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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Remote sensing and geophysical characterization of a floodplain-hillslope system in the East River watershed, Colorado

This package is part of the Watershed Function SFA Data Collection and contains remote sensing data and geophysical measurements acquired at the Pumphouse hillslope-floodplain site, in the East River, Colorado. The remote sensing dataset includes: a) a WorldView-2 (WV-2) pan-sharpened multi-spectral remote sensing image, and b) LiDAR derived products. The WV-2 image was acquired on September 24th, 2015, and it is composed of eight spectral bands (coastal, blue, green, yellow, red, red edge, NIR1, and NIR2) with a spatial resolution of 0.5 m, covering an area of 750x750 m. The LiDAR products are a digital surface model (DSM), representing the top-of-canopy elevation, and a digital terrain model (DTM), representing the bare-ground elevation, at the spatial resolution of 0.5 m. Such data are extracted from the LiDAR point-cloud dataset acquired over the entire watershed on August 2015 (http://dx.doi.org/10.21952/WTR/1412542). The geophysical dataset is composed of the electrical resistivity tomography (ERT) data acquired along a 158.75 m long transect, spanning the hillslope topographical gradient with 1.25 m electrode spacing. The dataset also includes soil water content measured with a TDR system. The package provides also polygons of different vegetation communities, which were used as reference data for mapping plant community distributions. The spatial polygons are based on plant locations acquired during the field campaign of June-July 2017 using an RTK DGPS system. The reference dataset is composed by 11 vegetation classes, namely, deciduous forest, evergreen forest, riparian shrubland, sagebrush, shrubland, potentilla, frasera, lupine meadow, veratrum, bunchgrass meadow, and forb, as well as four non-vegetation classes, such as water-river, water-lake, man-made, bare area, and shadow. The vegetation classes are determined based on distinct spectral and/or structural signatures of dominant species. The package includes products derived by these datasets, such as: a) a plant height map, computed by subtracting the DTM from the DSM, and b) a plant community distribution map, obtained by using a data-fusion machine-learning-based framework that exploit the WV-2 image and plant height information.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Probing nucleon's spin structures with polarized Drell-Yan in the Fermilab SpinQuest experiment

Although the proton was discovered about 100 years ago, its spin structure still remains a mystery. Recent studies suggest that the orbital angular momentum of sea quarks could significantly contribute to the proton's spin. The SeaQuest experiment, which recently completed data collection, probed the unpolarized light quark sea distributions of the proton using the Drell-Yan process. Its successor, the SpinQuest (E1039), will access the $\bar{u}$ and $\bar{d}$ Sivers functions using polarized NH$_3$ and ND$_3$ targets. A non-zero Sivers asymmetry, observed in SpinQuest, would be a strong indication of non-zero sea-quark orbital angular momentum. The SpinQuest experiment can also probe the sea quark's transversity distribution, which is relevant for the determination of proton's tensor charge. Recent study suggests that sea-quarks might contribute significantly to deuteron's tensor polarized structure functions. This can be further probed in SpinQuest using tensor polarized ND$_3$ target. The current status and future plan of the experiment are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Ab initio calculation of the potential bubble nucleus Si 34

