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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Protocol to detect dilution cycles in chemostat experiments and estimate growth rate slopes with linear modeling with R software chemostat_regression

Chemostat growth chambers measure optical density over time and require manual calculation of growth rates. Here, we present chemostat_regression, R software that enables users to automatically identify chemostat cycles and estimate growth rate using a linear regression approach. We describe steps for creating requisite software environment(s), formatting input data, executing the software via command line/RStudio/R-Shiny, interpreting results, assessing the validity of results, and modifying input parameters.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Sustainable production of 5-hydroxymethyl furfural from glucose for process integration with high fructose corn syrup infrastructure

5-Hydroxymethyl furfural (HMF) is a platform chemical, which can be derived from lignocellulosic biomass, and used for production of liquid fuels and polymers. We demonstrate a process for production of HMF using sequential enzymatic and catalytic reactions of glucose to synthesize HMF, and simulated-moving-bed (SMB) separation to purify HMF. The adsorption thermodynamic parameters of glucose, fructose, and HMF on a commercial chromatography resin are experimentally determined for modeling the SMB-based HMF production process. The experimental data are used to develop a rigorous process model and then estimate the cost of production. Chromatographic separation of HMF has 16% lower operating costs compared to an extraction-based process and has a minimum selling price of approximately $1478 per ton. We demonstrate that the HMF process can be integrated with the high fructose corn syrup (HFCS) process, and we performed analyses considering two systems including construction of a new integrated facility and retrofitting an existing HFCS facility to produce HMF. Our analyses suggest that the latter approach is a promising short-term low-risk strategy to advance the HMF production technology to commercial scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Atmospheric parameters, spectral indexes and their relation to CPV spectral performance

Air Mass and atmosphere components (basically aerosol (AOD) and precipitable water (PW)) define the absorption of the sunlight that arrive to Earth. Radiative models such as SMARTS or MODTRAN use these parameters to generate an equivalent spectrum. However, complex and expensive instruments (as AERONET network devices) are needed to obtain AOD and PW. On the other hand, the use of isotype cells is a convenient way to characterize spectrally a place for CPV considering that they provide the photocurrent of the different internal subcells individually. Crossing data from AERONET station and a Tri-band Spectroheliometer, a model that correlates Spectral Mismatch Ratios and atmospheric parameters is proposed. Considering the amount of stations of AERONET network, this model may be used to estimate the spectral influence on energy performance of CPV systems close to all the stations worldwide.

14 SOLAR ENERGY↗
DOE OSTI2015

Detecting primordial B-modes after Planck

We update the forecasts for the measurement of the tensor-to-scalar ratio r for various ground-based experiments (AdvACT, CLASS, Keck/BICEP3, Simons Array, SPT-3G), balloons (EBEX 10k and Spider) and satellites (CMBPol, COrE and LiteBIRD), taking into account the recent Planck data on polarized dust and using a component separation method. The forecasts do not change significantly with respect to previous estimates when at least three frequencies are available, provided foregrounds can be accurately described by few parameters. We argue that a theoretically motivated goal for future experiments is r∼2×10{sup −3}, and that this is achievable if the noise is reduced to ∼1 μK-arcmin and lensing is reduced to 10% in power. We study the constraints experiments will be able to put on the frequency and ℓ-dependence of the tensor signal as a check of its primordial origin. Futuristic ground-based and balloon experiments can have good constraints on these parameters, even for r∼2×10{sup −3}. For the same value of r, satellites will marginally be able to detect the presence of the recombination bump, the most distinctive feature of the primordial signal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

WE-AB-207B-09: Margin Reduction for Planning Target Volume (PTV) in Patients with Localized Prostate Cancer: Impact On Delivered Dose and Quality of Life

