Search NASA⌕ Search

SEARCH · Search NASA

Results for “DES”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Deep eutectic solvent-based polymer electrolyte for solid-state lithium metal batteries

Poly(ethylene) oxide (PEO)-based electrolytes have been widely studied for solid-state lithium batteries while their ionic conductivity and lithium-ion transference number still need to be further improved. Herein, using the combined experimental and theoretical approach, we demonstrate a novel, solid-state PEO-deep eutectic solvent (DES) electrolyte for the first time. We found that the in situ formation of DES can reduce the crystallinity of PEO matrix and more Li + ions can move freely owing to the weakened coordination between ether oxygens and Li-ions. Besides, we show that more Li + ions can be dissociated from Li salts in PEO-DES electrolyte using the molecular dynamics simulations. Such liquid-free PEO-DES electrolytes showed good ionic conductivity (2.1 × 10 −4 S cm −1 ) which is 160% higher than that of conventional PEO-LiTFSI (8.1 × 10 −5 S cm −1 ) electrolyte at 60 °C. Additionally, the PEO-DES electrolyte showed 136% increase of Li-ion transference number (0.33) compared with ionic liquid-doped PEO-LiTFSI (0.14) at 60 °C. Moreover, the PEO-DES exhibited good compatibility with Li metal and stable Li plating/stripping behavior with little morphology change of Li metal. In conclusion, this research also provides new insights into the enhancement mechanisms of novel polymer electrolytes, improving our fundamental understanding of critical challenges that have impeded the adoption of solid-state lithium metal batteries.

Deep eutectic solvent↗
DOE OSTI2023

A Membrane Contactor Enabling Energy-Efficient CO 2 Capture from Point Sources with Deep Eutectic Solvents

We demonstrate a scalable and energy-efficient hollow fiber membrane contactor (HFMC)-based process using a green solvent for CO 2 capture. This process uses a deep eutectic solvent (DES) in an HFMC to provide close interfacial interactions and contact between the DES and CO 2 . This approach overcomes disadvantages associated with direct absorption in DES and could potentially be applied to a variety of solvent-based CO 2 capture methods. Commercial low-cost polymer hollow fiber membranes (e.g., microporous polypropylene) were evaluated for CO 2 capture with reline, a prototypical DES. Single-gas measurements showed that the DES-based polypropylene HFMC can capture and separate CO 2 while rejecting N2. From a mixed gas containing 50 mol % N 2 and 50 mol % CO ν , the DES-based HFMC separated CO 2 with a purity of 96.9 mol %. The effect of several process parameters including solvent flow rate, pressure, and temperature on the CO 2 separation performance was studied. The flux of the recovered CO 2 was 67.43 mmole/m 2 /h at a feed pressure of 4 bar. In situ Fourier transform infrared (FTIR) measurements combined with density functional theory (DFT)-based molecular dynamics simulations revealed that reline absorbs CO 2 by physical absorption without forming a new chemical compound, and CO 2 separation by reline occurs via the pressure swing mechanism. This research provides fundamental insights about physical solvent-based separation processes and a pathway toward practical deployment.

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

Combination of cluster number counts and two-point correlations: validation on mock Dark Energy Survey

ABSTRACT We present a method of combining cluster abundances and large-scale two-point correlations, namely galaxy clustering, galaxy–cluster cross-correlations, cluster autocorrelations, and cluster lensing. This data vector yields comparable cosmological constraints to traditional analyses that rely on small-scale cluster lensing for mass calibration. We use cosmological survey simulations designed to resemble the Dark Energy Survey Year 1 (DES-Y1) data to validate the analytical covariance matrix and the parameter inferences. The posterior distribution from the analysis of simulations is statistically consistent with the absence of systematic biases detectable at the precision of the DES-Y1 experiment. We compare the χ2 values in simulations to their expectation and find no significant difference. The robustness of our results against a variety of systematic effects is verified using a simulated likelihood analysis of DES-Y1-like data vectors. This work presents the first-ever end-to-end validation of a cluster abundance cosmological analysis on galaxy catalogue level simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Dark energy survey year 1 results: Constraining baryonic physics in the Universe

