Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Systematic error”

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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Emergent Constraints for Cloud Feedbacks and Climate Sensitivity

Emergent constraints are physically explainable empirical relationships between characteristics of the current climate and long-term climate prediction that emerge in collections of climate model simulations. With the prospect of constraining long-term climate prediction, scientists have recently uncovered several emergent constraints related to long-term cloud feedbacks. We review these proposed emergent constraints, many of which involve the behavior of low-level clouds, and discuss criteria to assess their credibility. With further research, some of the cases we review may eventually become confirmed emergent constraints, provided they are accompanied by credible physical explanations. Because confirmed emergent constraints identify a source of model error that projects onto climate predictions, they deserve extra attention from those developing climate models and climate observations. While a systematic bias cannot be ruled out, it is noteworthy that the promising emergent constraints suggest larger cloud feedback and hence climate sensitivity.

58 GEOSCIENCES↗
DOE OSTI2017

Equilibrium clumped-isotope effects in doubly substituted isotopologues of ethane

We combine path-integral Monte Carlo methods with a new intramolecular potential energy surface to quantify the equilibrium enrichment of doubly substituted ethane isotopologues due to clumped-isotope effects. Ethane represents the simplest molecule to simultaneously exhibit 13C–13C, 13C–D, and D–D clumped-isotope effects, and the analysis of corresponding signatures may provide useful geochemical and biogeochemical proxies of formation temperatures or reaction pathways. Utilizing path-integral statistical mechanics, we predict equilibrium fractionation factors that fully incorporate nuclear quantum effects, such as anharmonicity and rotational-vibrational coupling which are typically neglected by the widely used Urey model. The magnitude of the calculated fractionation factors for the doubly substituted ethane isotopologues indicates that isotopic clumping can be observed if rare-isotope substitutions are separated by up to three chemical bonds, but the diminishing strength of these effects suggests that enrichment at further separations will be negligible. The Urey model systematically underestimates enrichment due to 13C–D and D–D clumped-isotope effects in ethane, leading to small relative errors in the apparent equilibrium temperature, ranging from 5 K at 273.15 K to 30 K at 873.15 K. We additionally note that the rotameric dependence of isotopologue enrichment must be carefully considered when using the Urey model, whereas the path-integral calculations automatically account for such effects due to configurational sampling. These findings are of direct relevance to future clumped-isotope studies of ethane, as well as studies of 13C–13C, 13C–D, and D–D clumped-isotope effects in other hydrocarbons.

Geochemistry & Geophysics↗
DOE OSTI2015

A spectroscopic study of the extreme black widow PSR J1311–3430

We report on a series of spectroscopic observations of PSR J1311–3430, an extreme black-widow gamma-ray pulsar with a helium-star companion. In a previous study we estimated the neutron star mass as M{sub NS}=2.68±0.14 M{sub ⊙} (statistical error), based on limited spectroscopy and a basic (direct heating) light-curve model; however, much larger model-dependent systematics dominate the mass uncertainty. Our new spectroscopy reveals a range of complex source behavior. The variable He i companion wind emission lines can dominate broadband photometry, especially in red filters or near minimum brightness, and the wind flux should complete companion evaporation in a spin-down time. The heated companion face also undergoes dramatic flares, reaching ∼40,000 K over ∼20% of the star; this is likely powered by a magnetic field generated in the companion. The companion center-of-light radial velocity is now well measured with K{sub CoL}=615.4±5.1 km s{sup −1}. We detect non-sinusoidal velocity components due to the heated face flux distribution. Using our spectra to excise flares and wind lines, we generate substantially improved light curves for companion continuum fitting. We show that the inferred inclination and neutron star mass, however, remain sensitive to the poorly constrained heating pattern. The neutron star’s mass, M{sub NS}, is likely less than the direct heating value and could range as low as 1.8 M{sub ⊙} for extreme equatorial heating concentration. While we cannot yet pin down M{sub NS}, our data imply that an intrabinary shock reprocesses the pulsar emission and heats the companion. Improved spectra and, especially, models that include such shock heating are needed for precise parameter measurement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

