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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

The Stability of Nd Hydroxyl Complexes at Near Neutral to Alkaline pH from 25 to 75 °C: Implications for Monazite Solubility in Hydrothermal Aqueous Fluids

Speciation of rare earth elements (REE) in aqueous fluids controls their mobilization during fluid-rock interaction. Thermodynamic modeling provides important insights into the factors controlling REE mobility and fractionation in aqueous fluids, in which aqueous REE hydroxyl complexes are particularly important across a broad pH range. However, the stability of these REE hydroxyl complexes remains poorly determined due to limited experimental data above ambient temperature. In this work, UV–vis spectrophotometry experiments were conducted to determine the cumulative hydrolysis constants for neodymium (Nd) from 25 to 75 °C in near-neutral to alkaline solutions. The color indicator m -cresol purple was used to determine in situ pH. Alkaline NaOH-bearing aqueous solutions were doped with varying initial Nd concentrations (0 to ∼0.155 mmol/kg), which resulted in the release of protons (H + ) and pH decrease during hydrolysis of Nd 3+ according to Nd 3+ + n H 2 O = Nd(OH) n 3– n + H + . The average OH – ligand number coordinated to Nd 3+ was found to increase from 1.0 to 1.6 at 25 °C and from 1.2 to 2.8 at 75 °C over a pH range from 6.3 to 9.0. The measured speciation shows an increased predominance of Nd(OH) 3 0 over the Nd(OH) 2 + and Nd(OH) 2+ species with increased temperature and pH. The increasing cumulative formation constants (log*β° n , n = 1 to 3) retrieved from 25 to 75 °C differ by 0.1 to 1.9 logarithmic units in comparison to existing thermodynamic databases. These updated thermodynamic data have important implications for geochemical modeling of the speciation of Nd hydroxyl complexes and the solubility of monazite as a function of pH.

58 GEOSCIENCES↗
DOE OSTI2020

Linking Hydrology and Dissolved Organic Matter Characteristics in a Subtropical Wetland: A Long-Term Study of the Florida Everglades

Dissolved organic matter (DOM) acts as an important biogeochemical component of aquatic ecosystems that controls nutrient cycling, influences water quality, and links terrestrial and oceanic carbon pools, yet long-term studies of how changing environmental drivers alter its abundance and composition are rare. Using a ten-year, spatially explicit dataset from Everglades National Park (ENP), a globally significant wetland, we investigated the relationships between DOM quality/quantity and hydrologic/climatic drivers along two contrasting marsh-estuarine transects based on generalized linear modeling and a cumulative sums analysis. Analyses revealed distinct spatial, seasonal, and interannual patterns in variability of DOC and optical properties. Landscape-scale seasonal patterns showed an enrichment in microbial-like and protein-like DOM during the dry season relative to the wet season. We found that, while some compositional constituents varied with the solar calendar, responsive to temperature and photoperiod, others varied with the hydrologic calendar. Independent water level and discharge effects indicated strong hydrologic control on DOM quality that differed between the two transects, evidencing differences in their connectivity to areas of high agricultural activity. Across all sites, a significant long-term increasing trend in the fluorescence index was observed, associated with a positive correlation with precipitation and also potential changes in agricultural inputs, with other features associated with drought and hurricanes. Lastly, the cumulative sums analysis revealed differences between the two transects in the sensitivity of DOM composition to decreased water levels associated with 30-year climate scenarios, with the less hydrologically dynamic transect exhibiting greater potential sensitivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Tundra Underlain By Thawing Permafrost Persistently Emits Carbon to the Atmosphere Over 15 Years of Measurements

Warming of the Arctic can stimulate microbial decomposition and release of permafrost soil carbon (C) as greenhouse gases, and thus has the potential to influence climate change. At the same time, plant growth can be stimulated and offset C release. This study presents a 15-year time series comprising chamber and eddy covariance measurements of net ecosystem C exchange in a tundra ecosystem in Alaska where permafrost has been degrading due to regional warming. The site was a carbon dioxide source to the atmosphere with a cumulative total loss of 781.6 g C m -2 over the study period. Both gross primary productivity (GPP) and ecosystem respiration (R eco ) were already likely higher than historical levels such that increases in R eco losses overwhelmed GPP gains in most years. This shift to a net C source to the atmosphere likely started in the early 1990s when permafrost was observed to warm and thaw at the site. Shifts in the plant community occur more slowly and are likely to constrain future GPP increases as compared to more rapid shifts in the microbial community that contribute to increased Reco. Observed rates suggest that cumulative net soil C loss of 4.18–10.00 kg C m -2 —8%–20% of the current active layer soil C pool—could occur from 2020 to the end of the century. This amount of permafrost C loss to the atmosphere represents a significant accelerating feedback to climate change if it were to occur at a similar magnitude across the permafrost region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