Purpose: To estimate the delivered (cumulative) dose to targets and organs at risk for localized prostate cancer patients treated with reduced PTV margins and to evaluate preliminary patient reported quality-of-life (QOL). Methods: Under an IRB-approved protocol, 20 prostate cancer patients (including 11 control patients) were treated with reduced planning margins (5 mm uniform with 4 mm at prostate/rectum interface). Control patients had standard margin (10/6 mm)-based treatments. A parameter-optimized Elastix algorithm along with energy-mass mapping was used to deform and resample dose of the day onto the planning CT for each fraction to estimate the delivered dose over all fractions. QOL data were collected via Expanded Prostate cancer Index Composite (EPIC-26) questionnaires at time points pre-treatment, post-treatment, and at 2, 6, 12, 18 month follow-ups. Standardized QOL scores [range: 0–100] were determined and baseline-corrected by subtracting pre-treatment QOL data. Mean QOL differences between the margin reduced group and control group (QOLmr-QOLcontrol) were calculated for first 18 months. Results: The difference between the cumulative mean dose (Dmean) and the planned mean dose (±SD) for PTV, prostate, bladder, and rectum were −2.2±1.0, 0.3±0.5, −0.7±2.6, and −2.1±1.3 Gy respectively for the margin-reduced group, and −0.8±2.0, 0.9±1.4, - 0.7±3.1 and −1.0±2.4 Gy for the control group. Difference between the two groups was statistically insignificant (p=0.1). Standardized and baseline corrected QOLmr-QOLcontrol for EPIC domains categorized as “Urinary Incontinence”, “Urinary Irritative/Obstructive”, “Bowel”, “Sexual”, and “Hormonal” were 0.6, 12.1, 9.1, 13.3, and −0.9 for the 18 months following radiation therapy (higher values better). Delivered dose to rectum showed a weak correlation to “Bowel” domain (Pearson’s coefficient −0.24, p<0.001), while bladder dose did not correlate to Urinary Incontinence/Irritative/Obstructive QOL domains. Conclusion: The margin-reduced group exhibited clinically meaningful improvement of QOL without compromising the PTV dose. A larger number of patients and greater follow-up is needed to draw unequivocal conclusions. This work was supported in part by a research grant from Varian Medical Systems, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Astrophysical S -factors, Thermonuclear Rates, and Electron Screening Potential for the 3 He(d,p) 4 He Big Bang Reaction via a Hierarchical Bayesian Model

Here, we developed a hierarchical Bayesian framework to estimate S-factors and thermonuclear rates for the 3 He(d,p) 4 He reaction, which impacts the primordial abundances of 3 He and 7 Li. The available data are evaluated and all direct measurements are taken into account in our analysis for which we can estimate separate uncertainties for systematic and statistical effects. For the nuclear reaction model, we adopt a single-level, two-channel approximation of R-matrix theory, suitably modified to take the effects of electron screening at lower energies into account. In addition to the usual resonance parameters (resonance location and reduced widths for the incoming and outgoing reaction channel), we include the channel radii and boundary condition parameters in the fitting process. Our new analysis of the 3 He(d,p) 4 He S-factor data results in improved estimates for the thermonuclear rates. This work represents the first nuclear rate evaluation using R-matrix theory embedded into a hierarchical Bayesian framework, properly accounting for all known sources of uncertainty. Therefore, it provides a test bed for future studies of more complex reactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models

Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Adjustment of Non-linear Interaction Parameters for Relativistic Mean Field Approach by Using Artificial Neural Networks

The relativistic mean field (RMF) model with a small number of adjusted parameters has been used successfully in the last thirty years for predictions of various ground-state nuclear properties of nuclei. In this model, Dirac and Klein–Gordon like equations obtained from application of variation principle on phenomenological Lagrangian density are solved iteratively for calculations of nuclear properties of nuclei. For this purpose, parameters such as masses of considered mesons, nucleon–meson coupling constants, and self-couplings of mesons are needed and they are fitted from experimental data. Some parameter sets for RMF model introduced to correct predictions of nuclear properties of nuclei cover nuclidic chart. Besides Artificial Neural Network (ANN) method is used successfully in many field of science as in nuclear physics. ANN is known as a very powerful tool that are used when standard techniques fail to estimate the correlation between the variables. In the present study, ANN method has been employed to check its understanding capability of relations between RMF model parameters and their predictions on the ground-state binding energies of some spherical nuclei. Understanding capability of ANN method for these relations of considered nuclei has been found well. Based on this success, new non-linear parameter set for RMF model called DEFNE by us have been produced by using ANN method. Furthermore, predictions of RMF model with DEFNE parameter set for ground-state binding energies and charge radii of nuclei cover nuclidic chart have been found as in agreement with the available experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Thermodynamic description of molten salt systems: KCl-LiCl-NaCl and KCl-LiCl-NdCl 3

