SEARCH · Engineering Papers

Results for “estimation”

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.

SourceResearch connectionFrom yearThrough year

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

At least 811 records · Page 45

All sources NASA NTRS DOE OSTI

DOE OSTI2013

Thermodynamic estimation: Ionic materials

Thermodynamics establishes equilibrium relations among thermodynamic parameters (“properties”) and delineates the effects of variation of the thermodynamic functions (typically temperature and pressure) on those parameters. However, classical thermodynamics does not provide values for the necessary thermodynamic properties, which must be established by extra-thermodynamic means such as experiment, theoretical calculation, or empirical estimation. While many values may be found in the numerous collected tables in the literature, these are necessarily incomplete because either the experimental measurements have not been made or the materials may be hypothetical. The current paper presents a number of simple and relible estimation methods for thermodynamic properties, principally for ionic materials. The results may also be used as a check for obvious errors in published values. The estimation methods described are typically based on addition of properties of individual ions, or sums of properties of neutral ion groups (such as “double” salts, in the Simple Salt Approximation), or based upon correlations such as with formula unit volumes (Volume-Based Thermodynamics). - Graphical abstract: Thermodynamic properties of ionic materials may be readily estimated by summation of the properties of individual ions, by summation of the properties of ‘double salts’, and by correlation with formula volume. Such estimates may fill gaps in the literature, and may also be used as checks of published values. This simplicity arises from exploitation of the fact that repulsive energy terms are of short range and very similar across materials, while coulombic interactions provide a very large component of the attractive energy in ionic systems. Display Omitted - Highlights: • Estimation methods for thermodynamic properties of ionic materials are introduced. • Methods are based on summation of single ions, multiple salts, and correlations. • Heat capacity, entropy, lattice energy, enthalpy, Gibbs energy values are available.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Estimation of sensor measurement errors in reactor coolant systems using multi-sensor fusion

A nuclear power plant is typically instrumented with a variety of sensors to continually monitor its variables, and their sensor’s measurements may be used to assess the plant state and initiate safety actions, if needed. Errors in sensor measurements, due to factors such as calibration drifts, critically affect such state assessments. Here, we address a problem of estimating sensor errors using physics-informed machine learning methods that use measurements collected under known plant conditions. For a given sensor, we propose an information fusion method that uses measurements from other sensors to estimate its output assuming it is error-free and provides its difference from an actual measurement as an error estimate. We present the ensemble of trees and support vector machine fusers, and evaluate their performance using measurements collected over an emulated test loop of a pressurized water reactor. The plant variables are related to each other through the underlying physical laws under inertial constraints that place bounds on their derivatives, which analytically justify the applicability of machine learning methods for computing these fusers. Under twenty scenarios, we assess their sensor error estimates for pressure sensors of the heat exchanger of a reactor’s primary coolant system. Multiple types of errors are captured by both fusers under externally induced calibration drifts, blockages, minor leaks and air gaps in sensing lines, and electromagnetic interference; the root mean square error of the estimation of error is under 2.2% percent of the maximum measurement. We present generalization equations, in the framework of statistical learning theory, for these methods that characterize the confidence probability that the estimation error is bounded by a specified parameter in future test scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

An adaptive synchronous extraction (ASE) method for estimating intensity and footprint of surface urban heat islands: A case study of 254 North American cities

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. In conclusion, overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Covariance operator estimation via adaptive thresholding

This paper studies sparse covariance operator estimation for nonstationary processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample covariance function using an estimate of the variance component. Building on recent results from empirical process theory, we derive an operator norm bound on the estimation error in terms of the sparsity level of the covariance and the expected supremum of a normalized process. Furthermore, our theory and numerical simulations demonstrate the advantage of adaptive threshold estimators over universal threshold and sample covariance estimators in nonstationary settings.

Al-Ghattas, Omar [University of Chicago, IL (Unite↗

DOE OSTI2020

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

Introduction: hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed. Methods: A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates. Results: A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used. Discussion: This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

Estimation of end of life mobile phones generation: The case study of the Czech Republic