TH-AB-207A-11: Anatomical and Functional Assessment of Coronary Artery Disease Using Low-Dose Whole-Organ Dynamic Computed Tomography

Purpose: To evaluate the accuracy of a low-dose, first-pass-analysis (FPA) dynamic computed tomography angiography and perfusion (CTAP) technique, for whole-organ anatomical and functional assessment of coronary artery disease (CAD). Methods: An angioplasty balloon was advanced into the left anterior descending (LAD) coronary artery of five swine (35–45 kg) to induce several levels of stenosis at maximal hyperemia (intracoronary adenosine, 240 µg/min). Reference fluorescence microspheres and intravenous contrast (370 mg/mL iodine, 25 mL, 7 mL/s) were injected centrally and dynamic imaging was performed using a 320-slice CT scanner at 100 kVp and 200 mA. Twenty volume scans were acquired per stenosis level to capture complete aortic and myocardial enhancement curves, but only two volume scans were used for whole-organ dynamic FPA CTAP measurement. All CTAP measurements in the LAD were compared to the reference microsphere perfusion measurements using linear regression, concordance correlation, and Bland-Altman analysis. Results: The result of dynamic FPA CTAP measurement in the LAD was in good agreement with the reference microsphere perfusion measurement (P-CTAP = 1.01 P-MICRO + 0.16, R{sup 2} = 0.95). The root mean square error (RMSE) and difference (RMSD) of measurement were 0.51 mL/min/g and 0.47 mL/min/g, respectively. Bland-Altman analysis demonstrated negligible systematic measurement bias. Additionally, the concordance correlation coefficient (CCC) was found to be ρ = 0.97, indicating excellent agreement between dynamic FPA CTAP measurement and the reference microsphere perfusion measurement. Lastly, the effective dose of the proposed technique using the “simulated” two-volume scan CTAP acquisition protocol was 2.6 mSv; much lower than the ∼10 mSv effective dose of current dynamic CTP techniques alone. Conclusion: The results indicate the potential for significant improvements in CAD assessment through low-dose, quantitative dynamic FPA CTAP. Such improvements are afforded through whole-organ CT scanning technology, and have the potential to improve patient outcomes and lead to healthier patient lives. Conflict of Interest (only if applicable): Grant funding from Toshiba America Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

AI to Predict Glass Compositions Satisfying Property and Cooling Rate Criteria

This project aimed to develop a predictive, artificial intelligence/machine learning-based model to identify glass compositions satisfying specified property requirements. Such a model would provide a systematic approach for narrowing down the nearly infinite range of possible compositions for glasses and minimize unnecessary experimental trial and error. A large empirical data set for training and testing the algorithm was obtained from the SciGlass database. It contains glass compositions and corresponding property data from a wide range of literature sources. However, the currently available form of this data, recently released under an open database license, is not conducive to easy querying and use. The data structure was deciphered and a customized parsing code developed to make this data more usable for the current and future work. Neural network models were developed and trained on viscosity data from the database and demonstrated potential for improving prediction accuracy over a traditional regression model.

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

Economic optimization of operations for hybrid energy systems under variable markets

We prosed a hybrid energy systems (HES) which is an important element to enable increasing penetration of clean energy. Our paper investigates the operations flexibility of HES, and develops a methodology for operations optimization for maximizing economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. In order to compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulation results of two specific HES configurations are included to illustrate the proposed methodology and computational capability. These results demonstrate the economic viability of HES under proposed operations optimizer, suggesting the diversion of energy for alternative energy output while participating in the ancillary service market. Economic advantages of such operations optimizer and associated flexible operations are illustrated by comparing the economic performance of flexible operations against that of constant operations. Sensitivity analysis with respect to market variability and prediction error, are also performed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Lu-Hf and Sm-Nd geochronological constraints on the influence of subduction metamorphism in controlling the Hf-Nd terrestrial array: Evidence from the world’s orogenic belts

