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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Experimental Investigation of Polymer Induced Fouling of Heater Tubes in the First-Ever Polymer Flood Pilot on Alaska North Slope

Mineral fouling in heat exchangers has been extensively investigated by researchers in recent times. The oil and gas industry has a long history of fouling issues in production systems as a result of produced fluids treatment. Due to decline in production rates in oilfields new technologies are being developed and field tested in pilots. Polymer flooding is one such technology that involves addition of polymers to injection fluids to enhance oil production. A polymer flood pilot has been set up in the Schrader Bluff viscous oil reservoir at Milne Point field on the Alaska North Slope (ANS). The results from the pilot are encouraging, however a major concern of the operator is the influence of polymer on the production system after breakthrough, especially the fouling in heat exchangers. This study investigates the propensity of polymer fouling on the heater tubes as a function of different variables, with the ultimate goal of determining safe and efficient operating conditions. This work applies a multi-experimental approach to study the severity of polymer-induced fouling in both dynamic and static states of produced fluids as well as studying the stability of polymer solutions at different temperatures. A unique experimental setup was designed and developed in-house to simulate the fouling process on the heating tube. The influence of heating tube skin temperature, tube material, and polymer concentration on fouling tendency was investigated. Each test was run five times with the same tube, and in each run, the freshly prepared synthetic brine and polymer solution was heated from 77°F to 122°F to mimic field-operating conditions. The heating time and fouling amount were recorded for each run. Dynamic Scale Loop (DSL) tests were conducted to study fouling due to polymer at different temperatures (165°F to 350°F) in a dynamic state of fluid flow where the fluids mimic the residence time of fluids in the heat exchanger on the field pilot. Cloud point measurement has also been conducted to find the critical temperature at which the polymer in solution becomes unstable and precipitates out. The morphology and composition of the deposit samples were analyzed by environmental scanning electron microscopy (ESEM) and X-ray diffraction (XRD), respectively. It was found that the presence of polymer in produced fluids would aggravate the fouling issues on both carbon steel and stainless-steel surfaces at all tested skin temperatures. Only higher skin temperatures of 250°F and 350°F could cause polymer-induced fouling issues on the copper tube surface, and the fouling tendency increased with polymer concentration. At the lower skin temperatures of 165°F, no polymer-induced fouling was identified on the copper tube. A critical temperature that is related to the cloud point of the polymer solution was believed to exist, below which polymer-induced fouling would not occur, and only mineral scale was deposited but above which the polymer would aggravate the fouling issue. The cloud point of the tested polymer solution was determined to be between 220°F and 230°F. In the DSL tests it was found that at higher skin temperatures of 250°F and 350°F tube blocking was observed in the DSL tests whereas the tests at 165°F and 200°F did not show any tube blocking in the same time period. These experiments also manifested the influence of cloud point of the solution as deposit rate increased significantly in both carbon steel and stainless-steel tubes when the skin temperature was higher than the solution cloud point. The results of this study have provided guidance to the operator for the field-operations.

Dhaliwal, Anshul↗
DOE OSTI2022

Potential-induced wetting and dewetting in hydrophobic nanochannels for mass transport control

Wetting and dewetting transitions play a central role in controlling the hydrophobicity of the lining of biological channels in order to regulate aqueous solution permeation. Understanding of the operational characteristics of biological nanochannels led to experimental efforts to mimic their behavior and to achieve potential-induced, repeatedly-switchable wettability transitions in synthetic nanochannels in the early 2010s. Since then, research has identified conditions needed to produce reversible wettability transitions using a number of different environmental stimuli - such as light, pH, and electrostatic charge - in addition to potential. Furthermore, nascent understanding of the underlying phenomena in synthetic nanochannels was rapidly followed by practical applications, including oil-water separations, drug release, and electroactive flow control based on switchable wettability. Furthermore, more practical applications are being developed continuously, as the physical and chemical principles that govern hydrophobic gating at the nanoscale are further elucidated, making it possible to exploit wettability as a design element in nanofluidic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Controlling saturation to improve per- and polyfluoroalkyl substance (PFAS) removal in biochar-amended stormwater bioretention systems

