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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites

Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗
DOE OSTI2021

The Dark Energy Survey supernova programme: modelling selection efficiency and observed core-collapse supernova contamination

ABSTRACT The analysis of current and future cosmological surveys of Type Ia supernovae (SNe Ia) at high redshift depends on the accurate photometric classification of the SN events detected. Generating realistic simulations of photometric SN surveys constitutes an essential step for training and testing photometric classification algorithms, and for correcting biases introduced by selection effects and contamination arising from core-collapse SNe in the photometric SN Ia samples. We use published SN time-series spectrophotometric templates, rates, luminosity functions, and empirical relationships between SNe and their host galaxies to construct a framework for simulating photometric SN surveys. We present this framework in the context of the Dark Energy Survey (DES) 5-yr photometric SN sample, comparing our simulations of DES with the observed DES transient populations. We demonstrate excellent agreement in many distributions, including Hubble residuals, between our simulations and data. We estimate the core collapse fraction expected in the DES SN sample after selection requirements are applied and before photometric classification. After testing different modelling choices and astrophysical assumptions underlying our simulation, we find that the predicted contamination varies from 7.2 to 11.7 per cent, with an average of 8.8 per cent and an r.m.s. of 1.1 per cent. Our simulations are the first to reproduce the observed photometric SN and host galaxy properties in high-redshift surveys without fine-tuning the input parameters. The simulation methods presented here will be a critical component of the cosmology analysis of the DES photometric SN Ia sample: correcting for biases arising from contamination, and evaluating the associated systematic uncertainty.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

A Bispectral Composite Threshold Approach for Automatic Cloud Detection in VIIRS Imagery

The detection of clouds in satellite imagery has a number of important applications in weather and climate studies. The presence of clouds can alter the energy budget of the Earth‐atmosphere system through scattering and absorption of shortwave radiation and the absorption and re‐emission of infrared radiation at longer wavelengths. The scattering and absorption characteristics of clouds vary with the microphysical properties of clouds, hence the cloud type. Thus, detecting the presence of clouds over a region in satellite imagery is important in order to derive atmospheric or surface parameters that give insight into weather and climate processes. For many applications however, clouds are a contaminant whose presence interferes with retrieving atmosphere or surface information. In these cases, is important to isolate cloud‐free pixels, used to retrieve atmospheric thermodynamic information or surface geophysical parameters, from cloudy ones. This abstract describes an application of a two‐channel bispectral composite threshold (BCT) approach applied to VIIRS imagery. The simplified BCT approach uses only the 10.76 and 3.75 micrometer spectral channels from VIIRS in two spectral tests; a straight‐forward infrared threshold test with the longwave channel and a shortwave - longwave channel difference test. The key to the success of this approach as demonstrated in past applications to GOES and MODIS data is the generation of temporally and spatially dependent thresholds used in the tests from a previous number of days at similar observations to the current data. The paper and subsequent presentation will present an overview of the approach and intercomparison results with other satellites, methods, and against verification data.

LaFontaine Frank J.↗
DOE OSTI2022

The Impact of Freeze‐Thaw History on Soil Carbon Response to Experimental Freeze‐Thaw Cycles

Abstract Freeze‐thaw is a disturbance process in cold regions where permafrost soils are becoming vulnerable to temperature fluctuations above 0°C. Freeze‐thaw alters soil physical and biogeochemical properties with implications for carbon persistence and emissions in Arctic landscapes. We examined whether different freeze‐thaw histories in two soil systems led to contrasting biogeochemical responses under a laboratory‐controlled freeze‐thaw incubation. We investigated controls on carbon composition through Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR‐MS) to identify nominal carbon oxidation states and relative abundances of aliphatic‐type carbon molecules in both surface and subsurface soils. Soil cores (∼60 cm‐depth) were sampled from two sites in Alaskan permafrost landscapes with different in situ freeze‐thaw characteristics: Healy (>40 freeze‐thaw cycles annually) and Toolik (<15 freeze‐thaw cycles annually). FT‐ICR‐MS was coupled with in situ temperature data and soil properties (i.e., soil texture, mineralogy) to assess (a) differences in soil organic matter composition associated with previous freeze‐thaw history and (b) sensitivity to experimental freeze‐thaw in the extracted cores. Control (freeze‐only) samples showed greater carbon oxidation in Healy soils compared with Toolik, even in lower mineral horizons where freeze‐thaw history was comparable across both sites. Healy showed the most loss of carbon compounds following experimental freeze‐thaw in the lower mineral depths, including a decrease in aliphatics. Toolik soils responded more slowly to freeze‐thaw as shown by intermediary carbon oxidation distributed across multiple carbon compound classes. Variations in the response of permafrost carbon chemistry to freeze‐thaw is an important factor for predicting changes in soil function as permafrost thaws in high northern latitudes.