To create a better understanding of the influence of subduction metamorphism on the Hf-Nd terrestrial array, this study examines all of the available Lu-Hf and Sm-Nd garnet isochron ages from the world’s orogenic eclogites and amphi bolites. The garnet isochron y-intercept values provide a record of each sample’s epsilon Hf ( ε Hf) and Nd ( ε Nd) at the age recorded by the isochron, which in the case of most orogenic eclogites and amphibolites is the time of prograde metamorphism during subduction. It is further possible to assess the extent to which each sample’s Lu-Hf and Sm-Nd isotope systematics have remained undisturbed since the date recorded by the isochron by comparison of the bulk-rock isotope systematics with the isochron initial value. This approach provides a means of understanding the Lu-Hf and Sm-Nd systematics of post-subduction metamorphic oceanic crust. On a global scale, the coupled Lu-Hf and Sm-Nd isotope systematics of this crust closely align with the Hf-Nd terrestrial array and have average 176 Lu/ 177 Hf and 147 Sm/ 144 Nd ratios within error of the chondritic uniform reservoir values. This is an important finding because it suggests that a reservoir of deeply subducted oceanic crust, possibly residing at the core-mantle boundary, is unlikely to be significantly fractionated from the Hf-Nd terrestrial array. In contrast to the samples of subduction-metamorphosed oceanic crust, metamorphic rocks with a continental crustal protolith demonstrate garnet isochron initial values and bulk-rock Lu-Hf and Sm-Nd isotope systematics indicating significant decoupling from the Hf-Nd terrestrial array. However, because this material is not readily subducted, this strongly decoupled signature is unlikely to be transferred into deep Earth. Mixing calculations aimed at developing a clearer understanding of what factors control decoupling of a sample’s Lu-Hf and Sm-Nd systematics from the Hf-Nd terrestrial array highlight the complexity of mass-transfer regimes operating in the subduction environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2010

Evaluation of MatriXX for IMRT and VMAT dose verifications in peripheral dose regions

Purpose: MatriXX is a two-dimensional ion chamber array designed for IMRT/VMAT (RapidArc, IMAT, etc.) dose verifications. Its dosimetric properties have been characterized for megavoltage beams in a number of studies; however, to the best of the authors' knowledge, there is still a lack of an investigation into its performance in the peripheral or low dose regions. In this work, the authors have carried out a systematic study on this issue. Methods: The authors compare the performance of MatriXX with a cylindrical ion chamber in solid water phantoms in the peripheral dose regions. The comparisons are performed for a number of typical irradiation conditions that involve different gantry and/or MLC motions, field sizes, and distances to the target including static gantry/open fields, static gantry/sweeping MLC gap (mimicking an IMRT delivery), dynamic gantry/oscillating sweeping MLC gap (mimicking a VMAT delivery), as well as clinical IMRT and VMAT plans. Results: MatriXX, when used according to the manufacturer's recommendations, is found to disagree with an ion chamber in peripheral dose regions. This disagreement has been attributed to four types of MatriXX errors, namely, positive bias, over-response to scattered doses, round-off error, and angular dependence, all of which contribute to dose inaccuracies in the peripheral regions. The positive bias, which is independent of the dose level, is cumulative when MatriXX operates in the movie mode. The accumulation is proportional to the number of movie frames (snaps) when the sampling time is greater than 500 ms and is proportional to the overall movie time for a sampling time shorter than 500 ms. This behavior suggests multiple sources of the bias. MatriXX is also found to over-respond to peripheral doses by about 2.0% for the regions investigated in this work (3-15 cm from the field edge), where phantom scatter and collimator scatter dominate. Round-off error is determined to be due to insufficient precision in conversion of the raw signals to MatriXX software data for low doses. Angular dependence is defined as the dose response of MatriXX at different gantry angles. Up to 8% difference in detector response has been observed between 0 deg. and 180 deg. Possible sources of these errors are discussed and a correction method is suggested. With corrections, MatriXX shows good agreement with the ion chamber in all cases involving different gantry and/or MLC dynamics, as well as the clinical plans. For both primary and peripheral doses, MatriXX shows dose linearity down to 2 cGy with an accuracy of within 1% of the local dose. Conclusions: The performance of MatriXX has been systematically evaluated in the peripheral dose regions. Major sources of error associated with MatriXX are identified and a correction method is suggested. This method has been successfully tested using both experimental and clinical plans. In all cases, good agreements between MatriXX and an ion chamber are achieved after corrections. The authors conclude that with proper corrections, MatriXX can be reliably used for peripheral dose measurements within the ranges studied.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Evaluating Climate Models’ Cloud Feedbacks Against Expert Judgment