Black carbon-amended bioretention systems are an increasingly popular strategy for the removal of organic contaminants, including poly- and perfluoroalkyl substances (PFASs) and other trace organic contaminants (TOrCs), from urban stormwater. Many PFASs preferentially accumulate at the air–water interface, but detention time requirements for stormwater bioretention systems typically result in full saturation of the bioretention systems, effectively removing their air–water interfaces. This study assessed the effect of bioretention system saturation on removal of PFASs, metals, and hydrophilic TOrCs. A field-aged mixture of 40% v/v sand, 30% v/v zeolite, and 30% v/v biochar was packed into columns which were operated with hydraulic controls to remain unsaturated or fully saturated throughout the duration of the experiment. Twenty-four storm events sized to a 95th percentile storm at a California, United States military site were simulated using synthetic stormwater fortified with aqueous film forming foam-derived PFASs, TOrCs, and metals to mimic real-world conditions. Unsaturated conditions outperformed saturated conditions for removal of all PFASs analyzed. A simulated inadvertent system perturbation (i.e. flooding event) in the unsaturated columns did not result in significantly greater mobilization of PFAS mass, suggesting that more sorption to the biochar occurred as a result of the transient retention of PFASs at the air–water interface reducing kinetic sorption limitations. Overall, maintaining unsaturated conditions in a biofilter may extend the sorptive filter lifetime for PFASs by up to 83%. The results have implications for bioretention system application and design for PFAS removal in contaminated catchment areas.

13 HYDRO ENERGY↗
DOE OSTI2023

Accelerated Weak Signal Search Using Mode Entanglement and State Swapping

Quantum fluctuations constitute the primary noise barrier limiting cavity-based axion-dark-matter searches. In an experiment designed to mimic a real axion search, we employ a quantum enhanced sensing technique to detect a synthetic axionlike microwave tone at an unknown frequency weakly coupled to a resonator, demonstrating a factor-of-5.6 acceleration relative to a quantum limited search for the same tone. This speed-up is achieved by dynamically coupling the resonator mode to a second (readout) mode with balanced swapping and two-mode squeezing interactions, which results in both visibility-bandwidth and peak-visibility increase. A small fractional imbalance between the two interaction rates yields further scan-rate enhancement and we demonstrate that an eightfold acceleration can be achieved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Next-generation poly-L-histidine formulations for miRNA mimic delivery

Many diseases, especially cancer, are caused by the abnormal expression of non-coding microRNAs (miRNAs), which regulate gene expression, leading to the development of miRNA-based therapeutics. Synthetic miRNA inhibitors have shown promising efficacy in blocking the activity of aberrant miRNAs that are upregulated in disease-specific pathologies. On the other hand, miRNAs that aid in preventing certain diseases and are reduced in expression in the disease state need different strategies. To tackle this, miRNA mimics, which mimic the activity of endogenous miRNAs, can be delivered for those miRNAs downregulated in different disease states. However, the delivery of miRNA mimics remains a challenge. Here, we report a cationic polylactic-co-glycolic acid (PLGA)-poly-L-histidine delivery system to deliver miRNA mimics. We chose miR-34a mimics as a proof of concept for miRNA delivery. miR-34a-loaded PLGA-poly-L-histidine nanoparticles (NPs) were formulated and biophysically characterized to analyze the structural properties of miRNA mimic-loaded NPs. In vitro efficacy was determined by investigating miR-34a and downstream target levels and performing cell viability and apoptosis assays. We confirmed in vivo efficacy through prolonged survival of miR-34a NP-treated A549-derived xenograft mice treated intratumorally. The results of these studies establish PLGA-poly-L-histidine NPs as an effective delivery system for miRNA mimics for treating diseases characterized by downregulated miRNAs.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2003