ABSTRACT Measurements of large-scale structure are interpreted using theoretical predictions for the matter distribution, including potential impacts of baryonic physics. We constrain the feedback strength of baryons jointly with cosmology using weak lensing and galaxy clustering observables (3 × 2pt) of Dark Energy Survey (DES) Year 1 data in combination with external information from baryon acoustic oscillations (BAO) and Planck cosmic microwave background polarization. Our baryon modelling is informed by a set of hydrodynamical simulations that span a variety of baryon scenarios; we span this space via a Principal Component (PC) analysis of the summary statistics extracted from these simulations. We show that at the level of DES Y1 constraining power, one PC is sufficient to describe the variation of baryonic effects in the observables, and the first PC amplitude (Q1) generally reflects the strength of baryon feedback. With the upper limit of Q1 prior being bound by the Illustris feedback scenarios, we reach $\sim 20{{\ \rm per\ cent}}$ improvement in the constraint of $S_8=\sigma _8(\Omega _{\rm m}/0.3)^{0.5}=0.788^{+0.018}_{-0.021}$ compared to the original DES 3 × 2pt analysis. This gain is driven by the inclusion of small-scale cosmic shear information down to 2.5 arcmin, which was excluded in previous DES analyses that did not model baryonic physics. We obtain $S_8=0.781^{+0.014}_{-0.015}$ for the combined DES Y1+Planck EE+BAO analysis with a non-informative Q1 prior. In terms of the baryon constraints, we measure $Q_1=1.14^{+2.20}_{-2.80}$ for DES Y1 only and $Q_1=1.42^{+1.63}_{-1.48}$ for DESY1+Planck EE+BAO, allowing us to exclude one of the most extreme AGN feedback hydrodynamical scenario at more than 2σ.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Utilization of guaiacol-based deep eutectic solvent for achieving a sustainable biorefinery

This study proposed a renewable deep eutectic solvent (DES) pretreatment using lignin-derived guaiacol as the hydrogen bond donor. The DES showed excellent biomass fractionation efficiency after the incorporation of trace AlCl 3 as the reinforcer, which removed 79.1 % lignin while preserving more than 90 % glucan. The pretreated bamboo exhibited 96.2 % glucan enzymatic hydrolysis yield at only 110 °C. The physicochemical properties of the pretreated solids were comprehensively investigated to explain how the DES fractionation overcame the biomass recalcitrance. The regenerated lignin from the DES pretreatment was also analyzed, which revealed that lignin β-O-4 bond was significantly cleaved. In conclusion, this guaiacol-based DES could greatly contribute to establish a closed-loop biorefinery sequence with high lignin fractionation efficiency and great solvent recyclability.

09 BIOMASS FUELS↗
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↗
DOE OSTI2024

The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set

Abstract We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find Ω M = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration ( q 0 < 0 in ΛCDM) with over 5 σ confidence. We find ( Ω M , w ) = ( 0.264 − 0.096 + 0.074 , − 0.80 − 0.16 + 0.14 ) in flat w CDM. For flat w 0 w a CDM, we find ( Ω M , w 0 , w a ) = ( 0.495 − 0.043 + 0.033 , − 0.36 − 0.30 + 0.36 , − 8.8 − 4.5 + 3.7 ) , consistent with a constant equation of state to within ∼2 σ . Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (Ω M , w ) = (0.321 ± 0.007, −0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2 σ . Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Thermodynamic Water Activity Explains the Unusual Electrochemical Stability of Aqueous Deep Eutectic Solvents

The presence of water in nonaqueous deep eutectic solvent (DES) electrolytes has been debated in recent years, with efforts ranging from its complete removal to willful addition. It was shown that controlled amounts of water can be beneficial, as it not only enhances the physicochemical properties of these electrolytes but also has no significant detrimental effects on their electrochemical stability. Despite these advantages, there is still limited understanding of how water interacts with DES systems at the molecular level. This study examines the water activity in ethylene glycol and glycerol, as well as their binary mixtures with choline chloride to form the DESs ethaline and glyceline, respectively. In this work, we show that the high electrochemical stability of glyceline is related to its lower water activity compared to ethaline and can be attributed to the robust H-bonding network formed by the three hydroxyl groups of glycerol. Its 3D H-bond network effectively integrates water molecules within its solvent structure, reducing degradation and maintaining stability at higher water contents. The deviations from the ideal Raoult's law behavior are reflected in the water activity and activity coefficients, which highlight the intricate H-bond interactions within DES-water mixtures. Water acts like a lubricant within the more viscous DES mixtures without being detrimental to their electrochemical performance. The presented results emphasize the necessity of customizing DES-water compositions to enhance their performance as electrolytes, especially in flow battery applications where electrochemical stability, ionic conductivity, and fluidity are of utmost importance.