Abstract The persistent and growing spread in effective climate sensitivity (ECS) across global climate models necessitates rigorous evaluation of their cloud feedbacks. Here we evaluate several cloud feedback components simulated in 19 climate models against benchmark values determined via an expert synthesis of observational, theoretical, and high‐resolution modeling studies. We find that models with smallest feedback errors relative to these benchmark values generally have moderate total cloud feedbacks (0.4–0.6 W m −2 K −1 ) and ECS (3–4 K). Those with largest errors generally have total cloud feedback and ECS values that are too large or too small. Models tend to achieve large positive total cloud feedbacks by having several cloud feedback components that are systematically biased high rather than by having a single anomalously large component, and vice versa. In general, better simulation of mean‐state cloud properties leads to stronger but not necessarily better cloud feedbacks. The Python code base provided herein could be applied to developmental versions of models to assess cloud feedbacks and cloud errors and place them in the context of other models and of expert judgment in real‐time during model development.

58 GEOSCIENCES↗
DOE OSTI2017

Fast and self-learning indoor airflow simulation based on in situ adaptive tabulation

Fast simulation for stratified indoor airflow distributions is desired for various applications, such as design of advanced indoor environments, emergency management, and coupled annual energy simulation for buildings with stratified air distributions. Reduced order models trained by pre-computed computational fluid dynamics results are fast, but their prediction may be inaccurate when applied for conditions outside the training domain. To overcome this limitation, we propose in this paper a fast and self-learning model based on an in situ adaptive tabulation (ISAT) algorithm, which is trained by a fast fluid dynamics (FFD) model as an example. The idea is that the ISAT will retrieve the solutions from an existing data set if the estimated prediction error is within a pre-defined tolerance. Otherwise, the ISAT will execute the FFD simulation, which is accelerated by running in parallel on a graphics processing unit, for a full-scale simulation. This paper systematically investigates the feasibility of the ISAT for indoor airflow simulations by presenting the ISAT-FFD implementation alongside results related to its overall performance. Using a stratified indoor airflow as an example, we evaluated how the training time of ISAT was impacted by four factors (training methods, error tolerances, number of inputs, and number of outputs). Then we demonstrated that a trained ISAT model can predict the key information for inputs both inside and outside the training domain. The ISAT was able to answer query points both inside and close to training domain using retrieve actions within a time less than 0.001 s for each query. In conclusion, we provided suggestions for using the ISAT for building applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