Fan Beam Emission Tomography for Estimating Scalar Properties in Laminar Flames

A new method of estimating temperatures and gas species concentrations (CO2 and H2O) in a laminar flame is reported. The path-integrated, spectral radiation intensities emitted from a laminar flame at multiple wavelengths and view angles are calculated using a narrow band radiation model. Synthetic data, in the form of radial profiles of temperature and gas concentrations, are used in these calculations. The calculations mimic measurements that would theoretically be obtained using a mid-infrared spectrometer with a scanner. The path integrated spectral radiation intensities are deconvoluted using a maximum likelihood estimation method in conjunction with an iterative scheme. The deconvolution algorithm accounts for the self-absorption of radiation by the intervening gases, and provides the local temperature and gas species concentrations. The deconvoluted temperatures and gas concentrations are compared with the synthetic data used for calculating the spectral radiation intensities. The deconvoluted temperatures and gas species concentrations are within 0.5 % of the synthetic data. The deconvolution algorithm is expected to provide combustion researchers with an easy method of obtaining the radial profiles of major gas species concentrations and temperatures in laminar flames non-intrusively using a mid-infrared spectrometer with a scanner.

Lim, Jongmook↗
DOE OSTI2025

Evaluating quenching in cosmological simulations of galaxy formation with spectral covariance in the optical window

ABSTRACT Cosmological hydrodynamical simulations provide valuable insights on galaxy evolution when coupled with observational data. Comparisons with real galaxies are typically performed via scaling relations of the observables. Here, we follow an alternative approach based on the spectral covariance in a model-independent way. We build upon previous work by Sharbaf et al. that studied the covariance of high-quality SDSS (Sloan Digital Sky Survey) continuum-subtracted spectra in a relatively narrow range of velocity dispersion ($\sigma \in [100,150]$ km s$^{-1}$). Here, the same analysis is applied to synthetic data from the eagle and IllustrisTNG100 simulations, to assess the ability of these runs to mimic real galaxies. The real and simulated spectra are consistent regarding spectral covariance, although with subtle differences that can inform the implementation of subgrid physics. Spectral fitting done a posteriori on stacks segregated with respect to latent space reveals that the first principal component (PC1) is predominantly influenced by the stellar age distribution, with an underlying age–metallicity degeneracy. Good agreement is found regarding star formation prescriptions but there is disagreement with active galactic nucleus (AGN) feedback, that also affects the subset of quiescent galaxies. We show a substantial difference in the implementation of the AGN subgrid prescriptions, regarding central black hole seeding, that could lead to the mismatch. Differences are manifest between these two simulations in the star formation histories stacked with respect to latent space. We emphasize that this methodology only relies on the spectral covariance to assess whether simulations provide a true representation of galaxy formation.

Sharbaf, Z. (ORCID:0009000450545946)↗
NASA NTRSDate not supplied

Modeling Stellar Jitter and its Effects on the Stellar Spectrum for More Precise Radial Velocity Measurements

Detection of Earth-size exoplanets is a technological and data analysis challenge. Future progress in Earth-mass exoplanet detection is expected from the development of extreme precision radial velocity (EPRV) measurements. The required precision of these measurements necessitates developing a new physics-based data analysis methodology to discriminate planetary signals from host-star-related effects, taking stellar variability and instrumental uncertainties into account. In this work, we investigate and quantify stellar disturbances of the planet-hosting solar-type star HD121504 from 3D radiative modeling obtained with the StellarBox code. The model was used to determine the statistical properties of the turbulent plasma and to obtain synthetic spectroscopic observations for several Fe I lines at different locations on the stellar disk to mimic high-resolution spectroscopic observations. Presented are the initial results of our effort to characterize stellar jitter properties as well as future objectives.

