memory, enabling nanosecond-scale operation with an energy cost of ~0.2 nJ per input. When interfaced with asymmetric output units serving as reconfigurable long-term weights, these networks allow both feature transformation and linear classification in the same material system. Leveraging these emergent interactions, the platform enables real-time pattern recognition and achieves high accuracy in spoken digit classification and early seizure detection, outperforming temporal-only or uncoupled architectures. These results position protonic nickelates as a compact, energy-efficient, CMOS-compatible platform that integrates processing and memory for scalable intelligent hardware.

Electrical and electronic engineering↗

DOE OSTI2023

Growing Pacific Linkage with Western North Atlantic Explosive Cyclogenesis

Abstract Explosive cyclones (ECs), defined as extratropical cyclones that experience normalized pressure drops of at least 24 hPa in 24 h, are impactful weather events in the North Atlantic sector, but year-to-year changes in the frequency and impacts of these storms are sizeable. To analyze the sources of this interannual variability, we track cases of ECs and dissect them into two spatial groups: those that formed near the east coast of North America (coastal) and those in the north central Atlantic (high latitude). The frequency of high-latitude ECs is strongly correlated with the North Atlantic Oscillation, a well-known feature, whereas coastal EC frequency is statistically linked with an atmospheric wave train emanating from the North Pacific in the last 30 years. This wave train pattern of alternating high and low pressure is associated with heightened upper-level divergence and Eady growth rates along the east coast of North America, likely resulting in a stronger correspondence between the atmospheric wave train and coastal EC frequency. Using coupled model experiments, we show that the tropical and North Pacific oceans are an important factor for this atmospheric wave train and the subsequent enhancement of seasonal baroclinicity in the North Atlantic.

58 GEOSCIENCES↗

DOE OSTI2024

Detailed Characterization of CZT Detector Response for Improved Coded-Aperture Imaging Performance

Gamma-ray imaging is a powerful method for locating and quantifying sources of radiation. The coded-aperture technique demonstrates superior angular resolution in comparison to other methods (e.g., Compton reconstruction). In this method, a mask constructed of highly attenuating material encodes the scene as a shadow pattern on a position-sensitive detector; this pattern can then be used to recreate the origin(s) of incident radiation. This is typically done through convolution of the mask and shadow patterns. Iterative methods which attempt to reconstruct the observed shadow pattern using a weighted combination of simulated patterns may also be employed. In either case, errors in event position reconstruction due to detector imperfections alter the shadow pattern and will therefore degrade system performance and may introduce imaging artifacts. These effects can be mitigated with a detailed understanding of such errors – allowing for the generation of representative simulations that include the errors and/or correction of raw imager data to remove the errors. We present a calibration process for a commercially available cadmium zinc telluride (CZT) gamma imager which provides a comprehensive characterization of the spatial and energy dependence of event reconstruction. By illuminating a mask featuring a regular grid of pinholes with a calibration source, the localized response of the detector can be measured with fine granularity. These local responses are combined to generate a full detector response map which can be used to distort simulations in a manner that is representative of the observed detector data. Details of the calibration procedure and an assessment of the impact of its end products on the performance of iterative imaging methods will be presented.

Ziock, Klaus-Peter↗

DOE OSTI2021

Search for topological defect dark matter with a global network of optical magnetometers

Ultralight bosons such as axion-like particles are viable candidates for dark matter. They can form stable, macroscopic field configurations in the form of topological defects that could concentrate the dark matter density into many distinct, compact spatial regions that are small compared with the Galaxy but much larger than the Earth. Here we report the results of the search for transient signals from the domain walls of axion-like particles by using the global network of optical magnetometers for exotic (GNOME) physics searches. We search the data, consisting of correlated measurements from optical atomic magnetometers located in laboratories all over the world, for patterns of signals propagating through the network consistent with domain walls. The analysis of these data from a continuous month-long operation of GNOME finds no statistically significant signals, thus placing experimental constraints on such dark matter scenarios.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Spatially and Temporally Resolved Velocimetry for Hypersonic Flows

The development of new hypersonic flight vehicles is limited by the physical understanding that may be obtained from ground test facilities. This has motivated the present development of a temporally and spatially resolved velocimetry measurement for Sandia National Laboratories (SNL) Hypersonic Wind Tunnel (HWT) using Femtosecond Laser Electronic Excitation Tagging (FLEET). First, a multi-line FLEET technique has been created for the first time and tested in a supersonic jet, allowing simultaneous measurements of velocities along multiple profiles in a flow. Secondly, two different approaches have been demonstrated for generating dotted FLEET lines. One employs a slit mask pattern focused into points to yield a dotted line, allowing for two- or three-component velocity measurements free of contamination between components. The other dotted-line approach is based upon an optical wedge array and yields a grid of points rather than a dotted line. Two successful FLEET measurement campaigns have been conducted in SNL’s HWT. The first effort established optimal diagnostic configurations in the hypersonic environment based on earlier benchtop reproductions, including validation of the use of a 267 nm beam to boost the measurement signal-to-noise ratio (SNR) with minimal risk of perturbing the flow and greater simplicity than a comparable resonant technique at 202 nm. The same FLEET system subsequently was reconstituted to demonstrate the ability to make velocimetry measurements of hypersonic turbulence in a realistic flow field. Mean velocity profiles and turbulence intensity profiles of the shear layer in the wake of a hypersonic cone model were measured at several different downstream stations, proving the viability of FLEET as a hypersonic diagnostic.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Scaling of Floods With Geomorphologic Characteristics and Precipitation Variability Across the Conterminous United States