Background: The possibility that an unconventional depletion (referred to as a “bubble”) happens in the middle of the charge density distribution of certain nuclei due to a purely quantum mechanical effect has attracted theoretical and experimental attention in recent years. Based on a mean-field rationale, a correlation between the occurrence of such a semibubble and an anomalously weak splitting between low angular-momentum spin-orbit partners has been further conjectured. Energy density functional and valence-space shell model calculations have been performed to identify and characterize the best candidates, among which Si 34 appears as a particularly interesting case. While the experimental determination of the charge density distribution of the unstable Si 34 is currently out of reach, ($d, p$) experiments on this nucleus have been performed recently to test the correlation between the presence of a bubble and an anomalously weak 1/2 – –3/2 – splitting in the spectrum of Si 35 as compared to S 37 . Purpose: We study the potential bubble structure of Si 34 on the basis of the state-of-the-art ab initio self-consistent Green's function many-body method. Methods: We perform the first ab initio calculations of Si 34 and S 36 . In addition to binding energies, the first observables of interest are the charge density distribution and the charge root-mean-square radius for which experimental data exist in S 36 . The next observable of interest is the low-lying spectroscopy of Si 35 and S 37 obtained from ($d, p$) experiments along with the spectroscopy of Al 33 and P 35 obtained from knock-out experiments. The interpretation in terms of the evolution of the underlying shell structure is also provided. The study is repeated using several chiral effective field theory Hamiltonians as a way to test the robustness of the results with respect to input internucleon interactions. The convergence of the results with respect to the truncation of the many-body expansion, i.e., with respect to the many-body correlations included in the calculation, is studied in detail. We eventually compare our predictions to state-of-the-art multireference energy density functional and shell model calculations. Results: The prediction regarding the (non)existence of the bubble structure in Si 34 varies significantly with the nuclear Hamiltonian used. Yet, demanding that the experimental charge density distribution and the root-mean-square radius of S 36 be well reproduced, along with Si 34 and S 36 binding energies, only leaves the NNLO sat Hamiltonian as a serious candidate to perform this prediction. In this context, a bubble structure, whose fingerprint should be visible in an electron scattering experiment of Si 34 , is predicted. Furthermore, a clear correlation is established between the occurrence of the bubble structure and the weakening of the 1/2 – –3/2 – splitting in the spectrum of Si 35 as compared to S 37 . Conclusions: The occurrence of a bubble structure in the charge distribution of Si 34 is convincingly established on the basis of state-of-the-art ab initio calculations. This prediction will have to be reexamined in the future when improved chiral nuclear Hamiltonians are constructed. On the experimental side, present results act as a strong motivation to measure the charge density distribution of Si 34 in future electron scattering experiments on unstable nuclei. In the meantime, it is of interest to perform one-neutron removal on Si 34 and S 36 in order to further test our theoretical spectral strength distributions over a wide energy range.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Planck Lensing and Cosmic Infrared Background Cross-correlation with Fermi-LAT: Tracing Dark Matter Signals in the Gamma-Ray Background

The extragalactic γ-ray background and its spatial anisotropy could potentially contain a signature of dark matter (DM) annihilation or particle decay. Astrophysical foregrounds, such as blazars and star-forming galaxies (SFGs), however, dominate the γ-ray background, precluding an easy detection of the signal associated with the DM annihilation or decay in the background intensity spectrum. The DM imprint on the γ-ray background is expected to be correlated with large-scale structure tracers. In some cases, such a cross-correlation is even expected to have a higher signal-to-noise ratio than the auto-correlation. One reliable tracer of the DM distribution in the large-scale structure is lensing of the cosmic microwave background (CMB), and the cosmic infrared background (CIB) is a reliable tracer of SFGs. We analyze Fermi-LAT data taken over 92 months and study the cross-correlation with Planck CMB lensing, Planck CIB, and Fermi-γ maps. We put upper limits on the DM annihilation cross-section from the cross-power spectra with the γ-ray background anisotropies. The unbiased power spectrum estimation is validated with simulations that include cross-correlated signals. We also provide a set of systematic tests and show that no significant contaminations are found for the measurements presented here. Using γ-ray background map from data gathered over 92 months, we find the best constraint on the DM annihilation with a 1σ confidence level upper limit of 10{sup −25}–10{sup −24} cm{sup 3} s{sup −1}, when the mass of DM particles is between 20 and 100 GeV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Use of X-Ray Tomography to Elucidate Cold Cap Structure in Waste Glass Melter