SMD↗
DOE OSTI2022

Modelling Hyperglycaemia in an Epithelial Membrane Model: Biophysical Characterisation

Biomimetic models are valuable platforms to improve our knowledge on the molecular mechanisms governing membrane-driven processes in (patho)physiological conditions, including membrane permeability, transport, and fusion. However, current membrane models are over simplistic and do not include the membrane’s lipid remodelling in response to extracellular stimuli. Our study describes the synthesis of glycated dimyristoyl-phosphatidylethanolamine (DMPE-glyc), which was structurally characterised by mass spectrometry (ESI-MS) and quantified by NMR spectroscopy to be further incorporated in a complex phospholipid (PL) membrane model enriched in cholesterol (Chol) and (glyco)sphingolipids (GSL) designed to mimic epithelial membranes (PL/Chol/GSL) under hyperglycaemia conditions. Characterisation of synthesised DMPE-glyc adducts by tandem mass spectrometry (ESI-MS/MS) show that synthetic DMPE-glyc adducts correspond to Amadori products and quantification by 1H NMR spectroscopy show that the yield of glycation reaction was 8%. The biophysical characterisation of the epithelial membrane model shows that excess glucose alters the thermotropic behaviour and fluidity of epithelial membrane models likely to impact permeability of solutes. The epithelial membrane models developed to mimic normo- and hyperglycaemic scenarios are the basis to investigate (poly)phenol-lipid and drug–membrane interactions crucial in nutrition, pharmaceutics, structural biochemistry, and medicinal chemistry.

Reis, Ana (ORCID:0000000245379902)↗
NASA NTRS2017

NASA's Bio-Inspired Acoustic Absorber Concept

Transportation noise pollutes our worlds cities, suburbs, parks, and wilderness areas. NASAs fundamental research in aviation acoustics is helping to find innovative solutions to this multifaceted problem. NASA is learning from nature to develop the next generation of quiet aircraft.The number of road vehicles and airplanes has roughly tripled since the 1960s. Transportation noise is audible in nearly all the counties across the US. Noise can damage your hearing, raise your heart rate and blood pressure, disrupt your sleep, and make communication difficult. Noise pollution threatens wildlife when it prevents animals from hearing prey, predators, and mates. Noise regulations help drive industry to develop quieter aircraft. Noise standards for aircraft have been developed by the International Civil Aviation Organization and adopted by the US Federal Aviation Administration. The US National Park Service is working with the Federal Aviation Administration to try to balance the demand for access to the parks and wilderness areas with preservation of the natural soundscape. NASA is helping by conceptualizing quieter, more efficient aircraft of the future and performing the fundamental research to make these concepts a reality someday. Recently, NASA has developed synthetic structures that can absorb sound well over a wide frequency range, and particularly below 1000 Hz, and which mimic the acoustic performance of bundles of natural reeds. We are adapting these structures to control noise on aircraft, and spacecraft. This technology might be used in many other industrial or architectural applications where acoustic absorbers have tight constraints on weight and thickness, and may be exposed to high temperatures or liquids. Information about this technology is being made available through reports and presentations available through the NASA Technical Report Server, http:ntrs.nasa.gov. Organizations who would like to collaborate with NASA or commercialize NASAs technology are encouraged to contact the NASA Glenn Technology Transfer Office, https:technology.grc.nasa.gov. The NASA Glenn Office of Education https:www.nasa.govcentersglenneducationindex.html and the NASA Glenn Virtual Interchange for Nature-Inspired Exploration https:www.grc.nasa.govvine are also helping to make research like this accessible to the public and students of all ages.

Acoustics↗
DOE OSTI2025

Prediction of electric and magnetic fields from spectral data using machine learning algorithms for Doppler-free saturation spectroscopy diagnostics

The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.