Highlights: Black-Right-Pointing-Pointer In this paper, we define lifespan of mobile phones and estimate their average total lifespan. Black-Right-Pointing-Pointer The estimation of lifespan distribution is based on large sample of EoL mobile phones. Black-Right-Pointing-Pointer Total lifespan of Czech mobile phones is surprisingly long, exactly 7.99 years. Black-Right-Pointing-Pointer In the years 2010-20, about 26.3 million pieces of EoL mobile phones will be generated in the Czech Republic. - Abstract: The volume of waste electrical and electronic equipment (WEEE) has been rapidly growing in recent years. In the European Union (EU), legislation promoting the collection and recycling of WEEE has been in force since the year 2003. Yet, both current and recently suggested collection targets for WEEE are completely ineffective when it comes to collection and recycling of small WEEE (s-WEEE), with mobile phones as a typical example. Mobile phones are the most sold EEE and at the same time one of appliances with the lowest collection rate. To improve this situation, it is necessary to assess the amount of generated end of life (EoL) mobile phones as precisely as possible. This paper presents a method of assessment of EoL mobile phones generation based on delay model. Within the scope of this paper, the method has been applied on the Czech Republic data. However, this method can be applied also to other EoL appliances in or outside the Czech Republic. Our results show that the average total lifespan of Czech mobile phones is surprisingly long, exactly 7.99 years. We impute long lifespan particularly to a storage time of EoL mobile phones at households, estimated to be 4.35 years. In the years 1990-2000, only 45 thousands of EoL mobile phones were generated in the Czech Republic, while in the years 2000-2010 the number grew to 6.5 million pieces and it is estimated that in the years 2010-2020 about 26.3 million pieces will be generated. Current European legislation sets targets on collection and recycling of WEEE in general, but no specific collection target for EoL mobile phone exists. In the year 2010 only about 3-6% of Czech EoL mobile phones were collected for recovery and recycling. If we make similar estimation using an estimated average EU value, then within the next 10 years about 1.3 billion of EoL mobile phones would be available for recycling in the EU. This amount contains about 31 tonnes of gold and 325 tonnes of silver. Since Europe is dependent on import of many raw materials, efficient recycling of EoL products could help reduce this dependence. To set a working system of collection, it will be necessary to set new and realistic collection targets.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

A BIM-based system for demolition and renovation waste estimation and planning

Highlights: ► We developed a waste estimation system leveraging the BIM technology. ► The system can calculate waste disposal charging fee and pick-up truck demand. ► We presented an example scenario demonstrating this system. ► Automatic, time-saving and wide applicability are the features of the system. - Abstract: Due to the rising worldwide awareness of green environment, both government and contractors have to consider effective construction and demolition (C and D) waste management practices. The last two decades have witnessed the growing importance of demolition and renovation (D and R) works and the growing amount of D and R waste disposed to landfills every day, especially in developed cities like Hong Kong. Quantitative waste prediction is crucial for waste management. It can enable contractors to pinpoint critical waste generation processes and to plan waste control strategies. In addition, waste estimation could also facilitate some government waste management policies, such as the waste disposal charging scheme in Hong Kong. Currently, tools that can accurately and conveniently estimate the amount of waste from construction, renovation, and demolition projects are lacking. In the light of this research gap, this paper presents a building information modeling (BIM) based system that we have developed for estimation and planning of D and R waste. BIM allows multi-disciplinary information to be superimposed within one digital building model. Our system can extract material and volume information through the BIM model and integrate the information for detailed waste estimation and planning. Waste recycling and reuse are also considered in our system. Extracted material information can be provided to recyclers before demolition or renovation to make recycling stage more cooperative and more efficient. Pick-up truck requirements and waste disposal charging fee for different waste facilities will also be predicted through our system. The results could provide alerts to contractors ahead of time at project planning stage. This paper also presents an example scenario with a 47-floor residential building in Hong Kong to demonstrate our D and R waste estimation and planning system. As the BIM technology has been increasingly adopted in the architectural, engineering and construction industry and digital building information models will likely to be available for most buildings (including historical buildings) in the future, our system can be used in various demolition and renovation projects and be extended to facilitate project control.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2017

An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks

We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable of tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.

Jalving, Jordan↗

DOE OSTI2022

Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions

Abstract Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high‐frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady‐state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub‐daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation‐based approach to estimate the sub‐daily flux under highly dynamic flow conditions using multi‐depth temperature observations at a 5‐min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi‐depth temperature data at 1‐ or 2‐hr time intervals assuming one‐dimensional flow and heat transport governing equation. By comparing estimated fluxes with model‐generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation‐based flux estimation method was able to capture the vertical sub‐daily fluxes using multi‐depth high‐resolution temperature data alone, even in the presence of multi‐dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1‐D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A Coupled Deep Learning Model for Estimating Surface NO 2 Levels from Remote Sensing Data: 15-Year Study Over the Contiguous United States