The mixture of KCl and LiCl has been used as electrolyte in the electrorefining process to recover uranium from used nuclear fuels due to the low melting point. However, lanthanides and sodium in the reactor waste continuously dissolve into it and thus alter its thermodynamic properties. To understand the thermodynamic behavior evolution of the electrolyte with the accumulation of impurities, thermodynamic modeling for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and four constituent binary systems in the entire composition space was performed using the CALPHAD (CALculation of PHAse Diagrams) approach. The ionic liquid was described by the two-sublattice model, where neutral species were introduced to consider short-range ordering (SRO) within the melt, whereas the solid solution was modeled based on the Compound Energy Formalism. Literature data on phase equilibria and thermochemical properties were critically evaluated and used during the optimization of thermodynamic parameters for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and their subsystems. The calculated phase diagrams and mixing enthalpies are in good agreement with the experimental data. The thermodynamic modeling for the KCl-LiCl-NdCl 3 system was carried out for the first time. To fill the gap in experimental measurement, enthalpy of mixing for the KCl-LiCl-NdCl 3 melt was estimated using the surrounded-ion model. These data then served as critical inputs for thermodynamic optimization. Furthermore, the present study can provide insights into thermodynamic property evolution of the electrolyte and solubility limit of various impurities during the electrorefining process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Data Assimilative Optimization of WSA Source Surface and Interface Radii using Particle Filtering

TheWang-Sheeley-Arge (WSA) model estimates solar wind speed and interplanetary magnetic field polarity in the inner heliosphere using global photospheric magnetic field maps. WSA employs the Potential Field Source Surface (PFSS) and Schatten Current Sheet (SCS) models to determine the Sun's global coronal magnetic field. The PFSS and SCS models are connected through two radial parameters, the source surface and interface radii, which specify the overlap region between the inner SCS and outer PFSS models. Though both radii values are adjustable, they have typically been fixed to 2.5 solar radii. Our work highlights how solar wind predictions improve when the radii are allowed to vary over time. Data assimilation using particle filtering (sequential Monte Carlo) is used to infer optimal values over a fixed time window. Solar wind model predictions and satellite observations are compared with a newly developed quality-of-agreement prediction metric. The agreement metric between the model and observations is assumed to correspond to the probability of the two key WSA model parameters, the source surface and interface radii, where the highest metric value implies the optimal radii.We find that the optimal particle filter values of solar radii can perform twice as well as standard values for an exploratory period during Carrington Rotation 1901, with these values also reducing nonphysical kinking effects seen in solar magnetic field lines. Data assimilation choices of input realization and time frame have implications for variation in the solar wind over time. We present this work's theoretical context and practical applications for prediction accuracy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V D,ss ) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V D,ss , accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V D,ss directly and to predict input parameters required for mechanistic and empirical V D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V D,ss predictions was also investigated. In predicting V D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V D,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V D,ss but was limited in estimating the compounds with low V D,ss .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

CosmoSIS: A system for MC parameter estimation

CosmoSIS is a modular system for cosmological parameter estimation, based on Markov Chain Monte Carlo and related techniques. It provides a series of samplers, which drive the exploration of the parameter space, and a series of modules, which calculate the likelihood of the observed data for a given physical model, determined by the location of a sample in the parameter space. While CosmoSIS ships with a set of modules that calculate quantities of interest to cosmologists, there is nothing about the framework itself, nor in the Markov Chain Monte Carlo technique, that is specific to cosmology. Thus CosmoSIS could be used for parameter estimation problems in other fields, including HEP. This paper describes the features of CosmoSIS and show an example of its use outside of cosmology. Furthermore, it also discusses how collaborative development strategies differ between two different communities: that of HEP physicists, accustomed to working in large collaborations, and that of cosmologists, who have traditionally not worked in large groups.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The Palomar Transient Factory and RR Lyrae: The metallicity–light curve relation based on AB-type RR Lyrae in the Kepler field