How Does Flow Alteration Propagate Across a Large, Highly Regulated Basin? Dam Attributes, Network Context, and Implications for Biodiversity

Abstract Large dams are a leading cause of river ecosystem degradation. Although dams have cumulative effects as water flows downstream in a river network, most flow alteration research has focused on local impacts of single dams. Here we examined the highly regulated Colorado River Basin (CRB) to understand how flow alteration propagates in river networks, as influenced by the location and characteristics of dams as well as the structure of the river network—including the presence of tributaries. We used a spatial Markov network model informed by 117 upstream‐downstream pairs of monthly flow series (2003–2017) to estimate flow alteration from 84 intermediate‐to‐large dams representing >83% of the total storage in the CRB. Using Least Absolute Shrinkage and Selection Operator regression, we then investigated how flow alteration was influenced by local dam properties (e.g., purpose, storage capacity) and network‐level attributes (e.g., position, upstream cumulative storage). Flow alteration was highly variable across the network, but tended to accumulate downstream and remained high in the main stem. Dam impacts were explained by network‐level attributes (63%) more than by local dam properties (37%), underscoring the need to consider network context when assessing dam impacts. High‐impact dams were often located in sub‐watersheds with high levels of native fish biodiversity, fish imperilment, or species requiring seasonal flows that are no longer present. These three biodiversity dimensions, as well as the amount of dam‐free downstream habitat, indicate potential to restore river ecosystems via controlled flow releases. Our methods are transferrable and could guide screening for dam reoperation in other highly regulated basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Method of Distributions for Two‐Phase Flow in Heterogeneous Porous Media

Abstract Multiscale heterogeneity and insufficient characterization data for a specific subsurface formation of interest render predictions of multi‐phase fluid flow in geologic formations highly uncertain. Quantification of the uncertainty propagation from the geomodel to the fluid‐flow response is typically done within a probabilistic framework. This task is computationally demanding due to, for example, the slow convergence of Monte Carlo simulations (MCS), especially when computing the tails of a distribution that are necessary for risk assessment and decision‐making under uncertainty. The frozen streamlines method (FROST) accelerates probabilistic predictions of immiscible two‐phase fluid flow problems; however, FROST relies on MCS to compute the travel‐time distribution, which is then used to perform the transport (phase saturation) computations. To alleviate this computational bottleneck, we replace MCS with a deterministic equation for the cumulative distribution function (CDF) of travel time. The resulting CDF‐FROST approach yields the CDF of the saturation field without resorting to sampling‐based strategies. Our numerical experiments demonstrate the high accuracy of CDF‐FROST in computing the CDFs of both saturation and travel time. For the same accuracy, it is about 5 and 10 times faster than FROST and MCS, respectively.

Yang, Hyung Jun↗
DOE OSTI2023

Small-Magnitude Seismic Swarms in Central Utah (US): Interactions of Regional Tectonics, Local Structures and Hydrothermal Systems

Swarms in Central Utah are situated in the complex transition between the Basin and Range (BR) province and the Colorado Plateau. Transecting transverse structures, volcanic deposits, and hydrothermal systems complicate the extensional BR horst and graben structures and provide a multitude of plausible triggering mechanisms. Revisiting the catalog of the University of Utah Seismograph Stations (1981–2022), we analyze spatio-temporal patterns and characteristic features of seismic sequences. Swarms with alternating seismicity rates, bursts, and longer swarms with persistent moment release exhibit a remarkable diversity in temporal evolution. Swarm durations do not scale with cumulative seismic moment: swarms lasting less than 1 day can have similar cumulative seismic moments as month-long swarms. Here, we observe stationary swarms re-occurring for years (e.g., Mineral Mountains), as well as singular swarms in low-seismicity areas (e.g., activating a local structure). The swarms show a pronounced heterogeneity in triggering and driving mechanisms, observed in the detailed analysis of exemplary sequences (detections, relocations, moment tensors, waveform-based clustering, and repeater analysis). The 2022 Sevier Valley sequence activated a BR-related normal fault, the first resolved fault plane in the valley since 1983. The 2011 Circleville sequence is interpreted as a swarm triggered by mainshock-aftershock activity characterized by increasing magnitudes, changing rupture mechanisms, and a concentration of highly similar events in the second part of the sequence. By jointly discussing exemplary sequences and catalog statistics, we draw a comprehensive picture of swarm activity and its relation to geothermal and tectonic activity.