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

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

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

Pretreatment of Biomass by Selected Type-III Deep Eutectic Solvents and Evaluation of the Pretreatment Effects on Hydrothermal Carbonization

Hydrothermal carbonization (HTC) is a novel thermochemical conversion that converts wet biomass into energy dense solid fuel. Residual moisture under subcritical conditions reacts with a lignin-cellulose-hemicellulose matrix with the major reactions being identified as dehydration and decarboxylation. Among other reaction parameters (e.g., temperature, time, pressure), biomass morphology often plays a key role in HTC. The hypothesis of this study was enhancing the porous structure of biomass without significantly affecting biopolymer composition would augment hydrothermal carbonization (HTC). To prove the hypothesis, two type-III deep eutectic solvents (DESs), namely choline chloride:urea (ChCl:Urea, 1:2 mol/mol) and methyltriphenylphosphonium bromide:ethylene glycol (MTPB:EG, 1:4 mol/mol), were studied to pretreat loblolly pine at room temperature and ambient pressure for 1 h. DES pretreatment shows swelling of the biomass, increasing the surface fiber-to-fiber gap length by 52% and 185% for ChCl:Urea and MPTB:EG pretreatments, respectively. The total pore volume remained intact (2.6 × 10 –3 cm 3 /g), although new small pores evolved, and existing pores were abated with DES pretreatment. Hydrochars prepared from DES pretreated loblolly pine showed a high O/C and H/C ratio resulting in a significant increase of energy content (up to 42%) and a decrease of mass yield (up to 50 wt %), indicating an enhancement of HTC severity due to the alteration of surface morphology by DES. Finally, a preliminary process economics revealed that integrated DES pretreatment-HTC would increase fixed capital investment but decrease the cost of operation and manufacturing than the standalone HTC process.

09 BIOMASS FUELS↗
DOE OSTI2025

Unlocking liquid chemisorption in solid matrices: immobilized deep eutectic solvent-ZIF-8 composites for next-generation CO 2 capture

Achieving net-zero emissions by 2050 requires scalable and durable CO 2 capture solutions, with direct air capture (DAC) gaining increasing prominence. Deep eutectic solvents (DESs) have emerged as promising candidates due to their strong chemisorptive properties, but their liquid-phase nature limits integration into solid systems. In this study, we introduce a solid–liquid hybrid approach by immobilizing [TEAB][TEPA] 2 , a highly CO 2 -reactive DES, onto various supports via non-covalent immobilization. This strategy induces solid-like behavior while retaining molecular-level reactivity of the DES. Among the supports including ZIF-8, zeolite Y, zirconia, and alumina, the [TEAB][TEPA] 2 @ZIF-8-56% composite demonstrated promising CO 2 uptake capacity of 2.00 mmol g −1 adsorbent at 1 bar, 1.48 at 0.15 bar, 0.96 at 0.01 bar, and 0.59 at 0.4 mbar. Particularly, we can observe that the chemisorption capacity under 400 ppm conditions is successfully introduced into ZIF-8, whereas pristine ZIF-8 showed negligible uptake under these same conditions. Breakthrough experiments confirmed robust capture behavior under humid conditions. The composite exhibited excellent cycling stability over 100 capture-release cycles, supported by thermogravimetric analysis. In situ DRIFTS verified that the chemisorptive nature of the DES remained intact after immobilization. Additional characterization studies, including FTIR, PXRD, N 2 physisorption, and XPS depth profiling, revealed partial DES infiltration and strong interfacial interactions. Comparative results using alumina-based composites underscored the importance of microporosity in stability. Finally, these findings suggest that DES-MOF composites hold promise as carbon capture materials, bridging liquid-phase functionality and solid-state robustness.