Doppler-free saturation spectroscopy↗
DOE OSTI2023

Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning

This article investigates the applicability of a recently proposed, nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-parameterized model. This is particularly relevant to nonlinear dynamical systems where modelling approaches involve the use of physics-based and data-driven components. In such cases, the data-driven components, where analytical descriptions of the physical processes are not readily available, are often prone to overfitting, meaning that the empirical aspects of these models will often involve the calibration of an unnecessarily large number of parameters. While an overparameterized model may fit the observed data well, such models may be inadequate for making predictions in regimes that are different from those wherein the data were recorded. In view of this, it is desirable to not only calibrate the model parameters, but also identify the optimal compromise between data fit and model complexity. In this article, we exhibit the optimal model discovery for an aeroelastic system wherein the structural dynamics are well-known and described by a differential equation model, coupled with a semi-empirical aerodynamic model for laminar separation flutter, resulting in low-amplitude limit cycle oscillations (LCO). To illustrate the performance of the algorithm, in this article, we use synthetic data and demonstrate the ability of the algorithm to correctly rediscover the optimal model and model parameters, given a known data-generating model. The synthetic data are generated from a forward simulation of a known differential equation model with parameters selected so as to mimic the dynamics observed in wind-tunnel experiments. Subsequently, we demonstrate the performance of the algorithm for model selection using noisy LCO data from wind tunnel experiments. As there is no ground truth available for the experimental data case, we provide a comparison between NSBL and Bayesian model selection to validate the results, and demonstrate the use of NSBL as an efficient alternative to traditional methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Solutal-buoyancy-driven intertwining and rotation of patterned elastic sheets

Abstract The intertwining of strands into 3D spirals is ubiquitous in biology, enabling functions from information storage to maintenance of cell structure and directed locomotion. In synthetic systems, entwined fibers can provide superior mechanical properties and act as artificial muscle or structural reinforcements. Unlike structures in nature, the entwinement of synthetic materials typically requires application of an external stimulus, such as mechanical actuation, light, or a magnetic field. Herein, we use computational modeling to design microscale sheets that mimic biology by transducing chemical energy into mechanical action, and thereby self-organize and interlink into 3D spirals, which spontaneously rotate. These flexible sheets are immersed in a fluid-filled microchamber that encompasses an immobilized patch of catalysts on the bottom wall. The sheets themselves can be passive or active (coated with catalyst). Catalytic reactions in the solution generate products that occupy different volumes than the reactants. The resulting density variations exert a force on the fluid (solutal buoyancy force) that causes motion, which in turn drives the interlinking and collective swirling of the sheets. The individual sheets do not rotate; rotation only occurs when the sheets are interlinked. This level of autonomous, coordinated 3D structural organization, intertwining, and rotation is unexpected in synthetic materials systems operating without external controls. Using physical arguments, we identify dimensionless ratios that are useful in scaling these ideas to other systems. These findings are valuable for creating materials that act as “machines”, and directing soft matter to undergo self-sustained, multistep assembly that is governed by intrinsic chemical reactions.

Manna, Raj Kumar (ORCID:0000000322729487)↗
DOE OSTI2020

Constraining the masses of high-redshift clusters with weak lensing: Revised shape calibration testing for the impact of stronger shears and increased blending

Weak lensing measurements suffer from well-known shear estimation biases, which can be partially corrected for with the use of image simulations. Here we present an analysis of simulated images that mimic Hubble Space Telescope/Advance Camera for Surveys observations of high-redshift galaxy clusters, including cluster specific issues such as non-weak shear and increased blending. Our synthetic galaxies have been generated to have similar observed properties as the background-selected source samples studied in the real images. First, we used simulations with galaxies placed on a grid to determine a revised signal-to-noise-dependent (S/N KSB ) correction for multiplicative shear measurement bias, and to quantify the sensitivity of our KSB+ bias calibration to mismatches of galaxy or PSF properties between the real data and the simulations. Next, we studied the impact of increased blending and light contamination from cluster and foreground galaxies, finding it to be negligible for high-redshift (z > 0.7) clusters, whereas shear measurements can be affected at the ~1% level for lower redshift clusters given their brighter member galaxies. Finally, we studied the impact of fainter neighbours and selection bias using a set of simulated images that mimic the positions and magnitudes of galaxies in Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) data, thereby including realistic clustering. While the initial SExtractor object detection causes a multiplicative shear selection bias of –0.028 ± 0.002, this is reduced to –0.016 ± 0.002 by further cuts applied in our pipeline. Given the limited depth of the CANDELS data, we compared our CANDELS-based estimate for the impact of faint neighbours on the multiplicative shear measurement bias to a grid-based analysis, to which we added clustered galaxies to even fainter magnitudes based on Hubble Ultra Deep Field data, yielding a refined estimate of ~ –0.013. Our sensitivity analysis suggests that our pipeline is calibrated to an accuracy of ~0.015 once all corrections are applied, which is fully sufficient for current and near-future weak lensing studies of high-redshift clusters. As an application, we used it for a refined analysis of three highly relaxed clusters from the South Pole Telescope Sunyaev-Zeldovich survey, where we now included measurements down to the cluster core (r > 200 kpc) as enabled by our work. Compared to previously employed scales (r > 500 kpc), this tightens the cluster mass constraints by a factor 1.38 on average.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2022