58 GEOSCIENCES↗
DOE OSTI2026

Fault Slip and Fluid Flow: Seismic Source Analysis to Assess Role of Multiple Slip Patches in Fault Permeability

The relationship between fault reactivation, microearthquakes (MEQs), and permeability evolution during fluid injection plays a critical role in energy harvesting and waste disposal. Recent studies have demonstrated the possibility of predicting fault permeability using cumulative seismic moments of MEQs quantitatively. To understand the underlying physical processes, we conduct fault reactivation experiments using Utah FORGE granitoid and analyze acoustic emission (AE) signals generated during stepwise increases in fluid injection pressure. Frequency analysis of thousands of calibrated AE signals reveals that fault reactivation produces multiple AE source patches with millimeter-scale radii—smaller than the sample fault radius. The cumulative area of the reactivated patches covers the fault multiple times over (∼10x–50x area) for each pressure step. These findings provide mechanistic insight that measured permeability enhancement is not driven by a single large slip event, but by the sequential and interacting activation of multiple slip patches that create a continuous flow pathway.

Nurshal, M. E. M. [Pennsylvania State University, ↗
DOE OSTI2025

Emissions leakage and economic losses may undermine deforestation-linked oil crop import restrictions

Import restrictions on deforestation-linked commodities are being considered strategies to reduce global deforestation and emissions. However, limited market share of importers imposing such restrictions and the potential for emissions leakage could reduce their effectiveness. Moreover, they could result in negative economic implications for producers and consumers. We quantify future emissions and economic implications of oil palm and soybean import restrictions. Current EU restrictions will likely have minimal impact due to the EU’s otherwise small and diminishing share of global palm and soy demand. If extended beyond the EU, such import restrictions could drive reductions in cumulative LUC emissions by 2050 in key oil crop exporting regions— up to 0.9% in Indonesia, 1.5% in the rest of Southeast Asia, 3.8% in Argentina and 6.7% in Brazil, relative to a scenario with no import restrictions. However, these key exporters could also face losses ranging $\$$4.1-$\$$61 billion in cumulative agricultural production revenue by 2050.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Author Correction: US oil and gas system emissions from nearly one million aerial site measurements

Correction to: Naturehttps://doi.org/10.1038/s41586-024-07117-5 Published online 13 March 2024 In the version of the article initially published, several errors were present and have been corrected in the HTML and PDF versions of the article and Supplementary Information. The main results, conclusions, and our interpretations of the data remain unchanged. See the new Supplementary Information Section S15 for a more detailed description of the errors corrected and the resulting effects on the analysis. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. The following practices may help researchers conducting similar analyses avoid making similar errors: 1, Clear, accessible documentation explaining the interpretation of all columns in data input tables and all internal variables within the model, 2, Simple cross-check calculations computed before and after unit conversions.

Sherwin, Evan D↗
DOE OSTI2020

Intercropping kura clover with prairie cordgrass mitigates soil greenhouse gas fluxes

Prairie cordgrass (PCG) ( Spartina pectinata Link) has a high tolerance to soil salinity and waterlogging, therefore, it can thrive on marginal lands. Optimizing the nitrogen (N) input is crucial to achieving desirable biomass production of PCG without negatively impacting the environment. Thus, this study was based on the hypothesis that the use of legumes such as kura clover ( Trifolium ambiguum M. Bieb.) (KC) as an intercrop with PCG can provide extra N to the crop reducing the additional N fertilizer and mitigating soil surface greenhouse gas (GHG) emissions. Specific objective of the study was to assess the impact of PCG managed with different N rates [0 kg N ha -1 (PCG-0N), 75 kg N ha -1 (PCG-75N), 150 kg N ha -1 (PCG-150N), and 225 kg N ha -1 (PCG-255N)], and PCG intercropped with KC (PCG-KC) on GHG fluxes and biomass yield. The experimental site was established in 2010 in South Dakota under a marginally yielding cropland. The GHG fluxes were measured from 2014 through 2018 growing seasons using the static chamber. Net global warming potential (GWP) was calculated. Data showed that cumulative CH 4 and CO 2 fluxes were similar for all the treatments over the study period. However, the PCG-KC, PCG-0N, and PCG-75N recorded lower cumulative N 2 O fluxes (384, 402, and 499 g N ha -1 , respectively) than the PCG-150N (644 g N ha -1 ) and PCG-255N (697 g N ha -1 ). The PCG-KC produced 85% and 39% higher yield than the PCG-0N in 2016 and 2017, respectively, and similar yield to the other treatments (PCG-75N, PCG-150N, and PCG-255N) in these years. Net GWP was 52% lower for the PCG-KC (112.38 kg CO 2 -eq ha -1 ) compared to the PCG-225N (227.78 kg CO 2 -eq ha -1 ), but similar to other treatments. Soil total N was 15%% and 13% higher under PCG-KC (3.7 g kg -1 ) than that under PCG-0N (3.2 g kg -1 ) and PCG-75N (3.3 g kg -1 ), respectively. This study concludes that intercropping prairie cordgrass with kura clover can enhance biomass yield and reduce fertilizer-derived N 2 O emissions and net global warming potential.