In a waste glass melter, the cold cap plays a vital role in establishing the melt rate for a particular melter feed. Data from x-ray tomography of laboratory-scale cold caps provide insight into the topology of bubble distribution within the cold cap for various feed compositions at temperatures from 600°C to 1040°C. The images were thresholded to capture features of interest, then processed by software to obtain statistics that can be used to characterize bubble topology. Statistics obtained include total pellet volume, porosity, bubble size distribution and morphology as a function of temperature. These statistics will be used to compare the melting behavior of the different glass formulations as a function of temperature. Information on bubble topology at various temperatures will be used to synthetically generate a cold cap structure that can be used to model heat transfer through the cold cap layer.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of each tissue type - white matter, grey matter and cerebral spinal fluid inside each image voxel. (4) The distributions of the three tissue types or tissue mixture across the structural image array are down-sampled and mapped onto the ASL voxel array via a co-registration operation. (5) The presented 4-dimensional expectation-maximization (4D-EM) algorithm takes the down-sampled three tissue type distributions on perfusion image data to generate the perfusion mean, variance and percentage images for each tissue type of interest. Results: Experimental results on three volunteer datasets demonstrated that the multi-spectral tissue-mixture segmentation algorithm was effective to initialize tissue mixtures from T1- and T2-weighted MR images. Compared with the conventional ASL image processing toolbox, the proposed 4D-EM algorithm not only generated comparable perfusion mean images, but also produced perfusion variance and percentage images, which the ASL toolbox cannot obtain. It is observed that the perfusion contribution percentages may not be the same as the corresponding tissue mixture volume fractions estimated in the structural images. Conclusion: A specific application to brain ASL images showed that the presented perfusion image analysis method is promising for detecting subtle changes in tissue perfusions, which is valuable for the early diagnosis of certain brain diseases, e.g. multiple sclerosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Search for Structure in the B s 0 π ± Invariant Mass Spectrum

The B 0 s π ± invariant mass distribution is investigated in order to search for possible exotic meson states. The analysis is based on a data sample recorded with the LHCb detector corresponding to 3 fb –1 of pp collision data at √s = 7 and 8 TeV. No significant excess is found, and upper limits are set on the production rate of the claimed X(5568) state within the LHCb acceptance. Upper limits are also set as a function of the mass and width of a possible exotic meson decaying to the B 0 s π ± final state. The same limits also apply to a possible exotic meson decaying through the chain B* 0 s π ± , B* 0 s →B 0 s γ where the photon is excluded from the reconstructed decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions

This paper presents an integrated technical framework to protect pipelines against both malicious intrusions and piping degradation using a distributed fiber sensing technology and artificial intelligence. A distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (φ-OTDR) was used to detect acoustic wave propagation and scattering along pipeline structures consisting of straight piping and sharp bend elbow. Signal to noise ratio of the DAS system was enhanced by femtosecond induced artificial Rayleigh scattering centers. Data harnessed by the DAS system were analyzed by neural network-based machine learning algorithms. The system identified with over 85% accuracy in various external impact events, and over 94% accuracy for defect identification through supervised learning and 71% accuracy through unsupervised learning.

Peng, Zhaoqiang↗
DOE OSTI2017

Measurement of internal structure of jets in Pb+Pb collisions at s NN = 2.76 TeV with the ATLAS detector at the LHC

CERN-LHC. The distributions of transverse momentum and longitudinal momentum fraction of charged particles in jets are measured in Pb+Pb and pp collisions with the ATLAS detector at the LHC. The distributions are measured as a function of jet transverse momentum and rapidity. The analysis utilises an integrated luminosity of 0.14 nb^{-1} of Pb+Pb data and 4.0 pb^{-1} of pp data collected in 2011 and 2013, respectively, at the same centre-of-mass energy of 2.76 TeV per colliding nucleon pair. The distributions measured in pp collisions are used as a reference for those measured in Pb+Pb collisions in order to evaluate the impact on the internal structure of jets from the jet energy loss of fast partons propagating through the hot, dense medium created in heavy-ion collisions. Modest but significant centrality-dependent modifications of fragmentation functions in Pb+Pb collisions with respect to those in pp collisions are seen. No significant dependence of modifications on jet pt and rapidity selections is observed except for the fragments with the highest transverse momenta for which some reduction of yields is observed for more forward jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Understanding Strain and Failure of a Knot in Polyethylene Using Molecular Dynamics with Machine-Learned Potentials

A neural network potential (NNP) has been developed by fitting to ab initio electronic structure data on hydrocarbons and is used to study failure of linear and knotted polyethylene (PE) chains. A linear PE chain must be highly strained before breaking as the stress is equally distributed across the chain. In contrast, the stress in a PE chain with a 31 or overhand knot, accumulates at the knot’s entrance/exit. We find the strain energy is greatest when the bond length and angle are strained simultaneously, and that the knot weakens the chain by increasing the variance of the C–C–C angle, thereby allowing rupture at lower bond strains. Here, we extend our analysis to both 51 and 52 knots and find that both break at the entrance/exit of a loop. Notably, molecular scale PE knots exhibit many of the same characteristics as knots in a macroscopic rope, with stick–slip phenomena upon tightening and similar points of failure.