ORIGINS OF SCATTER IN THE RELATIONSHIP BETWEEN HCN 1-0 AND DENSE GAS MASS IN THE GALACTIC CENTER

We investigate the correlation of HCN 1-0 with gas mass in the central 300 pc of the Galaxy. We find that on the ∼10 pc size scale of individual cloud cores, HCN 1-0 is well correlated with dense gas mass when plotted as a log–log relationship. There is ∼0.75 dex of scatter in this relationship from clouds like Sgr B2, which has an integrated HCN 1-0 intensity of a cloud less than half its mass, and others that have HCN 1-0 enhanced by a factor of 2–3 relative to clouds of comparable mass. We identify the two primary sources of scatter to be self-absorption and variations in HCN abundance. We also find that the extended HCN 1-0 emission is more intense per unit mass than in individual cloud cores. In fact the majority (80%) of HCN 1-0 emission comes from extended gas with column densities below 7 × 10{sup 22} cm{sup −2}, accounting for 68% of the total mass. We find variations in the brightness of HCN 1-0 would only yield a ∼10% error in the dense gas mass inferred from this line in the Galactic center. However, the observed order of magnitude HCN abundance variations, and the systematic nature of these variations, warn of potential biases in the use of HCN as dense gas mass tracer in more extreme environments such as an active galactic nucleus and shock-dominated regions. We also investigate other 3 mm tracers, finding that HNCO is better correlated with mass than HCN, and might be a better tracer of cloud mass in this environment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Evaluation of Clouds in Version 1 of E3SM Atmosphere Model with Satellite and Simulators

This study systematically evaluates clouds simulated by the Energy Exascale Earth System Model (E3SM) Atmosphere Model version one (EAMv1) against satellite and ground-based cloud observations. Both low (1°) and high (0.25°) resolution EAMv1 configurations generally underestimate clouds in low and midlatitutes and overestimate clouds in the Arctic although the error is smaller in the high-resolution model. The underestimate of clouds is due to the underestimate of optically thin to intermediate clouds. EAMv1 overestimates the optically intermediate to thick clouds. Other model errors include the largely under-predicted stratocumulus along the coasts and high clouds over the tropical deep convection regions. The underestimate of thin clouds results in too much LW radiation being emitted to space and too little SW radiation being reflected back to space while the overestimate of optically intermediate and thick clouds leads to too little LW radiation being emitted to space and too much SW radiation being reflected back to space. EAMv1 shows better skill in reproducing the observed distribution of clouds and their properties and has smaller radiatively relevant errors in the distribution of clouds than most of the CFMIP2 models. It produces more supercooled liquid cloud fraction than CAM5 and most CMIP5 models owing to a new ice nucleation scheme and a reduction of ice deposition growth rate. It simulates the diurnal variation of clouds during the summer season at SGP qualitatively well, with both the evolution of shallow clouds and the nocturnal peak in high clouds well captured, however, it largely overestimates the observed magnitude.

Zhang, Yuying↗
DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗
DOE OSTI2026

Emulating galaxy and peculiar velocity clustering on non-linear scales

We explore the potential of cross-correlating galaxies and peculiar velocities on non-linear scales to enhance cosmological constraints. Leveraging the ABACUSSUMMIT simulation suite and the halo occupation distribution (HOD) formalism, we trained emulator models to describe the non-linear clustering of galaxies and velocities in redshift space. Our analysis demonstrates that combining galaxy and peculiar velocity clustering provides tighter constraints on both HOD and cosmological parameters, particularly on σ8 and w0. We further applied our models to realistic mock catalogues, reproducing the expected density and peculiar velocity errors of type-Ia supernovae, Tully-Fisher and fundamental plane measurements for the combined ZTF and DESI measurements. While systematic biases arise in the HOD parameters, the cosmological constraints remain unbiased, yielding a 3.8% precision measurement on fσ 8 compared to 4.7% when using galaxy clustering alone. We demonstrate that while combining tracers with realistic velocity measurements still yields an improvement, the gains are diminished, highlighting the need for further efforts to reduce velocity measurement uncertainties and correct observational systematics on small scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