09 BIOMASS FUELS↗
DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗
DOE OSTI2024

Microscopic origin of criticality at macroscale in QCD chiral phase transition

We reveal that the criticality of the chiral phase transition in QCD at the macroscale arises from the microscopic energy levels of its fundamental constituents, the quarks. We establish a novel relation between cumulants of the chiral order parameter (i.e., chiral condensate) and correlations among the energy levels of quarks (i.e., eigenspectra of the massless Dirac operator), which naturally leads to a generalization of the Banks-Casher relation. Based on this novel relation and through (2+1)-flavor lattice QCD calculations using the HISQ action with varying light quark masses in the vicinity of the chiral phase transition, we demonstrate that the correlations among the infrared part of the Dirac eigenspectra exhibit same universal scaling behaviors as expected of the cumulants of the chiral condensate. We find that these universal scaling behaviors extend up to the physical values of the up and down quark masses.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Equilibrium expectations for non-Gaussian fluctuations near a QCD critical point

With the highly anticipated results from the Beam Energy Scan II program at RHIC being recently revealed, an understanding of particle-number fluctuations and their significance as a potential signature of a possible QCD critical point is crucial. Early works that embarked on this endeavor sought to estimate the fluctuations due to the presence of a critical point assuming they stay in equilibrium. From these results came the proposal to focus efforts on higher, non-Gaussian, moments of the event-by-event distributions, in particular of the number of protons. These non-Gaussian moments are especially sensitive to critical fluctuations, as their magnitudes are proportional to high powers of the critical correlation length. As the equation of state provides key input for hydrodynamical simulations of heavy-ion collisions, we estimate equilibrium fluctuations from the BEST equation of state (EoS) that includes critical features from the 3D Ising Model. In particular, the proton factorial cumulants and their dependence on non-universal mapping parameters is investigated within the BEST EoS. Furthermore, the correlation length, as a central quantity for the assessment of fluctuations in the vicinity of a critical point, is also calculated in a consistent manner with the scaling equation of state. An understanding of the equilibrium estimates of proton factorial cumulants will be useful for further comparison to estimates of out-of-equilibrium fluctuations in order to determine the magnitude of the observable fluctuations to be expected in heavyion collision experiments, in which the time spent near a critical point is short.

Karthein, Jamie M. [Massachusetts Institute of Tec↗
DOE OSTI2025

Fission Product Yield Modeling and Evaluation

Although independent and cumulative fission product yields have been a part of evaluated libraries for decades, there have been few updates over the years. The fission product yield sub-library in the ENDF/B-VIII.0 library is still largely based on the evaluation of England and Rider from the mid-90’s, with only more recent updates to the energy dependence of 239 Pu below 2 MeV and fixes to isomeric states and missing fission products. Over the past several years, there have been a wealth of new measurements of independent and cumulative fission product yields, particularly those with short half-lives, and there have been significant improvements in the modeling of prompt and delayed fission observables. Here, we describe recent progress in the improvement of fission product yield calculations, using the BeoH code and the underlying Hauser Fesh-bach Fission Fragment Decay (HF 3 D) model, developed at Los Alamos National Laboratory. We will describe our recent calculations for consistent prompt and delayed fission observables for major and minor actinides, including new work investigating isomeric ratios. We will detail the ongoing evaluation process for energy-dependent fission product yields from thermal up to 20 MeV incident neutron energy and some validation work that has been performed for these new fission product yield calculations. Additionally, we will discuss future perspectives of this work, highlighting the need for additional data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Too big, too small, or just right? A benchmark assessment of density functional theory for predicting the spatial extent of the electron density of small chemical systems