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

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Effects of Porous Carbon Morphology, Agglomerate Structure and Relative Humidity on Local O 2 Transport Resistance

The morphology and ionomer distribution in a polymer electrolyte membrane fuel cell (PEMFC) cathode electrode are quantified with nano-scale resolution X-ray computed tomography (nano-CT). Using the nano-CT data, different shapes, sizes and compositions of agglomerates are extracted. Statistical information from multiple techniques, including transmission electron microscopy (TEM), ultra-small angle X-ray scattering (USAXS), and Brunauer-Emmett-Teller (BET) gas adsorption porosimetry are combined to reconstruct the high surface-area porous carbon (HSC) support, exterior (on the surface of carbon) and interior (inside carbon pores) catalysts, ionomer, and primary pores in the extracted agglomerates. Application of capillary condensation theory to the reconstructed agglomerate structure is shown to accurately represent the experimentally-observed relative humidity (RH) dependence of the electrochemically-active surface area (ECA). Direct numerical simulations show that the high local O 2 transport resistance ( R O 2 ) under dry conditions is mainly associated with the reduced ECA. We demonstrate that the agglomerate shape and size affect R O 2 only if the primary pores are poorly accessible (i.e., capillary-condensed water filled primary pores). We also evaluate the effect of transport to the interior catalyst particles on R O 2 .

agglomerate↗
DOE OSTI2023

Genomic variation across Chinook salmon populations reveals effects of a duplication on migration alleles and supports fine scale structure

Abstract The distribution of ecotypic variation in natural populations is influenced by neutral and adaptive evolutionary forces that are challenging to disentangle. This study provides a high‐resolution portrait of genomic variation in Chinook salmon ( Oncorhynchus tshawytscha ) with emphasis on a region of major effect for ecotypic variation in migration timing. With a filtered data set of ~13 million single nucleotide polymorphisms (SNPs) from low‐coverage whole genome resequencing of 53 populations (3566 barcoded individuals), we contrasted patterns of genomic structure within and among major lineages and examined the extent of a selective sweep at a major effect region underlying migration timing (GREB1L/ROCK1). Neutral variation provided support for fine‐scale structure of populations, while allele frequency variation in GREB1L/ROCK1 was highly correlated with mean return timing for early and late migrating populations within each of the lineages ( r 2 = .58–.95; p < .001). However, the extent of selection within the genomic region controlling migration timing was much narrower in one lineage (interior stream‐type) compared to the other two major lineages, which corresponded to the breadth of phenotypic variation in migration timing observed among lineages. Evidence of a duplicated block within GREB1L/ROCK1 may be responsible for reduced recombination in this portion of the genome and contributes to phenotypic variation within and across lineages. Lastly, SNP positions across GREB1L/ROCK1 were assessed for their utility in discriminating migration timing among lineages, and we recommend multiple markers nearest the duplication to provide highest accuracy in conservation applications such as those that aim to protect early migrating Chinook salmon. These results highlight the need to investigate variation throughout the genome and the effects of structural variants on ecologically relevant phenotypic variation in natural species.

Horn, Rebekah L.↗
DOE OSTI2024

Reduced‐Order Modeling for Linearized Representations of Microphysical Process Rates