Rooney, Erin C.↗
DOE OSTI2023

Applications of physics informed neural operators

Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code , an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Time for a drought experiment: Do you know your plants’ water status?

Abstract Drought stress is an increasing concern because of climate change and increasing demands on water for agriculture. There are still many unknowns about how plants sense and respond to water limitation, including which genes and cellular mechanisms are impactful for ecology and crop improvement in drought-prone environments. A better understanding of plant drought resistance will require integration of several research disciplines. A common set of parameters to describe plant water status and quantify drought severity can enhance data interpretation and research integration across the research disciplines involved in understanding drought resistance and would be especially useful in integrating the flood of genomic data being generated in drought studies. Water potential (ψw) is a physical measure of the free energy status of water that, along with related physiological measurements, allows unambiguous description of plant water status that can apply across various soil types and environmental conditions. ψw and related physiological parameters can be measured with relatively modest investment in equipment and effort. Thus, we propose that increased use of ψw as a fundamental descriptor of plant water status can enhance the insight gained from many drought-related experiments and facilitate data integration and sharing across laboratories and research disciplines.

Juenger, Thomas E. (ORCID:0000000195509288)↗
DOE OSTI2022

Increasing the Pressure‐Free Stripping Capacity of the Lithium Metal Anode in Solid‐State‐Batteries by Carbon Nanotubes

Abstract Lithium metal is the key anode material for solid‐state‐batteries as its successful implementation will drastically increase their energy and power densities. However, anode contact loss during stripping leads to dendrites upon plating and subsequent cell failure. Design strategies to mitigate these issues are crucial to enable the use of lithium metal anodes. This paper reports the dissolution kinetics of composite anodes made of lithium metal and carbon nanotubes (CNTs) with a garnet‐type solid electrolyte (SE). In addition to an enhancement of the effective diffusion within the anode, its dissolution is fundamentally changed from being 2D to 3D. By maintaining contact with the SE, the CNTs facilitate lithium transport to the interface, which yields more than 20 mAh cm −2 discharge capacity at 100 µ A cm −2 without the application of external stack pressure ( > 1 MPa). Conclusions drawn from electrochemical data on the anode microstructure are validated using cryo‐focused‐ion‐beam scanning electron microscopy and correlated with the mechanical properties. Micro‐indentation, acoustic analysis, and stress–strain testing show that mechanical properties of the anode, like yield strength and hardness, are adjustable. Overall, it is shown that the mechanical and electrochemical properties of Li–CNT composite electrodes can be tailored to suit the requirements of a practical cell.

Fuchs, Till↗
DOE OSTI2022

A galaxy-driven model of type Ia supernova luminosity variations

ABSTRACT Type Ia supernovae (SNe Ia) are used as standardizable candles to measure cosmological distances, but differences remain in their corrected luminosities which display a magnitude step as a function of host galaxy properties such as stellar mass and rest-frame U−R colour. Identifying the cause of these steps is key to cosmological analyses and provides insight into SN physics. Here we investigate the effects of SN progenitor ages on their light-curve properties using a galaxy-based forward model that we compare to the Dark Energy Survey 5-yr SN Ia sample. We trace SN Ia progenitors through time and draw their light-curve width parameters from a bimodal distribution according to their age. We find that an intrinsic luminosity difference between SNe of different ages cannot explain the observed trend between step size and SN colour. The data split by stellar mass are better reproduced by following recent work implementing a step in total-to-selective dust extinction ratio (RV) between low- and high-mass hosts, although an additional intrinsic luminosity step is still required to explain the data split by host galaxy U−R. Modelling the RV step as a function of galaxy age provides a better match overall. Additional age versus luminosity steps marginally improve the match to the data, although most of the step is absorbed by the width versus luminosity coefficient α. Furthermore, we find no evidence that α varies with SN age.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