Multipole moments are the first-order responses of the energy to spatial derivatives of the electric field strength. The quality of density functional theory prediction of molecular multipole moments thus characterizes errors in modeling the electron density itself, as well as the performance in describing molecules interacting with external electric fields. However, only the lowest non-zero moment is translationally invariant, making the higher-order moments origin-dependent. Therefore, instead of using the 3 × 3 quadrupole moment matrix, we utilize the translationally invariant 3 × 3 matrix of second cumulants (or spatial variances) of the electron density as the quantity of interest (denoted by K). The principal components of K are the square of the spatial extent of the electron density along each axis. A benchmark dataset of the principal components of K for 100 small molecules at the coupled cluster singles and doubles with perturbative triples at the complete basis set limit is developed, resulting in 213 independent K components. The performance of 47 popular and recent density functionals is assessed against this Var213 dataset. Several functionals, especially double hybrids, and also SCAN and SCAN0 predict reliable second cumulants, although some modern, empirically parameterized functionals yield more disappointing performance. The H, Li, and Be atoms, in particular, are challenging for nearly all methods, indicating that future functional development could benefit from the inclusion of their density information in training or testing protocols.

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

Evaluation of two-particle properties within finite-temperature self-consistent one-particle Green’s function methods: Theory and application to GW and GF2

One-particle Green’s function methods can model molecular and solid spectra at zero or non-zero temperatures. One-particle Green’s functions directly provide electronic energies and one-particle properties, such as dipole moment. However, the evaluation of two-particle properties, such as $\langle$S 2 $\rangle$ and $\langle$N 2 $\rangle$, can be challenging because they require a solution of the computationally expensive Bethe–Salpeter equation to find two-particle Green’s functions. We demonstrate that the solution of the Bethe–Salpeter equation can be completely avoided. Applying the thermodynamic Hellmann–Feynman theorem to self-consistent one-particle Green’s function methods, we derive expressions for two-particle density matrices in a general case and provide explicit expressions for GF2 and GW methods. Such density matrices can be decomposed into an antisymmetrized product of correlated one-electron density matrices and the two-particle electronic cumulant of the density matrix. Cumulant expressions reveal a deviation from ensemble representability for GW, explaining its known deficiencies. We analyze the temperature dependence of $\langle$S 2 $\rangle$ and $\langle$N 2 $\rangle$ for a set of small closed-shell systems. Interestingly, both GF2 and GW show a non-zero spin contamination and a non-zero fluctuation of the number of particles for closed-shell systems at the zero-temperature limit.

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

Parametrized ion-distribution model for extended x-ray absorption fine-structure analysis at high-energy-density conditions

Experiments today can compress solids near isentropically to pressures approaching 100 × 106 atmospheres; however, determining the temperature of such matter remains a major challenge. Extended x-ray absorption fine-structure (EXAFS) spectroscopy is one of the few techniques sensitive to the bulk temperature of highly compressed solid matter, and the validity of this temperature measurement relies on constraining the local ion structure from the EXAFS spectrum. At high-energy-density (HED) conditions, the local ion structure often becomes distorted, which must be accounted for during the EXAFS analysis. Described here is a technique, using a parametrized ion-distribution model to directly analyze EXAFS spectra that provides a better constraint on the local structure than traditional second- or third-order cumulant expansion techniques at HED conditions. The parametrized ion-distribution model is benchmarked by analyzing EXAFS spectra from nickel molecular-dynamics simulations at ∼100 GPa and shown to provide a 10%–20% improvement in constraining the cumulants of the true ion distribution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Cross-correlating radial peculiar velocities and CMB lensing convergence

We study, for the first time, the cross correlation between the angular distribution of radial peculiar velocities (PV) and the lensing convergence of cosmic microwave background (CMB) photons. We derive theoretical expectations for the signal and its covariance and assess its detectability with existing and forthcoming surveys. We find that such cross-correlations are expected to improve constraints on different gravitational models by partially breaking degeneracies with the matter density. We identify in the distance-scaling dispersion of the peculiar velocities the most relevant source of noise in the cross correlation. For this reason, we also study how the above picture changes assuming a redshift-independent scatter for the PV, obtained for example using a reconstruction technique. Our results show that the cross correlation might be detected in the near future combining PV measurements from DESI and the convergence map from CMB-S4. Using realistic direct PV measurements we predict a cumulative signal-to-noise ratio of approximately 3.8σ using data on angular scales 3 ≤ ℓ ≤ 200. For an idealized reconstructed peculiar velocity map extending up to redshift z = 0.15 and a smoothing scale of 4 Mpc h -1 we predict a cumulative signal-to-noise ratio of approximately 27σ from angular scales 3 ≤ ℓ ≤ 200. We conclude that currently reconstructed peculiar velocities have more constraining power than directly observed ones, even though they are more cosmological-model dependent.

79 ASTRONOMY AND ASTROPHYSICS↗