Abstract Representing cloud microphysical processes in large scale atmospheric models is challenging because many processes depend on the details of the droplet size distribution (DSD, the spectrum of droplets with different sizes in a cloud). While full or partial statistical moments of droplet size distributions are the typical variables used in bulk models, prognostic moments are limited in their ability to represent microphysical processes across the range of conditions experienced in the atmosphere. Microphysical parameterizations employing prognostic moments are known to suffer from structural uncertainty in their representations of inherently higher dimensional cloud processes, which limit model fidelity and lead to forecasting errors. Here we investigate how data‐driven reduced‐order modeling can be used to learn predictors for microphysical process rates in bulk microphysics schemes in an unsupervised manner from higher dimensional bin distributions. Using simulations characteristic of marine stratiform clouds, we simultaneously learn lower dimensional representations of droplet size distributions and predict the evolution of the microphysical state of the system. Droplet collision‐coalescence, the main process for generating warm rain, is estimated to have an intrinsic dimension of three. This intrinsic dimension provides a lower limit on the number of degrees of freedom needed to accurately represent collision‐coalescence in models. We demonstrate how deep learning based reduced‐order modeling can be used to discover intrinsic coordinates describing the microphysical state of the system, where process rates such as collision‐coalescence are globally linearized. These implicitly learned representations of the DSD retain more information about the DSD than typical moment‐based representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Physical Principles of Self-Consistent Simulation of the Generation of Interface States and the Transport of Hot Charge Carriers in Field-Effect Transistors Based on Metal–Oxide–Semiconductor Structures

A detailed simulation of degradation (caused by hot charge carriers) based on self-consistent consideration of the transport of charge carriers and the generation of defects at the SiO{sub 2}/Si interface is carried out for the first time. The model is tested using degradation data obtained with decananometer n-type-channel field-effect transistors. It is shown that the mutual influence of the above aspects is significant and their independent simulation gives rise to considerable quantitative errors. In calculations of the energy distribution for charge carriers, the actual band structure of silicon and such mechanisms as impact ionization, scattering at an ionized impurity, and also electron–phonon and electron–electron interactions are taken into account. At the microscopic level, the generation of defects is considered as the superposition of single-particle and multiparticle mechanisms of breakage of the Si–H bond. A very important applied aspect of this study is the fact that our model makes it possible to reliably assess the operating lifetime of a transistor subjected to the effects of “hot” charge carriers.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Revisiting stochastic variability of AGNs with structure functions

Discrepancies between reported structure function (SF) slopes and their overall flatness as compared to the expectations from the damped random walk (DRW) model, which generally well describes the variability of active galactic nuclei (AGNs), have triggered us to study this problem in detail. We review common AGN variability observables and identify their most common problems. Equipped with this knowledge, we study ∼9000 r-band AGN light curves from Stripe 82 of the Sloan Digital Sky Survey, using SFs described by stochastic processes with the power exponential covariance matrix of the signal. We model the “subensemble” SFs in the redshift–absolute magnitude bins with the full SF equation (including the turnover and the noise part) and a single power law (SPL; in the “red noise regime” after subtracting the noise term). The distribution of full-equation SF (SPL) slopes peaks at γ=0.55±0.08 (0.52 ± 0.06) and is consistent with the DRW model. There is a hint of a weak correlation of γ with the luminosity and a lack of correlation with the black hole mass. The typical decorrelation timescale in the optical is τ=0.97±0.46 year. The SF amplitude at one year obtained from the SPL fitting is SF{sub 0}=0.22±0.06 mag and is overestimated because the SF is already at the turnover part, so the true value is SF{sub 0}=0.20±0.06 mag. The asymptotic variability is SF{sub ∞}=0.25±0.06 mag. It is strongly anticorrelated with both the luminosity and the Eddington ratio and is correlated with the black hole mass. The reliability of these results is fortified with Monte Carlo simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Extended range X-ray pair distribution functions

Here we describe a dual detector system for high-energy X-ray, simultaneous, small and wide-angle X-ray scattering (SAXS and WAXS), designed to extract extended-range pair distribution functions (ER-PDF) for disordered materials. The hardware and software provides continuous reciprocal space coverage over atomic to nanometer length-scales. Details of the varying resolution, splicing of data and normalization on an absolute scale are outlined. In addition, the combination of SAXS and WAXS theory is considered with a view to enabling a direct Fourier transformation of the structure factor spanning multiple length-scales into real space. Important distinctions between the ER-PDF and the pair distance distribution function (PDDF) representations are demonstrated. It is shown that when the SAXS intensity in the structure factor, S(Q), is similar to the WAXS intensity, the contributions to the ER-PDF are minimal. However, when the SAXS S(Q) intensities are substantially stronger than the WAXS, the ER-PDF can provide important structural information on the local, intermediate and nanometer length-scales. Notably, the ER-PDF method provides direct information on particle sizes and their density distributions, overcoming the limitations of PDDF analysis for densely packed systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