Yoo, Yunsung [Northwestern Univ., Evanston, IL (Un↗
DOE OSTI2024

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

Context. The determination of accurate photometric redshifts (photo-zs) in large imaging galaxy surveys is key for cosmological studies. One of the most common approaches are machine learning techniques. These methods require a spectroscopic or reference sample to train the algorithms. Attention has to be paid to the quality and properties of these samples since they are key factors in the estimation of reliable photo-zs. Aims. The goal of this work is to calculate the photo-zs for the Y3 DES Deep Fields catalogue using the DNF machine learning algorithm. Moreover, we want to develop techniques to assess the incompleteness of the training sample and metrics to study how incompleteness affects the quality of photometric redshifts. Finally, we are interested in comparing the performance obtained with respect to the EAzY template fitting approach on Y3 DES Deep Fields catalogue. Methods. We have emulated -- at brighter magnitude -- the training incompleteness with a spectroscopic sample whose redshifts are known to have a measurable view of the problem. We have used a principal component analysis to graphically assess incompleteness and to relate it with the performance parameters provided by DNF. Finally, we have applied the results about the incompleteness to the photo-z computation on Y3 DES Deep Fields with DNF and estimated its performance. Results. The photo-zs for the galaxies on DES Deep Fields have been computed with the DNF algorithm and added to the Y3 DES Deep Fields catalogue. They are available at https://des.ncsa.illinois.edu/releases/y3a2/Y3deepfields. Some techniques have been developed to evaluate the performance in the absence of "true" redshift and to assess completeness. We have studied... (Partial abstract)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Dark Energy Survey Identification of A Low-Mass Active Galactic Nucleus at Redshift 0.823 from Optical Variability

We report the identification of a low-mass active galactic nucleus (AGN), DES J0218−0430, in a redshift z = 0.823 galaxy in the Dark Energy Survey (DES) Supernova field. We select DES J0218−0430 as an AGN candidate by characterizing its long-term optical variability alone based on DES optical broad-band light curves spanning over 6 yr. An archival optical spectrum from the fourth phase of the Sloan Digital Sky Survey shows both broad Mg ii and broad H β lines, confirming its nature as a broad-line AGN. Archival XMM–Newton X-ray observations suggest an intrinsic hard X-ray luminosity of |$L_{{\rm 2-12\, keV}}\approx 7.6\pm 0.4\times 10^{43}$| erg s^−1, which exceeds those of the most X-ray luminous starburst galaxies, in support of an AGN driving the optical variability. Based on the broad H β from SDSS spectrum, we estimate a virial black hole (BH) mass of M_• ≈ 10^6.43–10^6.72 M_⊙ (with the error denoting the systematic uncertainty from different calibrations), consistent with the estimation from OzDES, making it the lowest mass AGN with redshift > 0.4 detected in optical. We estimate the host galaxy stellar mass to be M_* ≈ 10^10.5 ± 0.3 M_⊙ based on modelling the multiwavelength spectral energy distribution. DES J0218−0430 extends the M_•–M_* relation observed in luminous AGNs at z ∼ 1 to masses lower than being probed by previous work. Our work demonstrates the feasibility of using optical variability to identify low-mass AGNs at higher redshift in deeper synoptic surveys with direct implications for the upcoming Legacy Survey of Space and Time at Vera C. Rubin Observatory.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

First cosmology results using type Ia supernovae from the Dark Energy Survey: the effect of host galaxy properties on supernova luminosity

ABSTRACT We present improved photometric measurements for the host galaxies of 206 spectroscopically confirmed type Ia supernovae discovered by the Dark Energy Survey Supernova Program (DES-SN) and used in the first DES-SN cosmological analysis. For the DES-SN sample, when considering a 5D (z, x1, c, α, β) bias correction, we find evidence of a Hubble residual ‘mass step’, where SNe Ia in high-mass galaxies (>1010M⊙) are intrinsically more luminous (after correction) than their low-mass counterparts by $\gamma =0.040\pm 0.019$ mag. This value is larger by 0.031 mag than the value found in the first DES-SN cosmological analysis. This difference is due to a combination of updated photometric measurements and improved star formation histories and is not from host-galaxy misidentification. When using a 1D (redshift-only) bias correction the inferred mass step is larger, with $\gamma =0.066\pm 0.020$ mag. The 1D−5D γ difference for DES-SN is $0.026\pm 0.009$ mag. We show that this difference is due to a strong correlation between host galaxy stellar mass and the x1 component of the 5D distance-bias correction. Including an intrinsic correlation between the observed properties of SNe Ia, stretch and colour, and stellar mass in simulated SN Ia samples, we show that a 5D fit recovers γ with −9 mmag bias compared to a +2 mmag bias for a 1D fit. This difference can explain part of the discrepancy seen in the data. Improvements in modelling correlations between galaxy properties and SN is necessary to ensure unbiased precision estimates of the dark energy equation of state as we enter the era of LSST.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Galaxy clustering in harmonic space from the dark energy survey year 1 data: compatibility with real-space results