The Dynamic Structure of Coronal Hole Boundaries

The boundaries of solar coronal holes are difficult to uniquely define observationally but are sites of interest in part because the slow solar wind appears to originate there. The aim of this article is to explore the dynamics of interchange magnetic reconnection at different types of coronal hole boundaries—namely streamers and pseudostreamers—and their implications for the coronal structure. We describe synthetic observables derived from three-dimensional magnetohydrodynamic simulations of the atmosphere of the Sun in which coronal hole boundaries are disturbed by flows that mimic the solar supergranulation. Our analysis shows that interchange reconnection takes place much more readily at the pseudostreamer boundary of the coronal hole. As a result, the portion of the coronal hole boundary formed by the pseudostreamer remains much smoother, in contrast to the highly distorted helmet-streamer portion of the coronal hole boundary. Our results yield important new insights on coronal hole boundary regions, which are critical in coupling the corona to the heliosphere as the formation regions of the slow solar wind.

V Aslanyan↗
DOE OSTI2026

N -Terminal Octylated Peptoid Hydrogels as 3D-Printable Cell Scaffolds and Proteolytically Robust Cargo Depots

Supramolecular hydrogels that mimic the extracellular matrix (ECM) represent promising materials for tissue engineering and drug delivery. However, conventional hydrogels formed via the self-assembly of natural or synthetic building blocks often face a trade-off between biological functionality and biochemical stability, limiting their utility in long-term or protease-rich environments. Peptoids, a class of peptide-inspired, sequence-defined polymers, offer a compelling alternative due to their exceptional proteolytic resistance and bioactivity. Despite this potential, the development of supramolecular peptoid hydrogels has been hindered by the absence of backbone hydrogen bond donors, which limits long-range ordering necessary for efficient hydrogel formation. This work describes a short peptoid functionalized at the N-terminus with an octyl chain that readily self-assembles into hydrogels. Hydrophobic interactions among pendant octyl groups promote directional peptoid packing into highly ordered nanosheets, which interconnect to form a porous hydrogel network. These hydrogels exhibit tunable viscoelasticity, shear-thinning, and self-healing properties, enabling their use as inks for extrusion-based 3D printing. They support NIH-3T3 fibroblast adhesion, spreading, and proliferation, maintaining greater than 95% cell viability over 4 days. Moreover, the hydrogels retain their macroscopic integrity under protease-rich conditions, enabling sustained cargo release and uniform cellular uptake. Together, this study demonstrates a class of supramolecular peptoid hydrogelators that integrate biocompatibility, 3D printability, and proteolytic stability, providing a versatile platform for ECMmimetic scaffolds in regenerative medicine and long-term therapeutic delivery.

cargo delivery↗
DOE OSTI2022

Realistic galaxy image simulation via score-based generative models

ABSTRACT We show that a denoising diffusion probabilistic model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real data set. We quantify the similarity by borrowing from the deep generative learning literature, using the ‘Fréchet inception distance’ to test for subjective and morphological similarity. We also introduce the ‘synthetic galaxy distance’ metric to compare the emergent physical properties (such as total magnitude, colour, and half-light radius) of a ground truth parent and synthesized child data set. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as adversarial networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate inpainting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we ‘DESI-fy’ cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.

79 ASTRONOMY AND ASTROPHYSICS↗