An underground, wireless, open-source, low-cost system for monitoring oxygen, temperature, and soil moisture

Abstract. The use of wireless sensor networks to measure soil parameters eliminates the need to remove sensors for field operations, such as tillage, thus allowing long-term measurements without multiple disturbances to soil structure. Wireless sensors also reduce above-ground cables and the risk of undesired equipment damage and potential data loss. However, implementing wireless sensor networks in field studies usually requires advanced and costly engineering knowledge. This study presents a new underground, wireless, open-source, low-cost system for monitoring soil oxygen, temperature, and soil moisture. The process of system design, assembly, programming, deployment, and power management is presented. The system can be left underground for several years without the need to change the battery. Emphasis was given on modularity so that it can be easily duplicated or changed if needed and deployed without previous engineering knowledge. Data from this type of system have a wide range of applications, including precision agriculture and high-resolution modelling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MEGASIM: Distribution and Detection of Earth Trojan Asteroids

Abstract Using N -body simulation results from the MEGASIM data set, we present spatial distributions of Earth Trojan Asteroids and assess the detectability of the population in current and next-generation ground-based astronomical surveys. Our high-fidelity Earth Trojan Asteroid (ETA) distribution maps show never-before-seen high-resolution spatial features that evolve over timescales up to 1 Gyr. The simulation was synchronized to start times and timelines of two observational astronomy surveys: (1) the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and (2) the Zwicky Transient Facility (ZTF). We calculate upper limits for the number of ETAs potentially observable with both the ZTF and LSST surveys. Due to the Yarkovsky Effect, we find no stable ETAs on billion-year timescales likely to be detected by any ETA survey, as no C-type or S-type ETAs (with H < 22 and H < 24, respectively) are likely to be stable on billion-year timescales, and ETAs large enough to remain stable on billion-year timescales are very rare relative to the rest of the ETA population. We find that a twilight ETA survey will not drastically increase the likelihood of individual ETA detection, but it would provide orders of magnitude more observations of select ETA populations. The null detection to date from ZTF restricts the potential ETA population to hundreds of objects larger than 100 m (at H ≈ 22), while a null detection by LSST will further restrict the ETA population to tens of objects larger than 100 m.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Organo‐Functionalized Lacunary Double Cubane‐Type Oxometallates: Synthesis, Structure, and Properties of [(M II Cl) 2 (V IV O) 2 {((HOCH 2 CH 2 )(H)N(CH 2 CH 2 O))(HN(CH 2 CH 2 O) 2 )} 2 ] (M=Co, Zn)

Abstract Organofunctionalized tetranuclear clusters [(M II Cl) 2 (V IV O) 2 {((HOCH 2 CH 2 )(H)N(CH 2 CH 2 O))(HN(CH 2 CH 2 O) 2 )} 2 ] (1, M=Co,2: M=Zn) containing an unprecedented oxometallacyclic {M 2 V 2 Cl 2 N 4 O 8 } (M=Co, Zn) framework have been prepared by solvothermal reactions. The new oxo‐alkoxide compounds were fully characterized by spectroscopic methods, magnetic susceptibility measurement, DFT and ab initio computational methods, and complete single‐crystal X‐ray diffraction structure analysis. The isostructural clusters are formed of edge‐sharing octahedral {VO 5 N} and trigonal bipyramidal {MO 3 NCl} units. Diethanolamine ligates the bimetallic lacunary double cubane core of1and2in an unusual two‐mode fashion, unobserved previously. In the crystalline state, the clusters of1and2are joined by hydrogen bonds to form a three‐dimensional network structure. Magnetic susceptibility data indicate weakly antiferromagnetic interactions between the vanadium centers [J iso (V IV −V IV )=−5.4(1); −3.9(2) cm −1 ], and inequivalent antiferromagnetic interactions between the cobalt and vanadium centers [J iso (V IV −Co II )=−12.6 and −7.5 cm −1 ] contained in1.