Abstract Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Electromagnetic shower reconstruction in the ICARUS liquid argon time projection chamber detector

The ICARUS-T600 liquid argon time projection chamber (LArTPC) detector is taking data at shallow depth as the far detector of the Short Baseline Neutrino (SBN) program at Fermilab, to search for a possible sterile neutrino signal at $\Delta m^{2} \approx 1~\text{eV}^{2}$ with the Booster (BNB) and Main Injector (NuMI) neutrino beams at $\GeV{\sim 0.8}$ and $\GeV{\sim 2}$ average energies respectively. The LArTPC technology, developed by the ICARUS collaboration and now a standard in neutrino physics, offers impressive charged-particle imaging capabilities with $\sim1 \ \text{mm}$ spatial resolution, enabling efficient discrimination between track-like signatures (e.g., from muons, pions, and protons) and electromagnetic showers (from electrons and photons). Moreover, electron and photon signatures can be distinguished both with the calorimetric measurement of local energy depositions at the shower start and with the cm-scale conversion gap signature of photons. This contribution discusses event reconstruction at ICARUS focusing on Pandora, a multi-algorithm pattern recognition software widely used in LArTPC experiments. Over a hundred Pandora algorithms and tools are used to reconstruct cosmic rays and neutrino interactions in the ICARUS detector. Recent developments have focused on the reconstruction of electromagnetic shower signatures, crucial to ensure a robust and efficient reconstruction of charged-current $\nu_e$ interactions, which serve as a key signature of sterile neutrino oscillations at SBN. In this contribution, recent improvements to the reconstruction are discussed, focusing on the discrimination between tracks and electromagnetic showers using neutrino simulations and data.

Triozzi, Riccardo [Padua U.; INFN, Padua] (ORCID:0↗

DOE OSTI2025

Next generation Arctic vegetation maps: Aboveground plant biomass and woody dominance mapped at 30 m resolution across the tundra biome

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m −2 ) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ∼6000 g m −2 (mean ≈ 350 g m −2 ), while predicted values ranged from 0 to ∼4000 g m −2 (mean ≈ 275 g m −2 ), resulting in model validation root-mean-squared-error (RMSE) ≈ 400 g m −2 and R 2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling.

Climate change↗

DOE OSTI2020

Limiting the number of quantisation levels of a harmonic lens as a method for improving the quality of the generated image

The characteristics (modulation transfer functions) of harmonic lenses made by direct laser patterning of resist are experimentally studied. The influence of technological errors in the manufacture of lenses on the formation of the point spread function is modelled for harmonic lenses with 32 and 256 quantisation levels. Direct laser writing is used to produce lenses with these numbers of levels. It is experimentally shown that the modulation transfer function of the harmonic lens with 32 quantisation levels is significantly greater at high spatial frequencies (50 mm{sup −1}). (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

SDSS-IV MaNGA: stellar rotational support in disc galaxies versus central surface density and stellar population age

ABSTRACT We investigate how the stellar rotational support changes as a function of spatially resolved stellar population age ($D_n4000$) and relative central stellar surface density ($\Delta \Sigma _1$) for MaNGA isolated/central disc galaxies. We find that the galaxy rotational support indicator $\lambda _{R_\mathrm{e}}$ varies smoothly as a function of $\Delta \Sigma _1$ and $D_n4000$. $D_n4000$ versus $\Delta \Sigma _1$ follows a ‘J-shape’, with $\lambda _{R_\mathrm{e}}$ contributing to the scatters. In this ‘J-shaped’ pattern rotational support increases with central $D_n4000$ when $\Delta \Sigma _1$ is low but decreases with $\Delta \Sigma _1$ when $\Delta \Sigma _1$ is high. Restricting attention to low-$\Delta \Sigma _1$ (i.e. large-radius) galaxies, we suggest that the trend of increasing rotational support with $D_n4000$ for these objects is produced by a mix of two different processes, a primary trend characterized by growth in $\lambda _{R_\mathrm{e}}$ along with mass through gas accretion, on top of which disturbance episodes are overlaid, which reduce rotational support and trigger increased star formation. An additional finding is that star-forming galaxies with low $\Delta \Sigma _1$ have relatively larger radii than galaxies with higher $\Delta \Sigma _1$ at fixed stellar mass. Assuming that these relative radii rankings are preserved while galaxies are star forming then implies clear evolutionary paths in central $D_n4000$ versus $\Delta \Sigma _1$. The paper closes with comments on the implications that these paths have for the evolution of pseudo-bulges versus classical bulges. The utility of using $\rm D_n4000$–$\Delta \Sigma _1$ to study $\lambda _{R_\mathrm{e}}$ reinforces the notion that galaxy kinematics correlate both with structure and with stellar-population state, and indicates the importance of a multidimensional description for understanding bulge and galaxy evolution.

Wang, Xiaohan (ORCID:0009000283121002)↗