This study proposes a novel two-step deep learning (DL) model for estimating surface NO 2 concentrations using satellite data over the contiguous United States (CONUS) from 2005 to 2019. The first phase of the model uses partial convolutional neural network (PCNN), an advanced DL model that accurately imputes gaps between surface NO 2 stations and creates 5,478 daily-mean NO 2 grids (PCNN-NO 2 ) of the 2005-2019 period over the study area. We then feed the PCNN-NO 2 , along with other predictor variables, into a deep neural network (DNN) to estimate surface NO 2 levels, achieving exceptional performance with a correlation coefficient of 0.975 to 0.978, a mean absolute bias of 0.99 ppb to 1.38 ppb, and a root mean square error of 1.47 ppb to 1.97 ppb. Spatial cross-validation results also indicate strong spatial performance of PCNN-DNN surface NO 2 estimates. In addition to its accurate estimates, the PCNN-DNN model consistently generates estimated NO 2 grids without any missing values, improving the quality of various applications such as emission reduction strategies and public health studies. Between 2005 and 2019, the 5,478 daily estimated NO 2 grids over the CONUS reveal significant reductions in NO 2 levels in fourteen major urban environments: Washington D.C. (-43%), New York (-45%), Los Angeles (-38%), Chicago (-25%), Boston (-43%), Houston (-34%), Dallas (-40%), Philadelphia (-41%), Phoenix (-38%), Detroit (-20%), Denver (-23%), Atlanta (-0.7%), Cincinnati (-38%), and Pittsburgh (-56%). Furthermore, the study shows that the denser urban regions that in-situ stations are installed in, the higher the difference between in-situ observations and regional-mean NO 2 levels.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Deep Learning Reconstruction of Daily Soil CO 2 Efflux Reveals Biogeochemical Insights and Reduces Annual Estimate Uncertainty Despite Limited Daily Predictability

Soil CO 2 efflux is commonly measured monthly or seasonally, leaving daily dynamics poorly resolved and contributing to global estimation uncertainty. We trained a single Long Short-Term Memory (LSTM) model to predict daily soil CO 2 efflux across 82 globally distributed sites in COSORE, with 0.2%–46.9% daily data coverage from 2003 to 2020. Despite using far fewer sites than are typically used to train a single deep learning model, with observations biased toward temperate mesic sites, the LSTM model performed well at approximately one-third of sites, reconstructed nearly 2 decades of daily efflux, and outperformed commonly used approaches for estimating daily efflux when applied to the same data set. Performance was weakest at pronounced peaks and troughs and at non-temperate sites with <1.5 years of observations and irregular data patterns. Nevertheless, annual efflux from reconstructed daily data had <40% error even at underperforming sites, substantially improving estimates derived from monthly and seasonal sampling (maximum errors of 95% and 136%, respectively). Temperature sensitivity (Q 10 ) estimated from reconstructed daily predictions closely matched estimates from daily observations, whereas Q 10 values derived from monthly or seasonal observations deviated substantially, suggesting that coarse temporal sampling may contribute to uncertainty in reported Q 10 values. Consistent daily reconstructions further enabled trend analyses for well-performing, predominantly temperate sites and showed increasing soil CO 2 efflux at most sites from 2003 to 2020, with more variable summer trends. Despite limitations, these results demonstrate the potential of LSTM models to reconstruct daily soil CO 2 efflux and reduce estimation uncertainties from sparse observations.

Smykalov, Valerie [Pennsylvania State University, ↗

DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗

DOE OSTI2022

Extension of the flux fit method for estimating power deposition profiles

The flux fit method is used to self-consistently estimate the power deposition profile and heat transport profiles from temperature measurements originating from perturbative experiments with a modulated source. Furthermore, this letter improves on this method by addressing the limitations and assumptions. The most crucial improvement is the additional freedom in the source deposition profile. Allowing for a variable central deposition location and height and including a skewness parameter produces deposition profiles more consistent with the measurement data, but still wider than equilibrium ray tracing in two different DIII-D discharges. Moreover, we show that the quality of the estimated deposition profile is key to the accuracy of diffusivity and connectivity estimates, but inversely, the estimated transport parameters hardly affect the quality of the power deposition estimate. Using this method, we show that the power deposition profile estimate is broadened with respect to ray-tracing by about 1.7-1.8 times in two DIII-D discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Fast uncertainty estimates in deep learning interatomic potentials

Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and material properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncertainty quantification efforts have primarily leveraged the standard deviation of predictions across an ensemble of independently trained neural networks. This incurs a large computational overhead in both training and prediction, resulting in order-of-magnitude more expensive predictions. Here, we propose a method to estimate the predictive uncertainty based on a single neural network without the need for an ensemble. This allows us to obtain uncertainty estimates with virtually no additional computational overhead over standard training and inference. We demonstrate that the quality of the uncertainty estimates matches those obtained from deep ensembles. We further examine the uncertainty estimates of our methods and deep ensembles across the configuration space of our test system and compare the uncertainties to the potential energy surface. Finally, we study the efficacy of the method in an active learning setting and find the results to match an ensemble-based strategy at order-of-magnitude reduced computational cost.