Chemistry↗
DOE OSTI2025

Discovery of FoTO1 and Taxol genes enables biosynthesis of baccatin III

Abstract Plants make complex and potent therapeutic molecules 1,2 , but sourcing these molecules from natural producers or through chemical synthesis is difficult, which limits their use in the clinic. A prominent example is the anti-cancer therapeutic paclitaxel (sold under the brand name Taxol), which is derived from yew trees (Taxusspecies) 3 . Identifying the full paclitaxel biosynthetic pathway would enable heterologous production of the drug, but this has yet to be achieved despite half a century of research 4 . WithinTaxus’ large, enzyme-rich genome 5 , we suspected that the paclitaxel pathway would be difficult to resolve using conventional RNA-sequencing and co-expression analyses. Here, to improve the resolution of transcriptional analysis for pathway identification, we developed a strategy we term multiplexed perturbation × single nuclei (mpXsn) to transcriptionally profile cell states spanning tissues, cell types, developmental stages and elicitation conditions. Our data show that paclitaxel biosynthetic genes segregate into distinct expression modules that suggest consecutive subpathways. These modules resolved seven new genes, allowing a de novo 17-gene biosynthesis and isolation of baccatin III, the industrial precursor to Taxol, inNicotiana benthamianaleaves, at levels comparable with the natural abundance inTaxusneedles. Notably, we found that a nuclear transport factor 2 (NTF2)-like protein, FoTO1, is crucial for promoting the formation of the desired product during the first oxidation, resolving a long-standing bottleneck in paclitaxel pathway reconstitution. Together with a new β-phenylalanine-CoA ligase, the eight genes discovered here enable the de novo biosynthesis of 3’-N-debenzoyl-2’-deoxypaclitaxel. More broadly, we establish a generalizable approach to efficiently scale the power of co-expression analysis to match the complexity of large, uncharacterized genomes, facilitating the discovery of high-value gene sets.

Science & Technology - Other Topics↗
DOE OSTI2021

University of Colorado and Black Swift Technologies RPAS-based measurements of the lower atmosphere during LAPSE-RATE

Abstract. Between 14 and 20 July 2018, small remotely piloted aircraft systems (RPASs) were deployed to the San Luis Valley of Colorado (USA) together with a variety of surface-based remote and in situ sensors as well as radiosonde systems as part of the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE). The observations from LAPSE-RATE were aimed at improving our understanding of boundary layer structure, cloud and aerosol properties, and surface–atmosphere exchange and provide detailed information to support model evaluation and improvement work. The current paper describes the observations obtained using four different types of RPASs deployed by the University of Colorado Boulder and Black Swift Technologies. These included the DataHawk2, the Talon and the TTwistor (University of Colorado), and the S1 (Black Swift Technologies). Together, these aircraft collected over 30 h of data throughout the northern half of the San Luis Valley, sampling altitudes between the surface and 914 m a.g.l. Data from these platforms are publicly available through the Zenodo archive and are co-located with other LAPSE-RATE data as part of the Zenodo LAPSE-RATE community (https://zenodo.org/communities/lapse-rate/, last access: 27 May 2021). The primary DOIs for these datasets are https://doi.org/10.5281/zenodo.3891620 (DataHawk2, de Boer et al., 2020a, e), https://doi.org/10.5281/zenodo.4096451 (Talon, de Boer et al., 2020d), https://doi.org/10.5281/zenodo.4110626 (TTwistor, de Boer et al., 2020b), and https://doi.org/10.5281/zenodo.3861831 (S1, Elston and Stachura, 2020).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fast Correction of Errors in the DFT‐Calculated Energies of Gaseous Nitrogen‐Containing Species