ABSTRACT We perform an analysis in harmonic space of the Dark Energy Survey Year 1 Data (DES-Y1) galaxy clustering photometric data using products obtained for the real-space analysis. We test our pipeline with a suite of lognormal simulations, which are used to validate scale cuts in harmonic space as well as to provide a covariance matrix that takes into account the DES-Y1 mask. We then apply this pipeline to DES-Y1 data taking into account survey property maps derived for the real-space analysis. We compare with real-space DES-Y1 results obtained from a similar pipeline. We show that the harmonic space analysis we develop yields results that are compatible with the real-space analysis for the bias parameters. This verification paves the way to performing a harmonic space analysis for the upcoming DES-Y3 data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Assessing tension metrics with dark energy survey and Planck data

ABSTRACT Quantifying tensions – inconsistencies amongst measurements of cosmological parameters by different experiments – has emerged as a crucial part of modern cosmological data analysis. Statistically significant tensions between two experiments or cosmological probes may indicate new physics extending beyond the standard cosmological model and need to be promptly identified. We apply several tension estimators proposed in the literature to the dark energy survey (DES) large-scale structure measurement and Planck cosmic microwave background data. We first evaluate the responsiveness of these metrics to an input tension artificially introduced between the two, using synthetic DES data. We then apply the metrics to the comparison of Planck and actual DES Year 1 data. We find that the parameter differences, Eigentension, and Suspiciousness metrics all yield similar results on both simulated and real data, while the Bayes ratio is inconsistent with the rest due to its dependence on the prior volume. Using these metrics, we calculate the tension between DES Year 1 3 × 2pt and Planck, finding the surveys to be in ∼2.3σ tension under the ΛCDM paradigm. This suite of metrics provides a toolset for robustly testing tensions in the DES Year 3 data and beyond.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Dark Energy Survey Year 3 Results: Deep Field optical + near-infrared images and catalogue

ABSTRACT We describe the Dark Energy Survey (DES) Deep Fields, a set of images and associated multiwavelength catalogue (ugrizJHKs) built from Dark Energy Camera (DECam) and Visible and Infrared Survey Telescope for Astronomy (VISTA) data. The DES Deep Fields comprise 11 fields (10 DES supernova fields plus COSMOS), with a total area of ∼30 sq. deg. in ugriz bands and reaching a maximum i-band depth of 26.75 (AB, 10σ, 2 arcsec). We present a catalogue for the DES 3-yr cosmology analysis of those four fields with full 8-band coverage, totalling 5.88 sq. deg. after masking. Numbering 2.8 million objects (1.6 million post-masking), our catalogue is drawn from images coadded to consistent depths of r = 25.7, i = 25, and z = 24.3 mag. We use a new model-fitting code, built upon established methods, to deblend sources and ensure consistent colours across the u-band to Ks-band wavelength range. We further detail the tight control we maintain over the point-spread function modelling required for the model fitting, astrometry and consistency of photometry between the four fields. The catalogue allows us to perform a careful star–galaxy separation and produces excellent photometric redshift performance (NMAD = 0.023 at i < 23). The Deep-Fields catalogue will be made available as part of the cosmology data products release, following the completion of the DES 3-yr weak lensing and galaxy clustering cosmology work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Consistency of cosmic shear analyses in harmonic and real space

ABSTRACT Recent cosmic shear studies have reported discrepancies of up to 1σ on the parameter ${S_{8}=\sigma _{8}\sqrt{{\Omega _{\rm m}}/0.3}}$ between the analysis of shear power spectra and two-point correlation functions, derived from the same shear catalogues. It is not a priori clear whether the measured discrepancies are consistent with statistical fluctuations. In this paper, we investigate this issue in the context of the forthcoming analyses from the third year data of the Dark Energy Survey (DES Y3). We analyse DES Y3 mock catalogues from Gaussian simulations with a fast and accurate importance sampling pipeline. We show that the methodology for determining matching scale cuts in harmonic and real space is the key factor that contributes to the scatter between constraints derived from the two statistics. We compare the published scales cuts of the KiDS, Subaru-HSC, and DES surveys, and find that the correlation coefficients of posterior means range from over 80 per cent for our proposed cuts, down to 10 per cent for cuts used in the literature. We then study the interaction between scale cuts and systematic uncertainties arising from multiple sources: non-linear power spectrum, baryonic feedback, intrinsic alignments, uncertainties in the point spread function, and redshift distributions. We find that, given DES Y3 characteristics and proposed cuts, these uncertainties affect the two statistics similarly; the differential biases are below a third of the statistical uncertainty, with the largest biases arising from intrinsic alignment and baryonic feedback. While this work is aimed at DES Y3, the tools developed can be applied to Stage-IV surveys where statistical errors will be much smaller.

79 ASTRONOMY AND ASTROPHYSICS↗