Chemistry↗

DOE OSTI2024

A three-point velocity estimation method for two-dimensional coarse-grained imaging data

Time delay and velocity estimation methods have been widely studied subjects in the context of signal processing, with applications in many different fields of physics. The velocity of waves or coherent fluctuation structures is commonly estimated as the distance between two measurement points divided by the time lag that maximizes the cross correlation function between the measured signals, but this is demonstrated to result in erroneous estimates for two spatial dimensions. We present an improved method to accurately estimate both components of the velocity vector, relying on three non-aligned measurement points. We introduce a stochastic process describing the fluctuations as a superposition of uncorrelated pulses moving in two dimensions. Using this model, we show that the three-point velocity estimation method, using time delays calculated through cross correlations, yields the exact velocity components when all pulses have the same velocity. The two- and three-point methods are tested on synthetic data generated from realizations of such processes for which the underlying velocity components are known. The results reveal the superiority of the three-point technique. Finally, we demonstrate the applicability of the velocity estimation on gas puff imaging data of strongly intermittent plasma fluctuations due to the radial motion of coherent, blob-like structures at the boundary of the Alcator C-Mod tokamak.

Materials Science↗

DOE OSTI2013

Active galactic nucleus black hole mass estimates in the era of time domain astronomy

We investigate the dependence of the normalization of the high-frequency part of the X-ray and optical power spectral densities (PSDs) on black hole mass for a sample of 39 active galactic nuclei (AGNs) with black hole masses estimated from reverberation mapping or dynamical modeling. We obtained new Swift observations of PG 1426+015, which has the largest estimated black hole mass of the AGNs in our sample. We develop a novel statistical method to estimate the PSD from a light curve of photon counts with arbitrary sampling, eliminating the need to bin a light curve to achieve Gaussian statistics, and we use this technique to estimate the X-ray variability parameters for the faint AGNs in our sample. We find that the normalization of the high-frequency X-ray PSD is inversely proportional to black hole mass. We discuss how to use this scaling relationship to obtain black hole mass estimates from the short timescale X-ray variability amplitude with precision ∼0.38 dex. The amplitude of optical variability on timescales of days is also anticorrelated with black hole mass, but with larger scatter. Instead, the optical variability amplitude exhibits the strongest anticorrelation with luminosity. We conclude with a discussion of the implications of our results for estimating black hole mass from the amplitude of AGN variability.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Single-epoch black hole mass estimators for broad-line active galactic nuclei: recalibrating Hβ with a new approach

Based on an updated Hβ reverberation mapping (RM) sample of 44 nearby active galactic nuclei (AGNs), we propose a novel approach for black hole (BH) mass estimation using two filtered luminosities computed from single-epoch (SE) AGN spectra around the Hβ region. We found that the two optimal-filter luminosities extract virial information (size and virial velocity of the broad-line region, BLR) from the spectra, justifying their usage in this empirical BH mass estimator. The major advantages of this new recipe over traditional SE BH mass estimators utilizing continuum luminosity and broad-line width are (1) it has a smaller intrinsic scatter of 0.28 dex calibrated against RM masses, (2) it is extremely simple to use in practice, without any need to decompose the spectrum, and (3) it produces unambiguous and highly repeatable results even with low signal-to-noise spectra. The combination of the two luminosities can also cancel out, to some extent, systematic luminosity errors potentially introduced by uncertainties in distance or flux calibration. In addition, we recalibrated the traditional SE mass estimators using broad Hβ FWHM and monochromatic continuum luminosity at 5100 Å (L {sub 5100}). We found that using the best-fit slopes on FWHM and L {sub 5100} (derived from fitting the BLR radius-luminosity relation and the correlation between rms line dispersion and SE FWHM, respectively) rather than simple assumptions (e.g., 0.5 for L {sub 5100} and 2 for FWHM) leads to more precise SE mass estimates, improving the intrinsic scatter from 0.41 dex to 0.36 dex with respect to the RM masses. We compared different estimators and discussed their applications to the Sloan Digital Sky Survey quasar sample. Due to the limitations of the current RM sample, application of any SE recipe calibrated against RM masses to distant quasars should be treated with caution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

DARK MATTER MASS FRACTION IN LENS GALAXIES: NEW ESTIMATES FROM MICROLENSING

We present a joint estimate of the stellar/dark matter mass fraction in lens galaxies and the average size of the accretion disk of lensed quasars based on microlensing measurements of 27 quasar image pairs seen through 19 lens galaxies. The Bayesian estimate for the fraction of the surface mass density in the form of stars is α = 0.21 ± 0.14 near the Einstein radius of the lenses (∼1-2 effective radii). The estimate for the average accretion disk size is R{sub 1/2}=7.9{sub −2.6}{sup +3.8}√(M/0.3 M{sub ⊙}) light days. The fraction of mass in stars at these radii is significantly larger than previous estimates from microlensing studies assuming quasars were point-like. The corresponding local dark matter fraction of 79% is in good agreement with other estimates based on strong lensing or kinematics. The size of the accretion disk inferred in the present study is slightly larger than previous estimates.

79 ASTRONOMY AND ASTROPHYSICS↗