Abstract Modeling adsorption phenomena on surfaces by DFT calculations often involves substantial errors, resulting in inaccurate predictions of catalytic activities. Such errors partly stem from the inaccurate description of the energetics of free molecules. Herein, we use a semiempirical group‐additivity method to correct the DFT‐calculated heats of formation of 106 carbon‐ and nitrogen‐containing gaseous compounds belonging to 15 different chemical families. PBE, PW91, RPBE and BEEF‐vdW initially yield mean absolute errors (MAEs) with respect to experiments in the range of 0.32–0.75 eV. After correcting the systematic errors, the overall MAEs decrease to ∼0.05 eV. Additionally, upon applying the corrections to three types of reaction enthalpies, the resulting MAEs are below 0.10 eV. These functional‐group corrections can be used in (electro)catalysis to correct the gas‐phase references necessary to evaluate equilibrium potentials and adsorption energies, predict error cancellation, and assess conflicting experimental data.

Urrego‐Ortiz, Ricardo↗
DOE OSTI2020

ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station

Abstract The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was launched to the International Space Station on 29 June 2018 by the National Aeronautics and Space Administration (NASA). The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as Level‐3 (L3) latent heat flux ( LE ) data products. These data are generated from the Level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, Version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r 2 = 0.88; overall bias = 8%; normalized root‐mean‐square error, RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are overrepresented. The 70‐m‐high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1‐km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Blazar variability power spectra from radio up to TeV photon energies: Mrk 421 and PKS 2155−304

ABSTRACT We present the results of the power spectral density (PSD) analysis for the blazars Mrk 421 and PKS 2155−304, using good-quality, densely sampled light curves at multiple frequencies, covering 17 decades of the electromagnetic spectrum, and variability time-scales from weeks up to a decade. The data were collected from publicly available archives of observatories at radio from Owens Valley Radio Observatory, optical and infrared (B, V, R, I, J, H, and Kbands), X-rays from the Swift and the Rossi X-ray Timing Explorer, high and very high energy (VHE) γ-rays from the Fermi and Very Energetic Radiation Imaging Telescope Array System as well as the High Energy Stereoscopic System. Our results are: (1) the power-law form of the variability power spectra at radio, infrared, and optical frequencies have slopes ∼1.8, indicative of random-walk-type noise processes; (2) the power-law form of the variability power spectra at higher frequencies, from X-rays to VHE γ-rays, however, have slopes ∼1.2, suggesting a flicker noise-type process; and (3) there is significantly more variability power at X-rays, high and VHE γ-rays on time-scales ≲ 100 d, as compared to lower energies. Our results do not easily fit into a simple model, in which a single compact emission zone is dominating the radiative output of the blazars across all the time-scales probed in our analysis. Instead, we argue that the frequency-dependent shape of the variability power spectra points out a more complex picture, with highly inhomogeneous outflow producing non-thermal emission over an extended, stratified volume.

Goyal, Arti↗
DOE OSTI2021

Sparse Bayesian mass mapping with uncertainties: hypothesis testing of structure

ABSTRACT A crucial aspect of mass mapping, via weak lensing, is quantification of the uncertainty introduced during the reconstruction process. Properly accounting for these errors has been largely ignored to date. We present a new method to reconstruct maximum a posteriori (MAP) convergence maps by formulating an unconstrained Bayesian inference problem with Laplace-type l1-norm sparsity-promoting priors, which we solve via convex optimization. Approaching mass mapping in this manner allows us to exploit recent developments in probability concentration theory to infer theoretically conservative uncertainties for our MAP reconstructions, without relying on assumptions of Gaussianity. For the first time, these methods allow us to perform hypothesis testing of structure, from which it is possible to distinguish between physical objects and artefacts of the reconstruction. Here, we present this new formalism, and demonstrate the method on simulations, before applying the developed formalism to two observational data sets of the Abell 520 cluster. Initial reconstructions of the Abell 520 catalogues reported the detection of an anomalous ‘dark core’ – an overdense region with no optical counterpart – which was taken to be evidence for self-interacting dark matter. In our Bayesian framework, it is found that neither Abell 520 data set can conclusively determine the physicality of such dark cores at $99{{\ \rm per\ cent}}$ confidence. However, in both cases the recovered MAP estimators are consistent with both sets of data.

Price, M. A.↗