A COMPREHENSIVE STATISTICAL ASSESSMENT OF STAR-PLANET INTERACTION

We investigate whether magnetic interaction between close-in giant planets and their host stars produce observable statistical enhancements in stellar coronal or chromospheric activity. New Chandra observations of 12 nearby (d < 60 pc) planet-hosting solar analogs are combined with archival Chandra, XMM-Newton, and ROSAT coverage of 11 similar stars to construct a sample inoculated against inherent stellar class and planet-detection biases. Survival analysis and Bayesian regression methods (incorporating both measurements errors and X-ray upper limits; 13/23 stars have secure detections) are used to test whether ''hot Jupiter'' hosts are systematically more X-ray luminous than comparable stars with more distant or smaller planets. No significant correlations are present between common proxies for interaction strength (M {sub P}/a {sup 2} or 1/a) versus coronal activity (L {sub X} or L {sub X}/L {sub bol}). In contrast, a sample of 198 FGK main-sequence stars does show a significant (∼99% confidence) increase in X-ray luminosity with M {sub P}/a {sup 2}. While selection biases are incontrovertibly present within the main-sequence sample, we demonstrate that the effect is primarily driven by a handful of extreme hot-Jupiter systems with M {sub P}/a {sup 2} > 450 M {sub Jup} AU{sup –2}, which here are all X-ray luminous but to a degree commensurate with their Ca II H and K activity, in contrast to presented magnetic star-planet interaction scenarios that predict enhancements relatively larger in L {sub X}. We discuss these results in the context of cumulative tidal spin-up of stars hosting close-in gas giants (potentially followed by planetary infall and destruction). We also test our main-sequence sample for correlations between planetary properties and UV luminosity or Ca II H and K emission, and find no significant dependence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Biased tracers of two fluids in the Lagrangian picture

We explore Lagrangian perturbation theory (LPT) for biased tracers in the presence of two fluids, focusing on the case of cold dark matter (CDM) and baryons. The presence of two fluids induces corrections to the Lagrangian bias expansion and tracer advection, both of which we formulate as expansions in the three linear modes of the Lagrangian equations of motion. We compute the linear-order two-fluid corrections in the Zeldovich approximation, finding that modifications to the bias expansion and tracer advection both enter as percent-level corrections over a large range of wavenumbers at low redshift and draw parallels with the Eulerian formalism. We then discuss nonlinear corrections in the two-fluid picture, and calculate contributions from the relative velocity effect ($\propto$ v$_r^2$) at one loop order. Finally, we conduct an exploratory Fisher analysis to assess the impact of two-fluid corrections on baryon acoustic oscillations (BAO) measurements, finding that while modest values of the relative bias parameters can introduce systematic biases in the measured BAO scale of up to 0.5 $σ$, fitting for these effects as additional parameters increases the error bar by less than 30% across a wide range of bias values.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Measuring cosmological distances using cluster edges as a standard ruler

ABSTRACT The line-of-sight velocity dispersion profile of galaxy clusters exhibits a ‘kink’ corresponding to the spatial extent of orbiting galaxies. Because the spatial extent of a cluster is correlated with the amplitude of the velocity dispersion profile, we can utilize this feature as a gravity-calibrated standard ruler. Specifically, the amplitude of the velocity dispersion data allows us to infer the physical cluster size. Consequently, observations of the angular scale of the ‘kink’ in the profile can be translated into a distance measurement to the cluster. Assuming the relation between cluster radius and cluster velocity dispersion can be calibrated from simulations, we forecast that with existing data from the Sloan Digital Sky Survey we will be able to measure the Hubble constant with 3.0 per cent precision. Implementing our method with data from the Dark Energy Spectroscopic Instrument (DESI) will result in a 1.3 per cent measurement of the Hubble constant. Adding cosmological supernova data improves the uncertainty of the DESI measurement to 0.7 per cent. While these error estimates are statistical only, they provide strong motivation for pursuing the necessary simulation program required to characterize and calibrate the systematic uncertainties impacting our proposed measurement. Whether or not our proposed measurement can in fact result in competitive H0 constraints will depend on what the eventual systematics floor for this method is.

79 ASTRONOMY AND ASTROPHYSICS↗