The wide-field synoptic sky surveys, known as the Palomar Transient Factory (PTF) and the intermediate Palomar Transient Factory (iPTF), will accumulate a large number of known and new RR Lyrae. These RR Lyrae are good tracers to study the substructure of the Galactic halo if their distance, metallicity, and galactocentric velocity can be measured. Candidates of halo RR Lyrae can be identified from their distance and metallicity before requesting spectroscopic observations for confirmation. This is because both quantities can be obtained via their photometric light curves, because the absolute V-band magnitude for RR Lyrae is correlated with metallicity, and the metallicity can be estimated using a metallicity–light curve relation. To fully utilize the PTF and iPTF light-curve data in related future work, it is necessary to derive the metallicity–light curve relation in the native PTF/iPTF R-band photometric system. In this work, we derived such a relation using the known ab-type RR Lyrae located in the Kepler field, and it is found to be [Fe/H]{sub PTF}=−4.089--7.346P+1.280ϕ{sub 31} (where P is pulsational period and ϕ{sub 31} is one of the Fourier parameters describing the shape of the light curve), with a dispersion of 0.118 dex. We tested our metallicity–light curve relation with new spectroscopic observations of a few RR Lyrae in the Kepler field, as well as several data sets available in the literature. Our tests demonstrated that the derived metallicity–light curve relation could be used to estimate metallicities for the majority of the RR Lyrae, which are in agreement with the published values.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A COMPARATIVE ANALYSIS OF THE SUPERNOVA LEGACY SURVEY SAMPLE WITH ΛCDM AND THE R{sub h}=ct UNIVERSE

The use of Type Ia supernovae (SNe Ia) has thus far produced the most reliable measurement of the expansion history of the universe, suggesting that ΛCDM offers the best explanation for the redshift–luminosity distribution observed in these events. However, analysis of other kinds of sources, such as cosmic chronometers, gamma-ray bursts, and high-z quasars, conflicts with this conclusion, indicating instead that the constant expansion rate implied by the R{sub h} = ct universe is a better fit to the data. The central difficulty with the use of SNe Ia as standard candles is that one must optimize three or four nuisance parameters characterizing supernova (SN) luminosities simultaneously with the parameters of an expansion model. Hence, in comparing competing models, one must reduce the data independently for each. We carry out such a comparison of ΛCDM and the R{sub h} = ct universe using the SN Legacy Survey sample of 252 SN events, and show that each model fits its individually reduced data very well. However, since R{sub h} = ct has only one free parameter (the Hubble constant), it follows from a standard model selection technique that it is to be preferred over ΛCDM, the minimalist version of which has three (the Hubble constant, the scaled matter density, and either the spatial curvature constant or the dark energy equation-of-state parameter). We estimate using the Bayes Information Criterion that in a pairwise comparison, the likelihood of R{sub h} = ct is ∼90%, compared with only ∼10% for a minimalist form of ΛCDM, in which dark energy is simply a cosmological constant. Compared to R{sub h} = ct, versions of the standard model with more elaborate parametrizations of dark energy are judged to be even less likely.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

On the estimation of boundary layer heights: a machine learning approach

Abstract. The planetary boundary layer height (zi) is a key parameter used in atmospheric models for estimating the exchange of heat, momentum, and moisture between the surface and the free troposphere. Near-surface atmospheric and subsurface properties (such as soil temperature, relative humidity, etc.) are known to have an impact on zi. Nevertheless, precise relationships between these surface properties and zi are less well known and not easily discernible from the multi-year dataset. Machine learning approaches, such as random forest (RF), which use a multi-regression framework, help to decipher some of the physical processes linking surface-based characteristics to zi. In this study, a 4-year dataset from 2016 to 2019 at the Southern Great Plains site is used to develop and test a machine learning framework for estimating zi. Parameters derived from Doppler lidars are used in combination with over 20 different surface meteorological measurements as inputs to a RF model. The model is trained using radiosonde-derived zi values spanning the period from 2016 through 2018 and then evaluated using data from 2019. Results from 2019 showed significantly better agreement with the radiosonde compared to estimates derived from a thresholding technique using Doppler lidars only. Noteworthy improvements in daytime zi estimates were observed using the RF model, with a 50 % improvement in mean absolute error and an R2 of greater than 85 % compared to the Tucker method zi. We also explore the effect of zi uncertainty on convective velocity scaling and present preliminary comparisons between the RF model and zi estimates derived from atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

WE-EF-BRA-04: Evaluation of Dosimetric Uncertainties in Individualized Targeted Radionuclide Therapy (TRT) Treatment Planning Using Pre-Clinical Data

Purpose: Dosimetry for targeted radionuclide therapy (TRT) is moving away from conventional model-based methods towards patient-specific approaches. To address this need, a Monte Carlo (MC) dosimetry platform was developed to estimate patient-specific therapeutic 3D dose distributions based on pre-treatment imaging. However, because a standard practice for patient-specific internal dosimetry has not yet been established, there are many sources of dosimetric uncertainties. The goal of this work was to quantify the sensitivity of various parameters on MC dose estimations. Methods: The ‘diapeutic’ agent, CLR1404, was used as a proof-of-principle compound in this work. CLR1404 can be radiolabeled with either {sup 124}I for PET imaging or {sup 131}I for radiotherapy or SPECT imaging. PET/CT images of 5 mice were acquired out to 240 hrs post-injection of {sup 124}I-CLR1404. The therapeutic {sup 131}I-CLR1404 absorbed dose (AD) distribution was calculated using a Geant4-based MC dosimetry platform. A series of sensitivity studies were performed. The variables that were investigated included the PET/CT voxel resolution, partial volume corrections (PVC), material segmentation, inter-observer contouring variability, and the pre-treatment image acquisition frequency. Results: Resampling the PET/CT voxel size between 0.2–0.8 mm resulted in up to a 13% variation in the mean AD. Application of the PVC increased the mean AD by 0.5–11.2%. Less than 1% differences in ROI mean AD were observed between the tissue segmentation schemes using 4 and 27 different material compositions. Inter-observer contouring variability led to up to a 20% CoV (stdev/mean) in the mean AD between the users. Varying the number and frequency of pre-treatment images used resulted in changes in mean AD up to 176% compared to the case using all 12 images. Conclusion: Voxel resolution, contour segmentation, the image acquisition protocol most significantly impacted patient-specific TRT dosimetry. Further work is needed to develop a standard protocol that optimizes accuracy and efficiency for patient-specific internal dosimetry. BT and JG are affiliated with Cellectar Biosciences which owns the licensing rights to CLR1404 and related compounds.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Novel Physiologically Based Model of Creatinine Renal Disposition to Integrate Current Knowledge of Systems Parameters and Clinical Observations

Creatinine is the most common clinical biomarker of renal function. As a substrate for renal transporters, its secretion is susceptible to inhibition by drugs, resulting in transient increase in serum creatinine and false impression of damage to kidney. Novel physiologically based models for creatinine were developed here and (dis)qualified in a stepwise manner until consistency with clinical data. Data from a matrix of studies were integrated, including systems data (common to all models), proteomics‐informed in vitro – in vivo extrapolation of all relevant transporter clearances, exogenous administration of creatinine (to estimate endogenous synthesis rate), and inhibition of different renal transporters (11 perpetrator drugs considered for qualification during creatinine model development and verification on independent data sets). The proteomics‐informed bottom‐up approach resulted in the underprediction of creatinine renal secretion. Subsequently, creatinine‐trimethoprim clinical data were used to inform key model parameters in a reverse translation manner, highlighting best practices and challenges for middle‐out optimization of mechanistic models.

Scotcher, Daniel↗
DOE OSTI2022

Dark Energy Survey Year 3 results: calibration of lens sample redshift distributions using clustering redshifts with BOSS/eBOSS

ABSTRACT We present clustering redshift measurements for Dark Energy Survey (DES) lens sample galaxies used in weak gravitational lensing and galaxy clustering studies. To perform these measurements, we cross-correlate with spectroscopic galaxies from the Baryon Acoustic Oscillation Survey (BOSS) and its extension, eBOSS. We validate our methodology in simulations, including a new technique to calibrate systematic errors that result from the galaxy clustering bias, and we find that our method is generally unbiased in calibrating the mean redshift. We apply our method to the data, and estimate the redshift distribution for 11 different photometrically selected bins. We find general agreement between clustering redshift and photometric redshift estimates, with differences on the inferred mean redshift found to be below |Δz| = 0.01 in most of the bins. We also test a method to calibrate a width parameter for redshift distributions, which we found necessary to use for some of our samples. Our typical uncertainties on the mean redshift ranged from 0.003 to 0.008, while our uncertainties on the width ranged from 4 to 9 per cent. We discuss how these results calibrate the photometric redshift distributions used in companion papers for DES Year 3 results.

79 ASTRONOMY AND ASTROPHYSICS↗